analysis/hospitalisation_prediction_urti_lr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predcition of Hospital Admission Related to Infections\n",
    "\n",
    "In this notebook, we develop a Cox proportional hazard regression model to predict the risk of hospital admission for common infections including urinary tract infection (urti), upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), sinusitis, otitis media or middle ear infection (ot media), and ear infection or otitis externa (ot externa)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "import gzip\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "from patsy import dmatrices\n",
    "from lifelines import CoxPHFitter\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "from sklearn.impute import KNNImputer\n",
    "import pickle\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from lifelines.utils import k_fold_cross_validation\n",
    "from lifelines.utils import concordance_index\n",
    "from lifelines.calibration import survival_probability_calibration\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import io\n",
    "import sys\n",
    "from contextlib import redirect_stdout\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"..\")\n",
    "current_working_dir = os.getcwd()\n",
    "# print(current_working_dir)\n",
    "\n",
    "# !python --version\n",
    "# import sklearn\n",
    "# print(sklearn.__version__)\n",
    "# import lifelines\n",
    "# print(lifelines.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Warning Shown\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.warn('DelftStack')\n",
    "warnings.warn('Do not show this message')\n",
    "print(\"No Warning Shown\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0- Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def makeFilePath(pathFromWorkspace, fileName):\n",
    "#     workingdir = \"/workspace/\" if (os.name != \"nt\") else \"\"\n",
    "#     filepath = workingdir+pathFromWorkspace+fileName\n",
    "\n",
    "#     return filepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_histplot(title, df, ax):\n",
    "    ax.hist(df, color = 'purple', edgecolor = 'black', bins=20)\n",
    "    ax.set_title(title)\n",
    "    ax.set_ylabel('Frequency')\n",
    "    ax.xaxis.set_tick_params(which='both', labelbottom=True, labeltop=False, rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def create_lineplot(data, var, title, legend_title, ax1):\n",
    "def create_lineplot(data, var, title, ax1):\n",
    "    a = data.groupby(['date', var]).size().reset_index()\n",
    "    a.columns = ['date', var, 'Freq']\n",
    "    a.Freq = a.Freq /1000\n",
    "    sns.lineplot(x='date', y='Freq', \n",
    "            hue=var, \n",
    "            data=a,\n",
    "            ax=ax1)\n",
    "    ax1.set_title(title)\n",
    "    ax1.set_ylabel('Count in 1000 patients')\n",
    "    ax1.xaxis.set_tick_params(which='both', labelbottom=True, labeltop=False, rotation=90)\n",
    "#     ax1.legend(title=legend_title)\n",
    "    ax1.grid()\n",
    "    ax1.axvline(x=\"2020-01\", color='black', ls='--', lw=1.5)\n",
    "    ax1.axvline(x=\"2020-04\", color='black', ls='--', lw=1.5)\n",
    "    ax1.axvline(x=\"2021-04\", color='black', ls='--', lw=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to transform cph model summary and save it as html\n",
    "def GetPrintSummary(model):\n",
    "    output = \"\"\n",
    "    with io.StringIO() as buf, redirect_stdout(buf):\n",
    "        model.print_summary(style=\"html\")\n",
    "        output = buf.getvalue()\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ConvertColumns(InfectionKey, NumSets):\n",
    "    for i in range(1, NumSets+1):        \n",
    "        arrayKey1 = 'admitted_'+InfectionKey+'_date_'+str(i)\n",
    "        #arrayKey2 = InfectionKey+'_date_'+str(i)\n",
    "        data[arrayKey1] = data[arrayKey1].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1- Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 79)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(f'../output/hospitalisation_data/input_hospitalisation.csv.gz')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deregistered_date</th>\n",
       "      <th>died_date</th>\n",
       "      <th>bmi_date_measured</th>\n",
       "      <th>smoking_status_date</th>\n",
       "      <th>most_recent_unclear_smoking_cat_date</th>\n",
       "      <th>flu_vaccine_med</th>\n",
       "      <th>flu_vaccine_clinical</th>\n",
       "      <th>admitted_date</th>\n",
       "      <th>urti_date_1</th>\n",
       "      <th>urti_date_2</th>\n",
       "      <th>...</th>\n",
       "      <th>hemiplegia_comor</th>\n",
       "      <th>hiv_comor</th>\n",
       "      <th>metastatic_cancer_comor</th>\n",
       "      <th>mild_liver_comor</th>\n",
       "      <th>mod_severe_liver_comor</th>\n",
       "      <th>mod_severe_renal_comor</th>\n",
       "      <th>mi_comor</th>\n",
       "      <th>peptic_ulcer_comor</th>\n",
       "      <th>peripheral_vascular_comor</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>60000</td>\n",
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       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>2.000000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1210</td>\n",
       "      <td>695</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1210</td>\n",
       "      <td>1210</td>\n",
       "      <td>1210</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2020-07-05</td>\n",
       "      <td>2022-04-15</td>\n",
       "      <td>2020-07</td>\n",
       "      <td>2021-06</td>\n",
       "      <td>2020-06</td>\n",
       "      <td>2018-03</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>2021-03-18</td>\n",
       "      <td>2021-02-17</td>\n",
       "      <td>2020-07-04</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>19</td>\n",
       "      <td>178</td>\n",
       "      <td>4012</td>\n",
       "      <td>556</td>\n",
       "      <td>559</td>\n",
       "      <td>1761</td>\n",
       "      <td>1724</td>\n",
       "      <td>72</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>9.994194e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.300001</td>\n",
       "      <td>0.300001</td>\n",
       "      <td>0.300001</td>\n",
       "      <td>0.300001</td>\n",
       "      <td>0.300001</td>\n",
       "      <td>0.300001</td>\n",
       "      <td>0.300001</td>\n",
       "      <td>0.300001</td>\n",
       "      <td>0.300001</td>\n",
       "      <td>5.767260e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000372e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.982130e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500024e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.999991e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       deregistered_date   died_date bmi_date_measured smoking_status_date  \\\n",
       "count              10000       20000            150000               20000   \n",
       "unique              1210         695                40                  40   \n",
       "top           2020-07-05  2022-04-15           2020-07             2021-06   \n",
       "freq                  19         178              4012                 556   \n",
       "mean                 NaN         NaN               NaN                 NaN   \n",
       "std                  NaN         NaN               NaN                 NaN   \n",
       "min                  NaN         NaN               NaN                 NaN   \n",
       "25%                  NaN         NaN               NaN                 NaN   \n",
       "50%                  NaN         NaN               NaN                 NaN   \n",
       "75%                  NaN         NaN               NaN                 NaN   \n",
       "max                  NaN         NaN               NaN                 NaN   \n",
       "\n",
       "       most_recent_unclear_smoking_cat_date flu_vaccine_med  \\\n",
       "count                                 20000           20000   \n",
       "unique                                   40              13   \n",
       "top                                 2020-06         2018-03   \n",
       "freq                                    559            1761   \n",
       "mean                                    NaN             NaN   \n",
       "std                                     NaN             NaN   \n",
       "min                                     NaN             NaN   \n",
       "25%                                     NaN             NaN   \n",
       "50%                                     NaN             NaN   \n",
       "75%                                     NaN             NaN   \n",
       "max                                     NaN             NaN   \n",
       "\n",
       "       flu_vaccine_clinical admitted_date urti_date_1 urti_date_2  ...  \\\n",
       "count                 20000         60000       20000       20000  ...   \n",
       "unique                   13          1210        1210        1210  ...   \n",
       "top                 2018-12    2021-03-18  2021-02-17  2020-07-04  ...   \n",
       "freq                   1724            72          29          31  ...   \n",
       "mean                    NaN           NaN         NaN         NaN  ...   \n",
       "std                     NaN           NaN         NaN         NaN  ...   \n",
       "min                     NaN           NaN         NaN         NaN  ...   \n",
       "25%                     NaN           NaN         NaN         NaN  ...   \n",
       "50%                     NaN           NaN         NaN         NaN  ...   \n",
       "75%                     NaN           NaN         NaN         NaN  ...   \n",
       "max                     NaN           NaN         NaN         NaN  ...   \n",
       "\n",
       "       hemiplegia_comor      hiv_comor metastatic_cancer_comor  \\\n",
       "count     200000.000000  200000.000000           200000.000000   \n",
       "unique              NaN            NaN                     NaN   \n",
       "top                 NaN            NaN                     NaN   \n",
       "freq                NaN            NaN                     NaN   \n",
       "mean           0.100000       0.100000                0.100000   \n",
       "std            0.300001       0.300001                0.300001   \n",
       "min            0.000000       0.000000                0.000000   \n",
       "25%            0.000000       0.000000                0.000000   \n",
       "50%            0.000000       0.000000                0.000000   \n",
       "75%            0.000000       0.000000                0.000000   \n",
       "max            1.000000       1.000000                1.000000   \n",
       "\n",
       "       mild_liver_comor mod_severe_liver_comor mod_severe_renal_comor  \\\n",
       "count     200000.000000          200000.000000          200000.000000   \n",
       "unique              NaN                    NaN                    NaN   \n",
       "top                 NaN                    NaN                    NaN   \n",
       "freq                NaN                    NaN                    NaN   \n",
       "mean           0.100000               0.100000               0.100000   \n",
       "std            0.300001               0.300001               0.300001   \n",
       "min            0.000000               0.000000               0.000000   \n",
       "25%            0.000000               0.000000               0.000000   \n",
       "50%            0.000000               0.000000               0.000000   \n",
       "75%            0.000000               0.000000               0.000000   \n",
       "max            1.000000               1.000000               1.000000   \n",
       "\n",
       "             mi_comor peptic_ulcer_comor peripheral_vascular_comor  \\\n",
       "count   200000.000000      200000.000000             200000.000000   \n",
       "unique            NaN                NaN                       NaN   \n",
       "top               NaN                NaN                       NaN   \n",
       "freq              NaN                NaN                       NaN   \n",
       "mean         0.100000           0.100000                  0.100000   \n",
       "std          0.300001           0.300001                  0.300001   \n",
       "min          0.000000           0.000000                  0.000000   \n",
       "25%          0.000000           0.000000                  0.000000   \n",
       "50%          0.000000           0.000000                  0.000000   \n",
       "75%          0.000000           0.000000                  0.000000   \n",
       "max          1.000000           1.000000                  1.000000   \n",
       "\n",
       "          patient_id  \n",
       "count   2.000000e+05  \n",
       "unique           NaN  \n",
       "top              NaN  \n",
       "freq             NaN  \n",
       "mean    9.994194e+05  \n",
       "std     5.767260e+05  \n",
       "min     1.000000e+00  \n",
       "25%     5.000372e+05  \n",
       "50%     9.982130e+05  \n",
       "75%     1.500024e+06  \n",
       "max     1.999991e+06  \n",
       "\n",
       "[11 rows x 79 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-2- Additional variables\n",
    "#### 1-2-1- Calculating CHarlson Comorbidity Index\n",
    "Based on https://www.nature.com/articles/4500889.pdf?origin=ppub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['CCI'] = np.nan\n",
    "col0 = data.columns.get_loc('CCI')\n",
    "\n",
    "col1 = data.columns.get_loc('cancer_comor')\n",
    "col2 = data.columns.get_loc('cardiovascular_comor')\n",
    "col3 = data.columns.get_loc('chronic_obstructive_pulmonary_comor') ##\n",
    "col4 = data.columns.get_loc('heart_failure_comor') ##\n",
    "col5 = data.columns.get_loc('connective_tissue_comor')\n",
    "col6 = data.columns.get_loc('dementia_comor')\n",
    "col7 = data.columns.get_loc('diabetes_comor')\n",
    "col8 = data.columns.get_loc('diabetes_complications_comor')\n",
    "col9 = data.columns.get_loc('hemiplegia_comor')\n",
    "col10 = data.columns.get_loc('hiv_comor')\n",
    "col11 = data.columns.get_loc('metastatic_cancer_comor')\n",
    "col12 = data.columns.get_loc('mild_liver_comor')\n",
    "col13 = data.columns.get_loc('mod_severe_liver_comor')\n",
    "col14 = data.columns.get_loc('mod_severe_renal_comor')\n",
    "col15 = data.columns.get_loc('mi_comor')\n",
    "col16 = data.columns.get_loc('peptic_ulcer_comor')\n",
    "col17 = data.columns.get_loc('peripheral_vascular_comor')\n",
    "\n",
    "col18 = data.columns.get_loc('age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0     26364\n",
       "3.0     25753\n",
       "4.0     23906\n",
       "1.0     23588\n",
       "0.0     18937\n",
       "5.0     17597\n",
       "6.0     15866\n",
       "7.0     12473\n",
       "8.0      9665\n",
       "9.0      7628\n",
       "10.0     6092\n",
       "11.0     4177\n",
       "12.0     2985\n",
       "13.0     1933\n",
       "14.0     1197\n",
       "15.0      726\n",
       "16.0      513\n",
       "17.0      270\n",
       "18.0      157\n",
       "19.0       95\n",
       "20.0       40\n",
       "21.0       27\n",
       "22.0        5\n",
       "23.0        4\n",
       "24.0        2\n",
       "Name: CCI, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for idx, x in enumerate(data['CCI']):\n",
    "    n=0\n",
    "    #comorbidity component\n",
    "    if data.iat[idx, col15] == 1: # mi_comor\n",
    "        n=n+1\n",
    "    if data.iat[idx, col2] == 1: # cardiovascular_comor\n",
    "        n=n+1 \n",
    "    if data.iat[idx, col17] == 1: # peripheral_vascular_comor\n",
    "        n=n+1 \n",
    "    if data.iat[idx, col3] == 1: # chronic_obstructive_pulmonary_comor\n",
    "        n=n+1\n",
    "    # if data.iat[idx, col4] == 1: # heart_failure_comor############################\n",
    "    #     n=n+1\n",
    "    if data.iat[idx, col7] == 1: # diabetes_comor\n",
    "        n=n+1\n",
    "    if data.iat[idx, col6] == 1: # dementia_comor\n",
    "        n=n+1\n",
    "    if data.iat[idx, col16] == 1: # peptic_ulcer_comor\n",
    "        n=n+1\n",
    "    if data.iat[idx, col5] == 1: # connective_tissue_comor\n",
    "        n=n+1\n",
    "    if data.iat[idx, col12] == 1: # mild_liver_comor\n",
    "        n=n+1\n",
    "    \n",
    "    if data.iat[idx, col9] == 1: # hemiplegia_comor\n",
    "        n=n+2\n",
    "    if data.iat[idx, col14] == 1: # mod_severe_renal_comor\n",
    "        n=n+2\n",
    "    if data.iat[idx, col8] == 1: # diabetes_complications_comor\n",
    "        n=n+2\n",
    "    if data.iat[idx, col1] == 1: # cancer_comor\n",
    "        n=n+2\n",
    "    #if data.iat[idx, col5] == 1: # Leukaemia#########################################\n",
    "    #    n=n+2\n",
    "    #if data.iat[idx, col12] == 1: # Lymphoma#########################################\n",
    "    #    n=n+2\n",
    "\n",
    "    if data.iat[idx, col13] == 1: # mod_severe_liver_comor\n",
    "        n=n+3\n",
    "\n",
    "    if data.iat[idx, col11] == 1: # metastatic_cancer_comor\n",
    "        n=n+6\n",
    "    if data.iat[idx, col10] == 1: # hiv_comor\n",
    "        n=n+6\n",
    "\n",
    "    #age component\n",
    "    if data.iat[idx, col18] <= 40:\n",
    "        n=n+0\n",
    "    if data.iat[idx, col18] > 40 and data.iat[idx, col18] <= 50:\n",
    "        n=n+1\n",
    "    if data.iat[idx, col18] > 50 and data.iat[idx, col18] <= 60:\n",
    "        n=n+2\n",
    "    if data.iat[idx, col18] > 60 and data.iat[idx, col18] <= 70:\n",
    "        n=n+3\n",
    "    if data.iat[idx, col18] > 70:# and data.iat[idx, col18] <= 80:\n",
    "        n=n+4\n",
    "    \n",
    "    data.iat[idx, col0]=n\n",
    "\n",
    "data.CCI.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1-2-2- Calculating all meds except abs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    132877\n",
       "2     22597\n",
       "3     18600\n",
       "1     17481\n",
       "4      7275\n",
       "5      1105\n",
       "6        65\n",
       "Name: meds_nonabs, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generate meds_noabs in the one month before\n",
    "data['meds_nonabs'] = data['all_meds'] - data['antibacterial_brit']\n",
    "\n",
    "# assign zero for any row with negative values which could be the case because of using dummy data and/or the inconsistency of the two codelists of all_meds and antibacterial_brit\n",
    "data['meds_nonabs'][data['meds_nonabs'] < 0] = 0\n",
    "\n",
    "data['meds_nonabs'].value_counts()\n",
    "# data[['all_meds_one_month', 'antibacterial_brit_one_month', 'meds_nonabs']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-3- Handling variables with missing values\n",
    "#### 1-3-1- Drop variables with many missing values\n",
    "We drop colmns with more than 50 percent missing values as they may not be usable in the prediction model, nor they can be imputed properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 82)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping any variable with more than 50 percent missing values\n",
    "perc = 50.0\n",
    "data[['age', 'sex', 'CCI', 'bmi', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs', 'region']] = data[['age', 'sex', 'CCI', 'bmi', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs', 'region']].loc[:, data[['age', 'sex', 'CCI', 'bmi', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs', 'region']].isnull().mean() < perc]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WRERERHaImaUD9wIjgOXAx2b2srvPLqvj7pfG1L8Y2DtmF/nuPrS+4lGPrYiIiEhUlZYmttRuP2CBuy9y9yJgCnB8DfXHEdyevkEosRURERGJqgSHIpjZBDP7JGaZUOkI3YFlMc+Xh+uqMLPeQF/grZjVmeF+PzSz0Yk2V0MRRERERKIqwYvH3H0SMKl+guEkYJpXnKqht7uvMLN+wFtm9qW7L9zRA6jHVkRERER21AqgZ8zzHuG6eE6i0jAEd18R/r8ImEHF8bfbTYmtiIiISFQ1/KwIHwO7mllfM2tBkLxWmd3AzHYD2gEfxKxrZ2YZ4eOOwIHA7Mrbbg8NRRARERGJqgaex9bdi83sIuBfQDow2d1nmdnvgU/cvSzJPQmY4u4es/lg4EEzKyXobL09djaFHaHEVkRERCSqknCDBnd/DXit0rrrKz2/Mc52/wX2rM9YlNiKpNjMoZelOoQGs9cXd6Y6hAYT5fNm5rVXEpHGoY43WYgKjbEVERERkUhQj62IiIhIVCVhKMLORImtiIiISFQ1saEISmxFREREoqqJ9dhqjK2IiIiIRIJ6bEVERESiSkMRRERERCQSmthQBCW2IiIiIlHVxBJbjbEVERERkUhQj62IiIhIVHnTupOgElsRERGRqGpiQxGU2IqIiIhElRJbEREREYmEJjbdly4eExEREZFIUI+tiIiISFRpKIKIiIiIRIJmRRARERGRSGhiPbYaYysiIiIikaDEVqQSM1tiZvlmtsXM1pvZq2bWMyx71MzczI6vtM1d4frx4fPxZvZeMuJNb5tD/4d+y97zp7Dnh5NoP/rgaut2v+Z0hn75OEO/fJzu15xeoSxrSF8GvzaRvb/+O4Nfm0jWkL4NHXqtnp72MmPP+hV7Dz+Wa2+eWGPdx6e8wCHHnsz+I07kulvvpKioqLxsxao1nHnRVQw7bDTHjjuHDz7+vKFDr1WUz5vaFlDb1LadQmlpYksjo8RWJL5j3T0H6AqsAf4SUzYfKP9tZ2bNgLHAwqRGGOp18wS8qJiZQ8ez+OK76HXruWQO7FmlXsdTRtLuyP2ZNfJSZo34NW2P2JdOpx4JgDVvxoDJV7Pu+Rl8sfsprJ32FgMmX401T+1opU4dO3Du+JM44eiRNdZ7/6NPeejJqTx8z2288dxjLF+5mnsffrK8/MobbmfwwP689/rf+dWEM7jsultYt35DA0dfsyifN7VNbVPbdiJemtjSyCixFamBuxcA04AhMav/ARxkZu3C50cB/wesTnJ4pGVl0G7UAay442lK8wrY8vEcNr75MR1+NrxK3Y5jDmP1pJfYumotW1evY82kl+gw9jAAWh2wB9YsnTUP/QMvKubbya+CGa0O3DPJLapoxPADOfzgH9O2Tesa6730+nROPOZIBvTrTZvWrThv/DhefG06AEu+Wc7s+Qu48OxTyczIYMShB7Frvz68OeP9ZDQhriifN7UtoLapbTsLL/WElsZGia1IDcwsG/gF8GHM6gLgJeCk8PnpwONJDg2AjH7d8JJSChevLF+XN3sxWQN7VambObAn+bMXx62XNbAn+XOWVKifP2dJ3P3sjBYsXsqgAdu+Ehw0oB9r161nw8ZNLFi8lB7dutKyZXaF8oWLl6YiVCDa501tC6ht26htkkxKbEXie9HMNgAbgRHAHZXKHwdON7O2wCHAi8kMrkx6yyxKN+dVWFeyOY/0nKw4dTMp2ZQXt15ay6wKZTXtZ2eUl5dPq5yW5c9zwse5efnk5RfQKiapDcqzyc3LT2qMsaJ83tS2srpqW237SZYot61ONMZWRIDR7t4WyAQuAt4xsy5lhe7+HtAJuBZ4xd23K0syswlm9omZffJ87pIdDrIkN5+0VhWTtvScbEq2VA2nJLeA9Ji6sfVKc/MrlNW0n51RdnYWW3K3feDkho9bZmeRnZXJlryKH0a5uXm0zE7dh1GUz5vaVlZXbattP8kS5bbVicbYikgZdy9x9+eBEuCgSsVPApezA8MQ3H2Suw9z92Entuyzw/EVLlqJpaeR0bdr+bqsIX3In/9NlboF85eRNaRP3Hr585eRNbh3hfpZg3vH3c/OaEDf3sxbsKj8+bwFi+jQvh1t27RmQN/eLF+5ujzZDcoX079v73i7Sooonze1LaC2baO2pVipJ7Y0MkpsRWpggeOBdsCcSsV/Jhim8G7SAwuV5hey4fUP6Xb5ONKyMsgZthttR+7H2udmVKm7dtrbdD7nOJp3aU/zzu3oMuF41k59C4DNH3yFl5Syy9nHYC2a0Wn8qGD9+18mszlVFBeXUFhYRElJKSWlpRQWFlFcXFKl3nFHHc7zr7zBwsVL2bR5Cw8+OoXRo44AoE+vHuw2oB/3PfIUhYVFTH/nfeYvXMyI4Qcmuznlonze1LaA2qa27TSa2FAE8yZ2qzWR2pjZEqAzQS+tA0uB29z9KTN7FFju7tfF2e494CF3fzScz/aX7l65l7eKT3qMTuiHML1tDn3+dDGtD96L4vWbWXHbE6x78V1y9hvCrk/8js8HjSuv2+PaM+g4Lkj4vn9mOstveay8LGv3vvS54yKyBvYg/+vlLPnNX8mftbjK8bbHXl/cmdD29z78JPdPfqrCuvPPOoUTjx7Jcaeey8tPPkjXLrsA8NiU53n4yWcpLCxkxPCDuP6Ki2jRogUQzGN77S0T+XLWPLp27sS1l1/IAfvunVBsM4deltD2O/N5S5TaFlDb1LbaDFv+ou14q+om7y8XJPQZk33xfQ0eY31SYiuSYokmtjuzRBPbnVmiia2ISFIS23vOSyyxveSBRpXY7uSzCouIiIjIDmtiHZhKbEVERESiqhGOk02ELh4TERERkUhQYisiIiISVUmY7svMjjKzeWa2wMx+G6d8vJl9Z2ZfhMsvY8rOMLOvw+WMRJuroQgiIiIiUdXAN1kws3TgXoLpL5cDH5vZy+4+u1LVv7v7RZW2bQ/cAAwjmIXo03Db9Tsaj3psRURERKKq4Xts9wMWuPsidy8CpgDH1zG6I4E33X1dmMy+CRy1Q+0MKbEVERERkR3VHVgW83x5uK6yn5nZ/5nZNDPruZ3b1pkSWxEREZGI8tLShBYzm2Bmn8QsE3YgjH8Afdz9BwS9so/VUn+HaYytiIiISFTV8QKw6rj7JGBSDVVWAD1jnvcI18XuY23M04eAP8ZsO7zStjN2MFRAPbYiIiIi0eWliS21+xjY1cz6mlkL4CTg5dgKZtY15ulxwJzw8b+AkWbWzszaASPDdTtMPbYiIiIiUZVgj21t3L3YzC4iSEjTgcnuPsvMfg984u4vA78ys+OAYmAdMD7cdp2Z3USQHAP83t3XJRKPElsRERER2WHu/hrwWqV118c8vhq4upptJwOT6ysWJbYiIiIiUdXEbqmrxFZEREQkqhp4KMLORomtiIiISFQ18J3HdjaaFUFEREREIkE9tiIiIiJRpaEIIiIiIhIFrovHRCSZBp1YnOoQGszMoZelOoQGs9cXd6Y6hAbzxV6XpzqEBmMW3d4rd0t1CLIzamI9thpjKyIiIiKRoB5bERERkahqYj22SmxFREREoqqJTfelxFZEREQkqtRjKyIiIiJR4E0ssdXFYyIiIiISCeqxFREREYmqJtZjq8RWREREJKp0gwYRERERiYQm1mOrMbYiIiIiEgnqsRURERGJqibWY6vEVkRERCSi3JXYioiIiEgUNLEeW42xFREREZFIUI+tiIiISFQ1sR5bJbYiIiIiEdXUbqmrxFZEREQkqpTYioiIiEgkNK0bjymxFamJmZ0MXAbsBmwGvgBucff3zGwgcAtwKNAcWAo8CtwD9AQWA83dvbhBg8zOIXPcJTTbbW88dxOF/3iM4k/fqVKt+fDjaXHwsVhOa7wwn+LP/kPhS5PLb7eY1nc3Mk+cQFrnHpSuXUPhs/dTsmh2g4Zem/S2OfT500W0Pngoxes2seL2J1n34rtx63a/5nQ6jTsCgO+emc6KWx8vL8sa0pc+f7qIzF17UPD1cpb85q/kz16clDZU5+lpL/Pia9P5etFiRh0xnFuuu7zauo9PeYGHn3qWgoICRhx6ENf/5iJatGgBwIpVa7juljv5cvY8unbuxDWXXcAB++6drGbUqPM5x9L1ghNIy8pg3asfsPTqB/Ci+D8OrQ7ak963TKBF907kfj6fxb/+C0UrvgPAWjSj923n0f7oAyjNL2TV/S+yZtLLyWxKuSi/JyGa56xMlNsm22hWBJFqmNllwN3ArUBnoBdwH3C8mfUHPgKWAXu6extgDDAMaJXMODPHnA8lW9ly7akUPP4nMsdeQFqXXlXqFX/1Ebl3XMKWK8eSe9uFpHXvS/NDjgsKs3PInnA9Rf9+ji1XnUTRv58ja8L1kNUymU2potfNE/CiYmYOHc/ii++i163nkjmwZ5V6HU8ZSbsj92fWyEuZNeLXtD1iXzqdeiQA1rwZAyZfzbrnZ/DF7qewdtpbDJh8NdY8tX/Xd+rYgXPHn8QJR4+ssd77H33KQ09O5eF7buON5x5j+crV3Pvwk+XlV95wO4MH9ue91//OryacwWXX3cK69RsaOPratT5kKF0vPJG5v7iBmftPIKNXZ7pfPi5u3WbtWjHgb1ex4o5n+Hz308iduZD+D2xL9LtfdhKZfbsyc/8JzB1zPV3PH03r4alJ3qP8nozqOYNot602XuoJLY2NEluROMysDfB74EJ3f97dc919q7v/w92vAP4f8F93v8zdVwG4+zx3P9ndNyQt0BYZNNvrxxS++iQUFVCyaDbFX31Es30PrVLVv18N+bnhMwN30jp2BSC972BKN62n+Iv3wUsp/mQGvmUjzff6cdKaUllaVgbtRh3AijuepjSvgC0fz2Hjmx/T4WfDq9TtOOYwVk96ia2r1rJ19TrWTHqJDmMPA6DVAXtgzdJZ89A/8KJivp38KpjR6sA9k9yiikYMP5DDD/4xbdu0rrHeS69P58RjjmRAv960ad2K88aP48XXpgOw5JvlzJ6/gAvPPpXMjAxGHHoQu/brw5sz3k9GE2rUccyhfDdlOgXzl1GyMZeV90yl49iq70uAdqN+RMH8Zax/5b944VZWTpxC9uA+ZPbvDkCHMYey8u6plGzMpWDBcr57+s1q99WQov6ejOI5KxPlttWq1BNbGhkltiLxHQBkAi9UU34EMC154cSXtkt3KC3Bv1tZvq5kxWLSu/aOW7/ZDw8h549TaXX7M6R178vW9/+5rdCsUm0jrZr9JENGv254SSmFi7e1LW/2YrIGVu2NzhzYs8LXuLH1sgb2JH/Okgr18+csibufndGCxUsZNKBv+fNBA/qxdt16NmzcxILFS+nRrSstW2ZXKF+4eGkqQq0ga1BP8mcvKX+eP2sJzXdpR3q7ql9oZA3sRV5M3dL8QgqWriFrUE/S27SkRZf2FcrzZqfm/EX9PRnFc1Ymym2rVWmCSyOjMbYi8XUAvq9hfGwHYFUS44nLWmThBfkVV+bnQUZW3PrFn77Dlk/fwTp1o/m+h+Gb1wNQsnguaa3b02yfgyn+4n2aDTsE69gFWmQ0dBOqld4yi9LNeRXWlWzOIz2natvSW2ZSsikvbr20llkVymraz84oLy+fVjnbhoTkhI9z8/LJyy+gVUxSG5Rn8+13a5MaYzxp2VkUVzonEJ6r9Zsr1m2ZSfHaTRXWlWzOJS0ni/SWmRW2ByjZlJrzF/X3ZBTPWZkot00qUmIrEt9aoKOZNasmuV0LdN3RnZvZBGACwD2H7smZe+zYX/telI9lVvqFmpkNhfnxNyjb7ruVlK7+hoyxF1Dw8K2Qt5n8h24m4/izyBxzPsVzP6Nk/hf4htQlSCW5+aS1qpi0pedkU7KlattKcgtIj6kbW680N79CWU372RllZ2exJXfbh2hu+LhldhbZWZlsyauYIOXm5tEyO/kfsu1POJg+fzgPgM0fzaE0L5/0VtviKDuXJbkFVbYtzS2oUBeCc1S6Jb+8fnpONsWFG4PHrbJScv6i9p6M8jmLctu2V2McJ5sIDUUQie8DoBAYXU35dOBnO7pzd5/k7sPcfdiOJrUApd+ugLR0rFO38nXp3ftSsqoOX0WnpZWPsQUoWfAVeRMvY8vV4yh4YiJpu/SgZOn8HY4tUYWLVmLpaWT03RZj1pA+5M//pkrdgvnLyBrSJ269/PnLyBpccUhF1uDecfezMxrQtzfzFiwqfz5vwSI6tG9H2zatGdC3N8tXri5PdoPyxfTvm/whJOteeJfPBp7MZwNP5uvTbiJ/3jKyh2wbQpE9pA9bv11fpXcMIH/+NxXOX1pWBhl9upA/LxgPWbR6HdkVzm/flJy/qL0no3zOoty27dbEhiIosRWJw903AtcD95rZaDPLNrPmZvZTM/sjcAPwYzO7w8y6AJjZADN70szaJi3QokKKZ35AxqhToEUG6X0H02zP/Sn++O0qVZsfMBLLaQNAWpeetBgxhpL5M8vL03r0g7R0yMwiY/TZ+IbvKZn7WdKaUllpfiEbXv+QbpePIy0rg5xhu9F25H6sfW5Glbprp71N53OOo3mX9jTv3I4uE45n7dS3ANj8wVd4SSm7nH0M1qIZncaPCta//2Uym1NFcXEJhYVFlJSUUlJaSmFhEcXFJVXqHXfU4Tz/yhssXLyUTZu38OCjUxg9KphCqk+vHuw2oB/3PfIUhYVFTH/nfeYvXMyI4QcmuzlVfD9tBp1OOpzMXXuQ3jqbbpeM4fupVd+XAOtf/4isQb1oN+pHWEZzul06lvw5SyhYuAKAtdNm0PWSMaS3aUlm/+50OvmIavfVkKL+noziOSsT5bbVpqnNimDujS9okWQxs1OAS4HBBPPYfkowj+1/zWwQcDNwGMGwniXAI8Bf2I55bDf/6pjEfgizc8g8+RKaDao4j216v93JOv9GtlwxBoDMky8hfcgwLCML37KR4i/eC2ZTKN4alJ9xBc2GDAOgeM6nFE57EN+yMaHQ5j2f2GinYM7Qi2l98F4Ur9/MitueYN2L75Kz3xB2feJ3fD5o23Q9Pa49g47hnKHfPzOd5bc8Vl6WtXtf+txxEVkDe5BfNmforMTmDN3rizsT2v7eh5/k/slPVVh3/lmncOLRIznu1HN5+ckH6dplFwAem/I8Dz/5LIWFhYwYfhDXX1FxHttrb5nIl7OCeWyvvfzChOex/WKv6ufU3R6dJxwXzBua2YJ1r33A0t9umzd0j7fuYeVfnmPdC8EcsK1/8gN63XwOGd07seXzr1l86Z8pWh5n3tCCIlbd98IOzxtqltiP2878nnSvfAHo9tsZz1l92Rnbtu+KFxI/abVYd/whCb3p27/0Tq0xmtlRBHO4pwMPufvtlcovA34JFAPfAWe5+9KwrAQo+6vuG3c/LpF4ldiKpFjCie1OLNHEdmeWaGK7M6uvxHZnlGhiuzOrj8RWkisKia2ZpQPzgRHAcuBjYJy7z46pcyjwkbvnmdn5wHB3/0VYtsXdcxKJMZaGIoiIiIhElJcmttTBfsACd1/k7kXAFOD4CjG4v+3uZRcDfAj0qM82xlJiKyIiIhJVCV48ZmYTzOyTmGVCpSN0J7gLZ5nl4brqnA28HvM8M9zvh2Y2egdbWS663xOKiIiINHF17HWtfnv3ScCk+ojFzE4luPX8ITGre7v7CjPrB7xlZl+6+8IdPYZ6bEVERERkR60guGC6TI9wXQVmdgRwLXCcuxeWrXf3FeH/i4AZQEJXvyqxFREREYmqhp/H9mNgVzPra2YtgJOACtNEmNnewIMESe23MevbmVlG+LgjcCAwmwRoKIKIiIhIRCU6FKHW/bsXm9lFwL8Ipvua7O6zzOz3wCfu/jJwB5ADPGtmsG1ar8HAg2ZWStDZenvsbAo7QomtiIiISEQ1dGIL4O6vAa9VWnd9zOMjqtnuv8Ce9RmLhiKIiIiISCSox1ZEREQkopLRY7szUWIrIiIiElVN7I50SmxFREREIko9tiIiIiISCV7atHpsdfGYiIiIiESCemxFREREIkpDEUREREQkElwXj4mIiIhIFDS1HluNsRURERGRSFCPrYiIiEhENbVZEZTYioiIiESUe6ojSC4ltiIpNve55qkOocGYRfc36hd7XZ7qEBrM0JkTUx1Cg5k59LJUh9Bgovzz9nSzzFSH0CD2TcIxmlqPrcbYioiIiEgkqMdWREREJKKaWo+tElsRERGRiNIYWxERERGJBPXYioiIiEgkNLU7j+niMRERERGJBPXYioiIiERUU7ulrhJbERERkYgqbWJDEZTYioiIiESUxtiKiIiIiDRC6rEVERERiShN9yUiIiIikaAbNIiIiIhIJDS1HluNsRURERGRSFCPrYiIiEhEabovEREREYmEpjbdlxJbERERkYhqahePaYxtgszsJ2Y2L+b5EjM7IpUxxWNmvcxsi5ml16HucDNbnoy46ouZnWJmbySw/QNm9rv6jElERCTVSt0SWhqbJpvYmtkMM1tvZhnbuZ2b2YCy5+7+H3cfVP8RVjhmwommu3/j7jnuXlJfce1M3P0pdx9Zl7pmNt7M3qu0/XnuflPDRJccnc85lqGfT2afuU/RZ+JFWIvqv5BpddCe7PHOX9hnwRQGPft7WnTvVF5mLZrRZ+JF7DP3KYZ+PpnOE45LRvhxpbfNof9Dv2Xv+VPY88NJtB99cLV1u19zOkO/fJyhXz5O92tOr1CWNaQvg1+byN5f/53Br00ka0jfhg69zqJ23p6e9jJjz/oVew8/lmtvnlhj3cenvMAhx57M/iNO5Lpb76SoqKi8bMWqNZx50VUMO2w0x447hw8+/ryhQ6+TKL8no9w2gIPPHsWNHz/ArV9O5hd/PJf0Gn7Wyoz81YncuWQKux64R/m6Y685lavfvotbv3qEq/49kWEn/qQhw5bt1CQTWzPrA/wEcCB1n9pJYmYpG3KSjGOnsn07i9aHDKXrhScy9xc3MHP/CWT06kz3y8fFrdusXSsG/O0qVtzxDJ/vfhq5MxfS/4HLy8u7X3YSmX27MnP/Ccwdcz1dzx9N6+F7J6spFfS6eQJeVMzMoeNZfPFd9Lr1XDIH9qxSr+MpI2l35P7MGnkps0b8mrZH7EunU48EwJo3Y8Dkq1n3/Ay+2P0U1k57iwGTr8aap/5tE8Xz1qljB84dfxInHF3z35nvf/QpDz05lYfvuY03nnuM5StXc+/DT5aXX3nD7Qwe2J/3Xv87v5pwBpdddwvr1m9o4OhrF+X3ZJTbNujgH3D4ecdx/8k3c9OBF9OhV2eOunRMjdt06NWZvUb9iI1r1lVYX5RfwMO/vINr9zyLZy6/n9E3nEGffQY2ZPgJcbeElrows6PMbJ6ZLTCz38YpzzCzv4flH4V5WFnZ1eH6eWZ2ZKLtbZKJLXA68CHwKHBGbIGZPWpm95rZq2a2OTwB/cOyd8NqM8Ov9X9RTW/qvmY2O+wRfsTMMsPt25nZK2b2XVj2ipn1iDl2+7D+yrD8RTNrCbwOdAuPucXMuplZmpn91swWmtlaM5tqZu3D/fQJe5bPNrNvgLdi1jUL65xpZnPCNi4ys3Pr+uKF+/lVuN33ZnaHmaWFZePN7H0zu8vM1gI3hm/oP5nZN2a2JvzaPyus3zF8HTaY2Toz+0/Mvnqa2fPh67XWzP5awzEq9MJWF6OZDQYeAA4IX8sNMef95pjtzwl/0NaZ2ctm1q3Svs8zs6/DuO81MwvLBpjZO2a2MTzu3+v6uiai45hD+W7KdArmL6NkYy4r75lKx7GHxq3bbtSPKJi/jPWv/Bcv3MrKiVPIHtyHzP7dAegw5lBW3j2Vko25FCxYzndPv1ntvhpSWlYG7UYdwIo7nqY0r4AtH89h45sf0+Fnw6vU7TjmMFZPeomtq9aydfU61kx6iQ5jDwOg1QF7YM3SWfPQP/CiYr6d/CqY0erAPZPcoqqieN5GDD+Qww/+MW3btK6x3kuvT+fEY45kQL/etGndivPGj+PF16YDsOSb5cyev4ALzz6VzIwMRhx6ELv268ObM95PRhOqFeX3ZJTbBrDvzw7ho6kzWPP1cvI35fLmn59n358fUuM2P7vpTF65/WlKtlb8ovNfd03j24UrcXe++WIBi/43l9777NqQ4SfEPbGlNhYMcbwX+CkwBBhnZkMqVTsbWO/uA4C7gD+E2w4BTgJ2B44C7rM6DJmsSVNObJ8KlyPNrHOl8pOA/we0AxYAtwC4e9n3MnuFX+tXl7ScAhwJ9AcGAteF69OAR4DeQC8gH/hrzHZPANkEJ3gX4C53zyV4s6wMj5nj7iuBi4HRwCFAN2A9wRsr1iHA4DCWyr4FjgFaA2cCd5nZPtW0J54TgGHAPsDxwFkxZfsDi4DOBK/d7eHrMBQYAHQHrg/rXg4sBzqF9a8BPHxjvwIsBfqE20yp4Rh1itHd5wDnAR+Er2XbyhuZ2WHAbcBYoGsYw5RK1Y4B9gV+ENYre41vAt4geO/0AP5STWz1KmtQT/JnLyl/nj9rCc13aUd6u1ZV6w7sRV5M3dL8QgqWriFrUE/S27SkRZf2FcrzZi8ha2CvBow+vox+3fCSUgoXr4yJZXHcWDIH9iR/9uK49bIG9iR/zpIK9fPnpKZNlUXxvNXVgsVLGTRg21fUgwb0Y+269WzYuIkFi5fSo1tXWrbMrlC+cPHSVIRaLsrvySi3DaDLwB6snLPt/bNyzlJad2pLdtucuPX3GrU/xUXFzJnxRY37bZ7RnF579WfN1zvvZSlJGGO7H7DA3Re5exHB5+XxleocDzwWPp4GHB52CB0PTHH3QndfTJBz7ZdIe5tcYmtmBxEkllPd/VNgIXBypWovuPv/3L2YIPkdup2H+au7L3P3dQRJ1zgAd1/r7s+5e567bw7LDgnj6kqQwJ7n7uvdfau7v1PDMc4DrnX35e5eCNwI/Nwqfi1/o7vnunt+5Y3d/VV3X+iBdwiSse0ZKPQHd1/n7t8Ad5e1MbTS3f8Svn4FwATg0rD+ZuBWgj8eALYSJI+9wzb/x92d4I3dDbgibEOBu78X7xjx2leHGGtyCjDZ3T8LX9urCXp4+8TUud3dN4T7fptt75GtBO+vbnFibjBp2VkUb8orf16yOXic3jKzat2WmZTE1A3q55KWk1Vev2x7gJJNeaTnZDVE2DVKb5lF6ebKccaPJb1Sm2LrpbXMitPe1LSpsiiet7rKy8unVU7L8uc54ePcvHzy8gtoFZPUBuXZ5OZV96OeHFF+T0a5bQAtsjMpiGlffvg4I05cGS0zGXXFSbzw/x6rUlbZz2/9JSvnLGXuOzPrL9jGpzuwLOb58nBd3DphbrAR6FDHbbdL6geZJd8ZwBvu/n34/Olw3V0xdVbHPM4D4v9JV73Yk7SUIEHDzLLD4xxF0KMH0CrsnewJrHP39XU8Rm/gBTMrjVlXQtCDGS+OCszsp8ANBD2paQQ9xV/W8diV913exjhlncJ9fxp+Ww9gQNlXDXcQJOVvhOWT3P12gtdjafgDUNvxdyTGmnQDPit74u5bwiEP3YEl4erq3iNXEvTa/s/M1gMT3X1y5QOY2QSChJ+r2wzlhJZ96hhaoP0JB9PnD+cBsPmjOZTm5ZPeatsv6LRWQVJQkltQZdvS3IIKdQHSc7Ip3ZJfXj89J5viwo3B41ZZlGxJfkJRkptf3o7YOOPFUpJbQHpM3dh6pbn5Fcpq2k9Dawrnra6ys7PYkrst0cgNH7fMziI7K5MteRWTo9zcPFpmpzY5iuJ7skzU2rbP8Qcy5tZzAFj08VyK8goqJLGZ4ePCOHEd+euf8+kL/2H98u9qPMaxV59Cl4E9uW/czn3dcaLz2MZ+XoUmufukhHbagJpUj204rnMscIiZrTaz1cClwF5mtlc9Hip2tH0voOy7ncuBQcD+7t4aKBvaYARJWHszaxtnf/FGuSwDfurubWOWTHdfUct2WDATxHPAn4DO4dfxr4Vx1FV1bax83O8JhlzsHhNnG3fPAXD3ze5+ubv3I7iQ7zIzOzxsXy+r/sKwuszMV12MtW27kuAPBwDCcc4dgBXVblG2Y/fV7n6Ou3cDziUYLzQgTr1J7j7M3Ydtb1ILsO6Fd/ls4Ml8NvBkvj7tJvLnLSM75srj7CF92PrtekrWb66ybf78b8gasu2YaVkZZPTpQv68YJxn0ep1ZMeUZw3pS/78b7Y7xkQVLlqJpaeR0bdrTCx94sZSMH9ZhTbF1sufv4yswb0r1M8a3DslbWoK562uBvTtzbwFi8qfz1uwiA7t29G2TWsG9O3N8pWry5PdoHwx/fv2jrerpInie7JM1Nr22Uvvc/Xu47l69/H8bfztrJ6/nG4xcXUb3JtN320gb8OWKtvueuAeHDT+KG78+AFu/PgB2nbtwBn3/prDztt2rfmRl/6c3YYP5cHTbo2bHO9MEh2KEPt5FS6Vk9oVVPy87UHVz8vyOuHnehtgbR233S5NKrElGJNaQjC4eWi4DAb+QzDuti7WAP1qqXOhmfUIL+a6Figbi9uKIMnbEJbdULaBu68iuEjsPgsuMmtuZmWJ7xqgg5m1iTnGA8AtZtYbwMw6mVnlMS3VaQFkAN8BxWHvbZ2myopxRRhnT+CSmDZW4O6lwN8IxvDuEsba3cIrH83smPCCKyP4aqIEKAX+B6wCbjezlmaWaWYH1lOMa4AeZtaimu2eAc40s6HhHwG3Ah+5+5LaDmhmY2zbBYHrCZLo0ho2qRffT5tBp5MOJ3PXHqS3zqbbJWP4furbceuuf/0jsgb1ot2oH2EZzel26Vjy5yyhYGHwu2TttBl0vWQM6W1aktm/O51OPqLafTWk0vxCNrz+Id0uH0daVgY5w3aj7cj9WPvcjCp11057m87nHEfzLu1p3rkdXSYcz9qpbwGw+YOv8JJSdjn7GKxFMzqNHxWsf397vqBoGFE8b8XFJRQWFlFSUkpJaSmFhUUUF1edZfC4ow7n+VfeYOHipWzavIUHH53C6FHBFOB9evVgtwH9uO+RpygsLGL6O+8zf+FiRgzf3l8B9SvK78kotw3gk+ffZf9fHErnAd3JbJ3NiItP4ONp8Uf73X/yzdxx5BVMHHUVE0ddxcY163n2mr/x3uP/AuDwC45nn+MO5IFTb46bGO9sPMGlDj4GdjWzvuHn6knAy5XqvMy2i/V/DrwVDjt8GTgpvMi8L7Arwef/Dmtqie0ZwCPhnK6ryxaCC7hOqaF3MNaNwGPh1fBjq6nzNMGY1UUEY3jLrra/G8gi6MX8EPhnpe1OIxijOZfg4q5fA7j7XIJka1F43G7APQRviDfMbHO4v/3rED/hONdfAVMJkq+TqfomrM1LwKfAF8CrwMM11L2KYED4h2a2CZhO0HMNwZt4OrAF+AC4z93fDufbPZbgYrNvCMbd/KKeYnwLmAWsNrPvK2/k7tOB3xH0aq8iuAjwpMr1qrEv8JGZbSF4TS9x90W1bJOwTTM+Z9X9L7Lbszex1//+RuHyb1kx8Zny8j3euof2JwR/JxWv28TCCX+k+1WnsM+sJ2i590AWnr9tvtEVE5+hcOlq9vpoErs9dzOrH3iJTTNSM4fo0msfJC0zg71mPkbfey/nm2sepGD+MnL2G8Le87a177sn/8XG6Z+w+/R72P3ff2bjW5/y3ZPBh5BvLWbB2bfR4WeHsvfsp+j4i8NZcPZt+NbqRrkkTxTP24OPPcMPDzueh5+cyiv/eosfHnY8Dz72DKtWf8u+R5zAqtXfAnDQj4Zx1ik/58yLf8uIE0+nW5dduPDsU8v3c8fvr2bW3K/58VFjuPv+R7jz5mtp365t0ttTWZTfk1Fu29x3ZvL2Ay9zwTO/4/r3/8r6Fd/zz7ueLS+/8o072Of44A+nvA1b2PzdxvLFS0vJ25hLUV4hAEdfOY523TpyzYx7uG3Wo9w261EOv2B0Kpq1UwiHDF4E/AuYQ3AN0ywz+72ZlXVzP0zQQbcAuAz4bbjtLIJcZDZBTnShJzjfvnlTu9eaJMzMHNjV3RekOpbqNIYYy3zc/YTI/hAGpyGaonz/9aEza76xQmM2c+hlqQ5BdsDTzapeUBkFdy6Z0uC/SP7b9WcJ/SL+8arnGtUvu6Z48ZiIiIhIkxDlP8LjUWIrIiIiElENfpHHTkaJrWw3bwR//jWGGEVERBqab9eER41fU7t4TEREREQiSj22IiIiIhFVGt1reONSYisiIiISUaVNbCiCElsRERGRiNIYWxERERGRRkg9tiIiIiIRpem+RERERCQSmtpQBCW2IiIiIhHV1HpsNcZWRERERCJBPbYiIiIiEdXUemyV2IqIiIhElMbYioiIiEgklDatvFaJrYiIiEhUNbU7j+niMRERERGJBPXYioiIiESUpzqAJFNiKyKyA8yi+3Exc+hlqQ6hwez1xZ2pDqHBRPm8jdtamOoQGi3NiiAiIiIikVBqGmMrIiIiItLoqMdWREREJKKiO2gqPiW2IiIiIhGlMbYiIiIiEglN7QYNGmMrIiIiIpGgHlsRERGRiGpqdx5TYisiIiISUbp4TEREREQioamNsVViKyIiIhJRTW1WBF08JiIiIiKRoB5bERERkYjSGFsRERERiQSNsRURERGRSNAYWxERERGRBJlZezN708y+Dv9vF6fOUDP7wMxmmdn/mdkvYsoeNbPFZvZFuAyt7ZhKbEVEREQiqjTBJUG/Bf7t7rsC/w6fV5YHnO7uuwNHAXebWduY8ivcfWi4fFHbAZXYioiIiESUW2JLgo4HHgsfPwaMrhKf+3x3/zp8vBL4Fui0owdUYiuNjpkNN7Pl9bzPWWY2vD73KSIikmqJ9tia2QQz+yRmmbAdh+/s7qvCx6uBzjVVNrP9gBbAwpjVt4RDFO4ys4zaDqjENsLM7CQz+8jMcs3s2/DxBWZmYfmjZlZkZlvMbF04/mW3VMe9vcxsiZkdsR31HzWzm2PXufvu7j6j3oNLos7nHMvQzyezz9yn6DPxIqxF9deGtjpoT/Z45y/ss2AKg579PS26b/vj2Fo0o8/Ei9hn7lMM/XwynSccl4zw40pvm0P/h37L3vOnsOeHk2g/+uBq63a/5nSGfvk4Q798nO7XnF6hLGtIXwa/NpG9v/47g1+bSNaQvg0deq3UtkBjatvT015m7Fm/Yu/hx3LtzRNrrPv4lBc45NiT2X/EiVx3650UFRWVl61YtYYzL7qKYYeN5thx5/DBx583dOh1EtXzViaKvyOTwd0nufuwmGVSbLmZTTezr+Isx1faj1PD7GNm1hV4AjjT3ctGQVwN7AbsC7QHrqotXiW2EWVmlwP3AHcAXQj+SjoPOJDgr6Eyf3T3HKAHQff/o8mNVOpD60OG0vXCE5n7ixuYuf8EMnp1pvvl4+LWbdauFQP+dhUr7niGz3c/jdyZC+n/wOXl5d0vO4nMvl2Zuf8E5o65nq7nj6b18L2T1ZQKet08AS8qZubQ8Sy++C563XoumQN7VqnX8ZSRtDtyf2aNvJRZI35N2yP2pdOpRwJgzZsxYPLVrHt+Bl/sfgprp73FgMlXY81TOymM2tb42tapYwfOHX8SJxw9ssZ673/0KQ89OZWH77mNN557jOUrV3Pvw0+Wl195w+0MHtif917/O7+acAaXXXcL69ZvaODoaxfV8wbR/R1ZFw09xtbdj3D3PeIsLwFrwoS1LHH9Nt4+zKw18Cpwrbt/GLPvVR4oBB4B9qstHiW2EWRmbYDfAxe4+zR33xy+MT5391PCN0gF7p4HPA3sUc0+s8xsopktNbONZvaemWWFZceFX+VvMLMZZjY4ZrslZvab8GuEjWb2dzPLDMs6mtkr4XbrzOw/ZpYWlrmZDYjZT5Ve1nD9E0Av4B9hz/OV4fpnzWx1eMx3zWz3cP0E4BTgyrD+P2LiPCJ8nGFmd5vZynC5u+zrj7JhEGZ2edgLvsrMzoyJZ5SZzTazzWa2wsx+U/czt+M6jjmU76ZMp2D+Mko25rLynql0HHto3LrtRv2IgvnLWP/Kf/HCraycOIXswX3I7N8dgA5jDmXl3VMp2ZhLwYLlfPf0m9XuqyGlZWXQbtQBrLjjaUrzCtjy8Rw2vvkxHX42vErdjmMOY/Wkl9i6ai1bV69jzaSX6DD2MABaHbAH1iydNQ/9Ay8q5tvJr4IZrQ7cM8kt2kZtCzS2to0YfiCHH/xj2rZpXWO9l16fzonHHMmAfr1p07oV540fx4uvTQdgyTfLmT1/AReefSqZGRmMOPQgdu3XhzdnvJ+MJlQryuctiDl6vyPryhNcEvQycEb4+AzgpcoVzKwF8ALwuLtPq1RWlhQbwfjcr2o7oBLbaDoAyCDOG6g6ZpZDkPBV953Yn4AfAj8m+DrgSqDUzAYCzwC/Jhjs/RpBkhnbKzyW4ErHvsAPgPHh+suB5eF2nYFr2M6fI3c/DfgGONbdc9z9j2HR68CuwC7AZ8BTYf1J4eM/hvWPjbPba4EfAUOBvQj+QrwuprwL0AboDpwN3BszhcnDwLnu3orgj4S3tqc9OyprUE/yZy8pf54/awnNd2lHertWVesO7EVeTN3S/EIKlq4ha1BP0tu0pEWX9hXK82YvIWtgrwaMPr6Mft3wklIKF6+MiWVx3FgyB/Ykf/biuPWyBvYkf86SCvXz56SmTWXUtkBja1tdLVi8lEEDtn39PmhAP9auW8+GjZtYsHgpPbp1pWXL7ArlCxcvTUWo5aJ+3qL4O7KuSi2xJUG3AyPM7GvgiPA5ZjbMzB4K64wFDgbGx5nW6ykz+xL4EugIVOngqkyJbTR1BL539+KyFWb237BnNN/MYgdO/cbMNgALgBy2JZ3EbJsGnAVc4u4r3L3E3f8b9vz+AnjV3d90960ECXAWQQJc5s/uvtLd1wH/IEgYAbYCXYHe7r7V3f8TjsFJmLtPDnuqC4Ebgb3Cnuy6OAX4vbt/6+7fAf8POC2mfGtYvtXdXwO2AINiyoaYWWt3X+/un9VHe2qTlp1F8aa88uclm4PH6S0zq9ZtmUlJTN2gfi5pOVnl9cu2ByjZlEd6TlZDhF2j9JZZlG6uHGf8WNIrtSm2XlrLrDjtTU2byqhtZXUbV9vqKi8vn1Y5Lcuf54SPc/PyycsvoFVMUhuUZ5Obl5/UGCuL+nmL4u/IukrldF/uvtbdD3f3XcMhC+vC9Z+4+y/Dx0+6e/OYKb3Kp/Vy98Pcfc9waMOp7r6ltmMqsY2mtUBHMysf1OTuP3b3tmFZ7Hn/k7u3dfcu7n6cuy+kqo5AJhWvUizTDSjvaggHfC8j6M0sszrmcR5BAg3B+N8FwBtmtsjM4s1vt93MLN3MbjezhWa2CVgS0466qNCm8HG3mOdrY/9ooGKbfgaMApaa2TtmdkA1MZZfZfpC7pJ4VWrU/oSD2Wf+0+wz/2l2feJ3lOblk95q2y/WtFbBB2dJbkGVbUtzCyrUBUjPyaZ0S355/fScbR+86a2yKNmS/A/dktz88naUx5KTHTeWktwC0mPqxtYrzc2vUFbTfpJFbSur27jaVlfZ2Vlsyd2W+OSGj1tmZ5GdlcmWvIpJU25uHi2zU5sYRe28NYXfkRKfEtto+gAoJJg/rj58DxQA/eOUrQR6lz0Jx8H0BFbUttOwR/Vyd+8HHAdcZmaHh8V5QOxvxy417arS85MJ2n4EwZCBPmXhVVO/sgptIhjDu7KauhUDcf/Y3Y8nGALxIjC1mnrlV5me0LJPvCo1WvfCu3w28GQ+G3gyX592E/nzlpEdc+Vx9pA+bP12PSXrN1fZNn/+N2QN2XbMtKwMMvp0IX9eMPasaPU6smPKs4b0JX/+N9sdY6IKF63E0tPI6Ns1JpY+cWMpmL+sQpti6+XPX0bW4N4V6mcN7p2SNpVR2wKNrW11NaBvb+YtWFT+fN6CRXRo3462bVozoG9vlq9cXZ7sBuWL6d+3d7xdJU3UzltT+B1ZVym+QUPSKbGNIHffQPD1+X1m9nMza2VmaeGYlZY1bhx/f6XAZOBOM+sW9ogeEF5QNRU42swON7PmBONmC4H/1rZfMzvGzAaEyfBGoIRtP0dfACeHxzoKOKSGXa0B+sU8bxXGsJYgOb61lvqVPQNcZ2adzKwjcD3wZA31y9rTwsxOMbM24bCMTSTp98L302bQ6aTDydy1B+mts+l2yRi+n/p23LrrX/+IrEG9aDfqR1hGc7pdOpb8OUsoWBj8LbJ22gy6XjKG9DYtyezfnU4nH1HtvhpSaX4hG17/kG6XjyMtK4OcYbvRduR+rH1uRpW6a6e9TedzjqN5l/Y079yOLhOOZ+3UYHjz5g++wktK2eXsY7AWzeg0flSw/v0vk9mcCtS2QGNrW3FxCYWFRZSUlFJSWkphYRHFxSVV6h131OE8/8obLFy8lE2bt/Dgo1MYPSqYkbBPrx7sNqAf9z3yFIWFRUx/533mL1zMiOEHJrs5FUT5vEE0f0fWVYovHks6q6chjbITMrNTgEsILmLKBRYRXNz0qLsXmdmjwHJ3v676vZTvKwu4DRhD8LX7TOBId883sxOAWwiGH3xBMBvDrHC7JcAv3X16+PxGYIC7n2pml4bxdQLWAw+6+01hvWEEdynpRdDz2QxY6O7XWXAjhSfdvUdY93jgL0BrgoHlDxBcIHYYsA74XbivXd19gZntCjxL0JM7w91Hx8YZztrwx7CthHWvdPeCyseObSPwLsEVoPsD6cA84FJ3f6+m1/bj7ifUyw9h5wnH0fWCE0jLbMG61z5g6W8fwIuCERN7vHUPK//yHOteeBeA1j/5Ab1uPoeM7p3Y8vnXLL70zxQt/y5oT4tm9L7tPNoffQClBUWsuu8F1kx6eYdiMkusaeltc+jzp4tpffBeFK/fzIrbnmDdi++Ss98Qdn3id3w+aNt0PT2uPYOO44Lk4ftnprP8lsfKy7J270ufOy4ia2AP8r9ezpLf/JX8WYurHC+Z1LZAstu21xd37vC29z78JPdPfqrCuvPPOoUTjx7Jcaeey8tPPkjXLrsA8NiU53n4yWcpLCxkxPCDuP6Ki2jRIrimdsWqNVx7y0S+nDWPrp07ce3lF3LAvolPFzVz6GUJbb8znzevh1tg7Yy/I/dd8ULiDavFH3ufmtAv4iuXPtngMdYnJbYiKVZfie3OKNHEVqS+JZLY7uwSTWx3ZvWR2O6MlNjWv9TOmCwiIiIiDaYxjpNNhBJbERERkYhqat+bKbEVERERiajSJpbaalYEEREREYkE9diKiIiIRJTG2IqIiIhIJDStgQhKbEVEREQiSz22IiIiIhIJpY1qFtrE6eIxEREREYkE9diKiIiIRFRTm+5Lia2IiIhIRDWttFaJrYiIiEhkNbWLxzTGVkREREQiQT22IiIiIhGlMbYiIiIiEglNK61VYisiIiISWRpjKyIiIiLSCKnHVkRERCSiNMZWRERERCKhaaW1SmxFUs6sqf3aiQb36N6APcrvyZlDL0t1CA1mry/uTHUIDeaLvS5PdQiNVlMbY6vEVkRERCSivIn12eriMRERERGJBPXYioiIiESUhiKIiIiISCRoVgQRERERiYSmldZqjK2IiIiIRIR6bEVEREQiSkMRRERERCQSmtrFYxqKICIiIhJRnuC/RJhZezN708y+Dv9vV029EjP7Ilxejlnf18w+MrMFZvZ3M2tR2zGV2IqIiIhEVGmCS4J+C/zb3XcF/h0+jyff3YeGy3Ex6/8A3OXuA4D1wNm1HVCJrYiIiIg0hOOBx8LHjwGj67qhmRlwGDBte7ZXYisiIiISUYkORTCzCWb2ScwyYTsO39ndV4WPVwOdq6mXGe77QzMbHa7rAGxw9+Lw+XKge20H1MVjIiIiIhGV6HACd58ETKqu3MymA13iFF1baT9uZtUN2u3t7ivMrB/wlpl9CWzckXiV2IqIiIhEVKk37HRf7n5EdWVmtsbMurr7KjPrCnxbzT5WhP8vMrMZwN7Ac0BbM2sW9tr2AFbUFo+GIoiIiIhIQ3gZOCN8fAbwUuUKZtbOzDLCxx2BA4HZ7u7A28DPa9q+MiW2IiIiIhHlCS4Juh0YYWZfA0eEzzGzYWb2UFhnMPCJmc0kSGRvd/fZYdlVwGVmtoBgzO3DtR1QQxFEREREIiqVdx5z97XA4XHWfwL8Mnz8X2DParZfBOy3PcdUYisiIiISUYneZKGx0VAEkRhmtsTMjjCz8WbmZnZXpfLjw/WPhs/7hM+3hMsaM3vFzEYkK+b0tjn0f+i37D1/Cnt+OIn2ow+utm73a05n6JePM/TLx+l+zekVyrKG9GXwaxPZ++u/M/i1iWQN6dvQodcqym0r0/mcYxn6+WT2mfsUfSZehLWovr+h1UF7ssc7f2GfBVMY9OzvadG9U3mZtWhGn4kXsc/cpxj6+WQ6Tziu2v00tCift6i27elpLzP2rF+x9/BjufbmiTXWfXzKCxxy7MnsP+JErrv1ToqKisrLVqxaw5kXXcWww0Zz7Lhz+ODjzxs69DqL4s+aVKXEVqR6C4GxZhb72+8MYH6cum3dPQfYC3gTeMHMxjd8iNDr5gl4UTEzh45n8cV30evWc8kc2LNKvY6njKTdkfsza+SlzBrxa9oesS+dTj0SAGvejAGTr2bd8zP4YvdTWDvtLQZMvhprntovdaLcNoDWhwyl64UnMvcXNzBz/wlk9OpM98vHxa3brF0rBvztKlbc8Qyf734auTMX0v+By8vLu192Epl9uzJz/wnMHXM9Xc8fTevheyerKRVE+bxFtW2dOnbg3PEnccLRI2us9/5Hn/LQk1N5+J7beOO5x1i+cjX3PvxkefmVN9zO4IH9ee/1v/OrCWdw2XW3sG79hgaOvnZR/VmrixTfeSzplNiKVG818CVwJAT3vAZ+THCVZ1zuvtrd7wFuBP5gZg36M5aWlUG7UQew4o6nKc0rYMvHc9j45sd0+NnwKnU7jjmM1ZNeYuuqtWxdvY41k16iw9jDAGh1wB5Ys3TWPPQPvKiYbye/Cma0OjDusKekiHLbynQccyjfTZlOwfxllGzMZeU9U+k49tC4dduN+hEF85ex/pX/4oVbWTlxCtmD+5DZP5ivvMOYQ1l591RKNuZSsGA53z39ZrX7akhRPm9RbtuI4Qdy+ME/pm2b1jXWe+n16Zx4zJEM6NebNq1bcd74cbz42nQAlnyznNnzF3Dh2aeSmZHBiEMPYtd+fXhzxvvJaEKNovizVleleEJLY6PEVqRmjwNl3yGeRDDVSGEdtnse2AUY1EBxAZDRrxteUkrh4pXl6/JmLyZrYK8qdTMH9iR/9uK49bIG9iR/zpIK9fPnLIm7n2SJctvKZA3qSf7sJeXP82ctofku7Uhv16pq3YG9yIupW5pfSMHSNWQN6kl6m5a06NK+Qnne7NS0McrnLcptq6sFi5cyaMC2YRODBvRj7br1bNi4iQWLl9KjW1datsyuUL5w8dJUhFpBFH/W6irRO481NkpsRWr2AjDczNoQJLiP13G7sk++9g0SVSi9ZRalm/MqrCvZnEd6TlacupmUbMqLWy+tZVaFspr2kyxRbluZtOwsiivFDUF7qtSt1Magfi5pOVnl9UtiXq+STalpY5TPW5TbVld5efm0ymlZ/jwnfJybl09efgGtYpLaoDyb3Lz8pMYYTxR/1uqqqQ1FSP0gM5GdmLvnm9mrwHVAB3d/38x+WodNy+5nvS5eYXiv7QkAV7fdixNb9tmh+Epy80lrVfGDJD0nm5ItVT9ISnILSI+pG1uvNDe/QllN+0mWKLat/QkH0+cP5wGw+aM5lOblk95q2wdiWXtLcguqbFuaW1ChLgTtKN2SX14/PSeb4sLgLpTprbJS0sYonrcyUW5bXWVnZ7Eld1tSlxs+bpmdRXZWJlvyKiaEubl5tMxOftLXFH7WJD712IrU7nHgcuDJ2irGOIHg1oHz4hW6+yR3H+buw3Y0qQUoXLQSS08jo2/X8nVZQ/qQP/+bKnUL5i8ja0ifuPXy5y8ja3DvCvWzBveOu59kiWLb1r3wLp8NPJnPBp7M16fdRP68ZWTHXA2fPaQPW79dT8n6zVW2zZ//TYU2pmVlkNGnC/nzgjGDRavXkV3hNeibkjZG8byViXLb6mpA397MW7Co/Pm8BYvo0L4dbdu0ZkDf3ixfubo82Q3KF9O/b+94u2pQTeFnra7cPaGlsVFiK1K7d4ARwF9qq2hmnc3sIuAG4Gp3b9BvckrzC9nw+od0u3wcaVkZ5AzbjbYj92PtczOq1F077W06n3Mczbu0p3nndnSZcDxrp74FwOYPvsJLStnl7GOwFs3oNH5UsP79Lxsy/BpFuW1lvp82g04nHU7mrj1Ib51Nt0vG8P3Ut+PWXf/6R2QN6kW7UT/CMprT7dKx5M9ZQsHC4Nbpa6fNoOslY0hv05LM/t3pdPIR1e6rIUX5vEW5bcXFJRQWFlFSUkpJaSmFhUUUF5dUqXfcUYfz/CtvsHDxUjZt3sKDj05h9KgjAOjTqwe7DejHfY88RWFhEdPfeZ/5CxczYviByW5OFVH8WaurpnbxmDXGbFykoZjZEoK7ofQAfunuB8WpczPQw93Hm1kfYDGQC1j4/yfAn939n3U55ic9Rif0Q5jeNoc+f7qY1gfvRfH6zay47QnWvfguOfsNYdcnfsfng7ZNadPj2jPoOC74EPr+meksv+Wx8rKs3fvS546LyBrYg/yvl7PkN38lf9biKsdLpp25be6W0PZlOk84jq4XnEBaZgvWvfYBS3/7AF5UDMAeb93Dyr88x7oX3gWg9U9+QK+bzyGjeye2fP41iy/9M0XLvwOCuTV733Ye7Y8+gNKCIlbd9wJrJlU7gUeNzBL7XNiZz1uidua27fXFnTu87b0PP8n9k5+qsO78s07hxKNHctyp5/Lykw/StcsuADw25XkefvJZCgsLGTH8IK6/4iJatGgBBPPYXnvLRL6cNY+unTtx7eUXcsC+iU+F9cVel9deqRY748/aviteqJ9fJDU4ttcxCf1A/+ObVxo8xvqkxFYkxRJNbCU16iux3RklmthKaiSS2O7s6iOx3Rkpsa1/unhMREREJKIa45RdiVBiKyIiIhJRjXGcbCKU2IqIiIhEVFMbcqpZEUREREQkEtRjKyIiIhJRjfHuYYlQYisiIiISUbp4TEREREQiQRePiYiIiEgk6OIxEREREZFGSD22IiIiIhGloQgiIiIiEgm6eExEREREIqFUY2xFRERERBof9diKiIiIRFTT6q9VYisiIiISWbp4TEREREQioaklthpjKyIiIiKRoB5bERERkYhqanceU2IrIiIVPN0sM9UhNJhxWwtTHUKD+WKvy1MdQoMZOnNiqkNotJraUAQltiIiIiIRpRs0iIiIiEgkNLWhCLp4TERERETqnZm1N7M3zezr8P92ceocamZfxCwFZjY6LHvUzBbHlA2t7ZhKbEVEREQiqhRPaEnQb4F/u/uuwL/D5xW4+9vuPtTdhwKHAXnAGzFVrigrd/cvajugElsRERGRiHL3hJYEHQ88Fj5+DBhdS/2fA6+7e96OHlCJrYiIiEhEJdpja2YTzOyTmGXCdhy+s7uvCh+vBjrXUv8k4JlK624xs/8zs7vMLKO2A+riMRERERGJy90nAZOqKzez6UCXOEXXVtqPm1m1XcBm1hXYE/hXzOqrCRLiFmEMVwG/ryleJbYiIiIiEdXQ0325+xHVlZnZGjPr6u6rwsT12xp2NRZ4wd23xuy7rLe30MweAX5TWzwaiiAiIiISUaXuCS0Jehk4I3x8BvBSDXXHUWkYQpgMY2ZGMD73q9oOqMRWREREJKI8wX8Juh0YYWZfA0eEzzGzYWb2UFklM+sD9ATeqbT9U2b2JfAl0BG4ubYDaiiCiIiIiNQ7d18LHB5n/SfAL2OeLwG6x6l32PYeU4mtiIiISETVw3CCRkWJrYiIiEhENfTFYzsbJbYiIiIiEaUeWxERERGJhKbWY6tZEUREREQkEtRjKyIiIhJRTW0oQsI9tmb2qJndHD4ebmbLEw8r4Th+Ymbz6rDNKWb2RsNHJ2XM7HUzOyN8PN7M3kt1TPGY2QNm9rs61p1hZr+svaaIiEhypXge26Src2IbfnivN7OMhgyoPrj7f9x9UB3qPeXuI5MR086oof8QMbMbzezJ2HXu/lN3f6yhjhkeN+FE093Pc/eb6iumhpTeNof+D/2WvedPYc8PJ9F+9MHV1u1+zekM/fJxhn75ON2vOb1CWdaQvgx+bSJ7f/13Br82kawhfRs69FpFuW1lOp9zLEM/n8w+c5+iz8SLsBbVf5HW6qA92eOdv7DPgikMevb3tOjeqbzMWjSjz8SL2GfuUwz9fDKdJxyXjPBrdPDZo7jx4we49cvJ/OKP55JeQ9vKjPzVidy5ZAq7HrhH+bpjrzmVq9++i1u/eoSr/j2RYSf+pCHDrpOonrcotuvpaS8z9qxfsffwY7n25ok11n18ygsccuzJ7D/iRK679U6KiorKy1asWsOZF13FsMNGc+y4c/jg488bOvR64V6a0NLY1CmxDe8I8RPAgdT/thRpQGaWnuoYtkevmyfgRcXMHDqexRffRa9bzyVzYM8q9TqeMpJ2R+7PrJGXMmvEr2l7xL50OvVIAKx5MwZMvpp1z8/gi91PYe20txgw+WqseWpHK0W5bQCtDxlK1wtPZO4vbmDm/hPI6NWZ7pePi1u3WbtWDPjbVay44xk+3/00cmcupP8Dl5eXd7/sJDL7dmXm/hOYO+Z6up4/mtbD905WU6oYdPAPOPy847j/5Ju56cCL6dCrM0ddOqbGbTr06sxeo37ExjXrKqwvyi/g4V/ewbV7nsUzl9/P6BvOoM8+Axsy/BpF9bxFtV2dOnbg3PEnccLRNfdjvf/Rpzz05FQevuc23njuMZavXM29D2/rm7nyhtsZPLA/773+d3414Qwuu+4W1q3f0MDRy/aqa4/t6cCHwKNsu+fvdjMzN7MLzOxrM9tsZjeZWX8z+6+ZbTKzqWbWIqb+MWb2hZltCOv8IKZsbzP7LNzP34HMmLIKPZFm1tPMnjez78xsrZn9NVxf4avwML7zwvg2mNm94f2Jy8rPMrM5Yc/1v8ysd7jezOwuM/s2bMeXZratu6Hia9DGzB42s1VmtsLMbjazdDNrEbb14rBeupm9b2bXh8/bm9kjZrYyPP6LdXydlpjZ1WY2O9zuETPLNLOWwOtANzPbEi7d4sR7tJl9HrZrmZndGFPWJ3zNzjCzb8zsezO7Niw7CrgG+EW475nh+sq9qWZmfzWzjWY218wOjyk4M3y9N5vZIjM7t1Jsx4ft3mRmC83sKDO7heCPsL+Gxy0717uZ2Ztmts7M5pnZ2Jj9PGpm95vZa2aWCxxqFYe2tDOzV8L3z/rwcY9qzu8AM3snbM/34XuzwaRlZdBu1AGsuONpSvMK2PLxHDa++TEdfja8St2OYw5j9aSX2LpqLVtXr2PNpJfoMDa4qUurA/bAmqWz5qF/4EXFfDv5VTCj1YF7NmT4NYpy28p0HHMo302ZTsH8ZZRszGXlPVPpOPbQuHXbjfoRBfOXsf6V/+KFW1k5cQrZg/uQ2T+4WU+HMYey8u6plGzMpWDBcr57+s1q95UM+/7sED6aOoM1Xy8nf1Mub/75efb9+SE1bvOzm87kldufpmRrSYX1/7prGt8uXIm7880XC1j0v7n03mfXhgy/RlE9b1Ft14jhB3L4wT+mbZvWNdZ76fXpnHjMkQzo15s2rVtx3vhxvPjadACWfLOc2fMXcOHZp5KZkcGIQw9i1359eHPG+8loQkJK8YSWxmZ7EtunwuVIM+ucwDGPBH4I/Ai4EpgEnEpwj+A9gHEQJK7AZOBcoAPwIPCymWVYkPy+CDwBtAeeBX4W72AW9L69AiwF+hDcsm1KDfEdA+wL/AAYG8aLmR1PkKidCHQC/gM8E24zEjgYGAi0CbdbW83+HwWKgQHA3uG2v3T3ovB1+L2ZDQZ+C6QDt4TbPQFkA7sDuwB31fY6xRzzlLAd/cMYr3P3XOCnwEp3zwmXlXHizSU4/22Bo4HzzWx0pToHAYMIbpt3vZkNdvd/ArcCfw/3vVc1r8f+wEKCe0DfADxvZu3Dsm8Jzkdr4EzgLjPbJ2z3fsDjwBVhbAcDS9z9WoJzc1F43IvCJP5N4OnwtTsJuM/MhsTEcTLBa90KqDzuNw14BOgN9ALygb9W056bgDeAdkAP4C/V1KsXGf264SWlFC7eduryZi8ma2CvKnUzB/Ykf/biuPWyBvYkf86SCvXz5yyJu59kiXLbymQN6kn+7CXlz/NnLaH5Lu1Ib9eqat2BvciLqVuaX0jB0jVkDepJepuWtOjSvkJ53uzUtrHLwB6snLO0/PnKOUtp3akt2W1z4tbfa9T+FBcVM2fGFzXut3lGc3rt1Z81X6fkcg4guuctqu2qqwWLlzJowLZhSoMG9GPtuvVs2LiJBYuX0qNbV1q2zK5QvnDx0ni72qm4e0JLY1NrYmtmBxF8oE91908JkpCTEzjmH919k7vPAr4C3nD3Re6+kaAHsey7ignAg+7+kbuXhOMyCwkS4h8BzYG73X2ru08DPq7mePsB3YAr3D3X3QvcvaYLlm539w3u/g3wNjA0XH8ecJu7z3H3YoKkbWjYa7uVICHaDbCwzqrKOw7/IBgF/DqM5VuCBPUkAHf/CriZIGn/DXCau5eYWVeCJPQ8d18ftvmdOrxOZf7q7svcfR1B8hb/u6U43H2Gu3/p7qXu/n8EyXzlbpf/5+757j4TmAlUl8TG8y3bzuPfgXkECTTu/qq7L/TAOwQJY9ngurOBye7+ZhjbCnefW80xjiFIeh9x92J3/xx4Doj9XvQld38/3FdBpddgrbs/5+557r6Z4DWsrutpK8HPS7c6vNcSlt4yi9LNeRXWlWzOIz0nK07dTEo25cWtl9Yyq0JZTftJlii3rUxadhbFleKGoD1V6lZqY1A/l7ScrPL6JTGvV8mm1LaxRXYmBTHx5IePM+LElNEyk1FXnMQL/6/24fc/v/WXrJyzlLnvzKy/YLdTVM9bVNtVV3l5+bTKaVn+PCd8nJuXT15+Aa1iktqgPJvcvPykxrgjmlqPbV0GmZ1BkHx+Hz5/Olx31w4ec03M4/w4z7uEj3sDZ5R9NR9qQZCkOrDCK/4pUd2fTT2BpWEyWherYx7nAWXdC72Be8wsduS5Ad3d/a3wK+97gd5m9jzwG3ffVGnfvQkS8lW2bYRDGrAsps5jBInTc+7+dUwb1rn7+jjx1vQ6lYnd/9JKZTUys/2B2wl601sAGQQ95LGqe83qIt557BYe+6cEvbgDCV6nbODLsF5P4LU6HqM3sL+ZbYhZ14ygF7zMMqphZtkE7/ejCHpiAVqZWbq7l1SqfiVBr+3/zGw9MNHdJ8fZ5wSCP0q4uu1enNiyTx2bUlFJbj5prSr+sk3PyaZkS9VftiW5BaTH1I2tV5qbX6Gspv0kSxTb1v6Eg+nzh/MA2PzRHErz8klvte3Dvqy9JbkFVbYtzS2oUBeCdpRuyS+vn56TTXHhxuBxq6yktnGf4w9kzK3nALDo47kU5RVUSGIzw8eFcWI68tc/59MX/sP65d/VeIxjrz6FLgN7ct+45F7XGdXzFtV27ajs7Cy25G5LxnPDxy2zs8jOymRLXsVEPjc3j5bZO3ey3hTV2GNrZlkEX6sfYmarzWw1cCmwl5ltT6/cjlgG3OLubWOWbHd/BlgFdLeY7JDgK+Lq9tPLzBK9UmQZcG6leLLc/b8A7v5nd/8hMIQgEbuimn0UAh1j9tHa3XePqXMfwdCJI8Pe8rLt2ptZ22r2Wd3rVCb2apteQNl3u3X5U+xp4GWgp7u3AR4gSOjroi77j3ceV4ZDKZ4D/gR0dve2BIlsWd1lBEMr6nLcZcA7lV6jHHc/v46xXk4w1GJ/d29NMOwB4rwO7r7a3c9x924Ew0PuM7MBcepNcvdh7j5sR5NagMJFK7H0NDL6di1flzWkD/nzv6lSt2D+MrKG9IlbL3/+MrIG965QP2tw77j7SZYotm3dC+/y2cCT+WzgyXx92k3kz1tGdswMDdlD+rD12/WUrN9cZdv8+d9UaGNaVgYZfbqQPy8YD1m0eh3ZFV6Dvklt42cvvc/Vu4/n6t3H87fxt7N6/nK6xbzu3Qb3ZtN3G8jbsKXKtrseuAcHjT+KGz9+gBs/foC2XTtwxr2/5rDztl2rfOSlP2e34UN58LRb4ybHDSmq5y2q7dpRA/r2Zt6CReXP5y1YRIf27WjbpjUD+vZm+crV5cluUL6Y/n17x9vVTkVDESoaDZQQJGtDw2UwwRjG06vbqJ78DTjPzPa3QMvwQqZWwAcE41R/ZWbNzexEgiEH8fyPIBG+PdxHppkduAPxPABcbWa7Q/lFYGPCx/uGcTYnGJNaAFSZIyMcnvAGMNHMWptZmgUXzx0S7uc0gvHH44FfAY+ZWU643esESVK7sM1lyVVNr1OZC82sRzh29Vqg7IKmNUAHM2tTQ7tbEfQWF4TjWrdnGMoaoI+Z1fQ+24Vt53EMwfvrNbb1Dn8HFIe9t7GXtD4MnGlmh4evY3cz2y3muP1i6r4CDDSz08LjNA/P2eA6tqMVwbcJG8LX8IbqKprZmJgLy9YTJMwNNl9KaX4hG17/kG6XjyMtK4OcYbvRduR+rH1uRpW6a6e9TedzjqN5l/Y079yOLhOOZ+3UtwDY/MFXeEkpu5x9DNaiGZ3GjwrWv/9llf0kS5TbVub7aTPodNLhZO7ag/TW2XS7ZAzfT307bt31r39E1qBetBv1IyyjOd0uHUv+nCUULFwBwNppM+h6yRjS27Qks393Op18RLX7SoZPnn+X/X9xKJ0HdCezdTYjLj6Bj6e9E7fu/SffzB1HXsHEUVcxcdRVbFyznmev+RvvPf4vAA6/4Hj2Oe5AHjj15riJcbJF9bxFtV3FxSUUFhZRUlJKSWkphYVFFBdX/rINjjvqcJ5/5Q0WLl7Kps1bePDRKYwedQQAfXr1YLcB/bjvkacoLCxi+jvvM3/hYkYM35F0IrlK3RNaGpvaEtszgEfc/ZuwJ2q1u68muHDmlHroBa2Wu38CnBMeaz2wgCDhw4MLrU4Mn68DfgE8X81+SoBjCS7W+gZYHtbf3nheAP4ATDGzTQTjg38aFrcmSDDXE3yVvha4o5pdnU6QtM0O608DuppZL+Bu4HR33+LuTwOfsG3Ix2kE4zfnEoxL/XUYV7WvU4ynCRLqRQRjpG8Ot51LMGZ2kQUzKsQbonABwQVtm4HrganVv0pVlA1ZWGtmn1VT5yNgV+B7giEYPw/HtG4mSO6nhu06maDnmDD2/xFeUAZsBN4hGHIAcA/w83AGgz+H+xpJMJZ5JcHQiT8QJM51cTeQFcb4IfDPGuruC3xkZlvCeC9x90U11E/Y0msfJC0zg71mPkbfey/nm2sepGD+MnL2G8Le87Z13H/35L/YOP0Tdp9+D7v/+89sfOtTvnsySBx8azELzr6NDj87lL1nP0XHXxzOgrNvw7fWdQRPw4hy2wA2zficVfe/yG7P3sRe//sbhcu/ZcXEbe3a4617aH9C8Dds8bpNLJzwR7pfdQr7zHqClnsPZOH520ZGrZj4DIVLV7PXR5PY7bmbWf3AS2yakbp5Nue+M5O3H3iZC575Hde//1fWr/ief961bRTTlW/cwT7HB0lB3oYtbP5uY/nipaXkbcylKK8QgKOvHEe7bh25ZsY93DbrUW6b9SiHXzA6Fc0ConveotquBx97hh8edjwPPzmVV/71Fj887HgefOwZVq3+ln2POIFVq78F4KAfDeOsU37OmRf/lhEnnk63Lrtw4dmnlu/njt9fzay5X/Pjo8Zw9/2PcOfN19K+XduUtGl7NLUbNFhj7GaWujOzJQSzLkxPdSwS3yc9RuuHsBFyr+uInMbnmeY7/X14dti4rYWpDkF2wNCZNd9YobFq3rFfg/8i6dxmt4Q+Y9ZsnNuoftklfEtdEREREZGdQepvvSMiIiIiDaIxTtmVCCW2EefufVIdg4iIiKRGUxtyqsRWREREJKIa48wGidAYWxERERGJBPXYioiIiESUhiKIiIiISCTo4jERERERiYSm1mOrMbYiIiIiEgnqsRURERGJqKY2K4ISWxEREZGIco2xFREREZEoUI+tiIiIiESCLh4TEREREWmE1GMrIiIiElEaYysiIiIikaChCCIiIiISCe6e0JIIMxtjZrPMrNTMhtVQ7ygzm2dmC8zstzHr+5rZR+H6v5tZi9qOqcRWRERERBrCV8CJwLvVVTCzdOBe4KfAEGCcmQ0Ji/8A3OXuA4D1wNm1HVCJrYiIiEhEeYJLQsd2n+Pu82qpth+wwN0XuXsRMAU43swMOAyYFtZ7DBhd2zE1xlYkxYYtf9GSeTwzm+Duk5J5zGSIarsg+W3bN1kHQuetsVLbGo/iohUJfcaY2QRgQsyqSfX8+nQHlsU8Xw7sD3QANrh7ccz67rXtTD22Ik3PhNqrNEpRbReobY2V2tY4Rblt283dJ7n7sJilQlJrZtPN7Ks4y/GpiFc9tiIiIiKyQ9z9iAR3sQLoGfO8R7huLdDWzJqFvbZl62ukHlsRERERSZWPgV3DGRBaACcBL3swJcPbwM/DemcAL9W2MyW2Ik1PZMaOVRLVdoHa1lipbY1TlNuWVGZ2gpktBw4AXjWzf4Xru5nZawBhb+xFwL+AOcBUd58V7uIq4DIzW0Aw5vbhWo/Z1CbuFREREZFoUo+tiIiIiESCElsRERERiQQltiIiIiISCUpsRUQkZcysn5n1SXUc9cHMLjOzoeHjH5nZN2a22MwOSHFosh2i9J5sipTYikScme1mZr8zs3tjnv8g1XFJVWaWVtuS6hgTZWbPmNmPw8dnArOAWWZW6z3gG4FLgcXh49uAO4GbgbtTFZDULuLvySZHsyKIRJiZjQHuBZ4HTnb31mY2DLi9HibVlnpmZqVUf3t2A9zd05MYUr0zs2+BHu5eZGZfAucBG4AX3X3XlAaXIDPbFP6MtQKWAp3cvcTMNrh72xSHVy/MbBDQHljn7vNSHU99iPJ7sinSncdEou33wAh3n2lmvwjXzQT2SmFMCTOz/1B9AgiAux+cpHDqU99UB5AELcIEojvQ3t3fBzCzzimOqz4sC3v+dgfeDZPa1kBJiuNKmJmdDvwB2IXgjyzMbA3wW3d/LJWx1YMovyebHCW2ItG2C/B/4WOP+b+xf1XzUKoDaCDfAK3cfVPlgjBB2pz8kOrdF2Z2NdAbeBUgTCiqtLkRugKYBhQBPwvXHQP8L2UR1QMzO4Lgm58bCb79WQl0B04E/mxmK939zdRFmLAovyebHA1FEIkwM3sDeNLdHzezde7e3sxOBU5y92NSHZ9UZGaXAj9091PjlD0BfOzuf05+ZPXHzPoDNwFbgSvc/Vsz+zmwr7tfldro6p+ZNQdw962pjmVHmdnLwPR47z0zuwg4qjH/Pmlq78moU2IrEmFmthvwBsEFLT8CZgADgZHu/nUKQ0tI+LVojdz98WTEUp/M7AtgTLxzY2YDgGfdfe+kByZ1ZmZtgEFATux6d38rNRElzsxWAXu5+7dxynYB/s/duyQ/MpGqlNiKRJyZZRN8HdobWAa84u5bUhtVYsKLrBYAqwnH+1XijXGMrZmtd/d2O1reWJjZSGAoVZO/61MSUD0xs/EEX9lvAfJiitzd+6UkqHpQdlFcDeWb3b1VMmNKlJmdVZd67j65oWOR+qUxtiIR5+55wNRUx1HP7gHGEIw5fZzg6uXC1IZUL4rNrLO7r6lcEF7IEoWLkP4KjAXepmLyFwW3AD9399dTHUh9MzMj/h+RRuMcs39aHeo4oMS2kVGPrUiE1TB7QCGwHHje3f+R3Kjqh5mlA0cBpwMHA68Aj7n7eykNLAFmNhVY6u5XxCn7A9DX3ccmP7L6Y2brCL7WXpbqWOpbOEtAN3dv9H+AxGoK09BJdCixFYkwM7sJOAN4jGAYQk+CRPBpgg+ks4E73P2PKQuyHoTjGq8FLiOY3uztFIe0Q8xsIPAh8D7B1fWrgK4EV9j/GDigMY+NBjCz+QQXyEVhhocKzOwyoBVwk7uXpjqe+mJmvWur4+5LkxFLQ6vcMx2l89hUKLEViTAz+wgY7+5zYtbtRtCzub+Z7Qc84+79UxZkAsKE9iSC5L0T8CTwZ3dfn9LAEmBm/YD/BxwBdADWAtOB6919cU3bNgZmdi5wNMGduSoMuXD3RSkJqp6Y2TKgC8F0X2tjy9y9V0qCklqFU3v9leCbn7axZeqJbnyU2IpEmJltBHaJHX9qZlnAqrI7IZnZFnfPqWYXOyUzO5ag5/kg4CXgibJJ1RszMzsQOC7eFENmdjvBWOIPkx9Z/Qm/1o6n0X+dbWaHVFfm7u8kM5b6ZGa1jjN19zpdjLUzMrN/EIz3vg14hyDBvRF4zd3/lsLQZAcosRWJsPAX9mbgeoIxtT0IegPbuPsxZrYnwTjbRnXbyDA5mkcwrjY/Xp3GeIW9mb0K3Ofur8YpOwq40N2PTX5k0pTF/Ly9TNAbXYW7/y6pQdUjM1sL9HL33LLbH5tZe+C/7r5bquOT7aNZEUSi7QzgPmA2wc/7VoI7B50RlhcB41ITWkIeJ7iYpWOqA6lnQ4F/VlM2nQhdoW1mvQjuXrU8KheShTdjuI7givtuBHfoegK4xd3jJoSNxAkE35CcDrwIPO7uH6Q0ovpVAhSHjzeYWSeCu451T11IsqPUYyvSBJhZGsEY1M4EH04nu3u31EZVv8KJ4g8C5sSOKW5MzGwzwdCRKr3Q4RCSbxvbfKGVmVlXYApwAME41A4EF8yd5O4rUxlboszsLmA/gm9FlhLMHf074BN3vzSVsdUHM2tHMKb9dKA9QdL+V3ffkMq4EhV+szXZ3V8wsweBXQm+Ccp290NTG51sr7RUByAiDSvsfbgYeB34HBgGXJLSoBJkZt3N7Hkzm2tmj5jZ7sAc4AFgppmdlOIQd9RcYGQ1ZSPD8sbufmAm0M7duwLtCN6XD6Q0qvoxhmCM9BvuPs/d3yDo7WzUU7SVcff17n4/cCTB2PYbgSjcCe80grG1AL8mmGP5K+DkVAUkO05DEUQiKPxK9DhgPMGH0ALgGaAPMDberTEbmQeAb4FLgV8A/wJ+Gfa4HE9w3/cpKYxvR90FPBjO0fuiu5eGve2jCe5odVkqg6snBwFd3X0rQDiu8UpgRWrDqhfxbmBQ0/pGI3wfHkkwjOkQ4FXg8MZ8UVyZ2B7n8NuSm1IXjSRKQxFEIiicBL8UeBR42t0/C9dXe8/3xiS82KOruxeFtwzeAGR4+AvNzDa6e5tUxrijwrlQ/x+QAXxPMI64ELjB3e9MZWz1wcy+Jrg718yYdT8guIhxQOoiS5yZ3c22oQjfEAxFuI5gKMKvUxdZYsxsIkGv85dsu9NfQWqjSoyZXevut4SPf19dvcZ4EWpTpx5bkWj6P4Kesf2Br81scWOe2zWO5mUX47h7XjhlWexf6Y22h8zd7zSzhwjGoJbNY/uBu29KbWT15o/AdDN7mG3jUM8kGIva2F1JkMjey7aLx54Bbk5lUPXgUmAhwc0nLgAuCO5jsI27H5yCuBLRI+Zxz5RFIfVOPbYiERXeLajsSuZewBsEXyEOdvdG/bWvmeURTPJf9un6InB8zPN/uHvLFIQmdWBmhxGMXyxP/tz936mNSqpjZmfUVsfdH0tGLPUtHGIxHHg/dr5vabyU2Io0AWZ2EEGCO5ZgWpvJ7n5laqPacWa2hOrvXQ+Au/dNTjTSlJnZwe7+bvj4sOrquftbyYuqftXUrjKNvH2bG/tsI7KNEluRJsTMMgnnpHT3n6Y6Hmkaojye0cy+cvc9wsfV3fLY3b1fEsOqVzW0q0xjb9+rwE2N/a5+EtAYW5EmJLzg45lwEUmWyI5nLEtqw8eR/JYgqu2KsRR43cxeApYR821QY/xjq6lTj62IiEg9MLOX3P34OOufd/cTUxGT1M7MHqmuzN3PTGYskjgltiIikjRmts7d28dZ/62775KKmOqLmW1y99Zx1sdts4jUPw1FEBGRZGpeeUV4Q5H0FMRSL2LGDbeIM4a4H8FX3bITM7PdCO4c19ndLzKzQQRzY/9fikOT7aTEVkREGpyZ/Ydg7GKmmb1bqbgH8N/kR1VvysYNp1FxDLETjNm8MdkBSd2Z2RjgPuA5gmnoLiKYs/d24IgUhiY7QEMRRESkwYVzoRpwP3BeTJEDa4C3ym6z21iZ2Tnu/rdUxyHbx8zmACe5+0wzW+/u7cJvEVa6e6dUxyfbR4mtiIgkjZnt5u5zUx1HQzKzVgS3Qi6/PZe7L0pdRFKT8BbdHd3dy8ZDm1kzgsS2UY/7borSUh2AiIg0He4+18zOMrM3zWxW+P/ZVvkerY2QmQ02s8+BjcCCcPk6XGTn9SlwWqV1JwH/S0EskiCNsRURkaQxsz8S3P74boKLqnoBvwEGAY32bnih+4G3gUOBxUAf4DYa9/jhpuBXwBtmdjbQ0sz+BQwERqY2LNkRGoogIiJJY2bfAvu4+/KYdT2Bzxr7eEYzWw/s4u5bzWyDu7c1s5bAV03gJgeNmpllA8cAvQku+HvF3bekNirZERqKICIiybQ5XCqv25SCWOpbAdumM/vezHoRfM52SF1IUhsz+7O757n7VHe/w92nuPsWM7s71bHJ9lNiKyIiyXQ38LyZjQjHpI4EngXuMrN+ZUtqQ9xh/wHGho+nAa8D7wBvpSwiqYvx1ayvPO5WGgENRRARkaQxs9I6VHN3b7Q3bAAwszSCOVFbAY+5e16KQ5JKzOys8OFfCeaujdUPGOPug5IblSRKia2IiEg9MLPfuPuf4qy/zN3vTEVMUj0zezt8+BOC3vYyZXMr3+PuHyY9MEmIElsREZF6YGab3L11nPXr3L19KmKS2pnZze5+XarjkPqh6b5ERKRBmdk/3f2o8HHZrXWrcPeDkxpYPTGzw8KH6WZ2KDE3ZiD4SrvyxXKyc3nXzAa6+/yyFWY2COjl7m+mMC7ZAUpsRUSkoT0e8/ihlEXRcB4O/88EJsesd2A1cHHSI5LtcS9Q+Y+qzeH6gckPRxKhoQgiIpIUZpYO3ADc4u6FqY6nvpnZ4+5+eqrjkO1jZhvdvU2ldQZsjDe0RHZumu5LRESSwt1LgAuAramOpSEoqW20FsUMJykznODucdLIaCiCiIgk0+PAecB9qQ6kPpjZMqoZMxzL3XslIRzZMTcSzK38MLAQ6A+cGS7SyGgogoiIJI2ZvQfsD6wguHVp+YdQY7x4zMwOqUs9d3+noWORHWdm+wFnAT0J3pcPu/vHqY1KdoQSWxERSRozO6O6Mnd/LJmxiEj0KLEVERGpB2b2++rK3P36ZMYi28fMhhLcqKEjMdO16bw1Prp4TEREksbM/mxmP6607sdmdneKQqpPPSst+wK/IRizKTspM5sAvA8cBlwF7AlcDgxIZVyyY9RjKyIiSWNm3wHd3b0oZl0GsMzdd0ldZA3DzI4Cxrl7tUMwJLXMbAFwprv/x8zWu3s7M/spcJLOW+OjxFZERJLGzL4luKNTQcy6bOAbd++YusgahpmlAesrz5MqO4/YWyGb2Vqgk7uX6lbIjZOm+xIRkWT6D3CzmV0ZJg9pBNMt/Se1YSXOzPpVWpUNnExwlb3svJabWR93XwLMB443s++Bopo3k52RElsREUmmS4BXgFVmthToBawCjk1pVPVjAcH0ZWUXH+UBXwD6Onvn9kdgMLAE+D0wDWgB/CqFMckO0lAEERFJqrCXdj+2zRn6P3cvTW1UIgEzawG0cPctqY5Ftp8SWxERSRkzOxQojeINDKLctqgxs7bA0UA3YCXwqrtvSGVMsmM03ZeIiCSNmb1jZgeGj68CpgBPm9k1qY0scVFuW5SZ2WEEwxB+RTBF28XAEjM7PJVxyY5Rj62IiCRNeNX5Lu5eEk6zdBywGXjf3XulNrrERLltUWZms4Eb3X1qzLoxwE3uvlvqIpMdoR5bERFJpjTAzaw/QefKbHdfBrRLcVz1Icpti7JuwHOV1r0AdElBLJIgzYogIiLJ9B7wV6ArQfJAmAh+n8qg6kmU2xZlTwAXAn+OWXc+8HhqwpFEaCiCiIgkjZl1ILhd6VbgDnffYmZHA7u6+90pDS5BUW5b1JjZfwimZoOgp31/YA2wAugOdAY+dPefpCZC2VFKbEVERKRJMbM6zS3s7o81dCxSv5TYiohI0phZBnA9MA7o4O5tzGwkMNDd/5ra6BITzn86HhgK5MSWufvpKQhJpMnRGFsREUmmuwi+6j0FeD1cNytc36gTW+AxYC/gHwRfa8tOysxOc/cnwsdnVVfP3ScnLyqpD+qxFRGRpDGzVcAAd881s3Xu3j5cv8Hd26Y2usSY2Xqgryb23/mZ2WvuPip8/HY11dzdD0tiWFIP1GMrIiLJVESlzx4z6wSsTU049eobICPVQUjtypLa8PGhqYxF6pfmsRURkWR6FnjMzPoCmFlXgiEIU1IaVf14HHjJzMaZ2WGxS6oDk+qZ2efVrP8k2bFI4jQUQUREkia8wOoPwDlANpAH/A24yt2LUhlbosxscTVF7u79khqM1JmZbXb3VpXWGbC2bKiMNB5KbEVEJCnMLB24AbjF3QvDIQjfuz6IJAXMrOwGDL8A/l6puA9BjqR5bBsZjbEVEZGkcPcSM7sAuDF8/l1qI6p/ZtYM+DHBzA/LgQ/cvTi1UUk1Flbz2IH3CYbNSCOjHlsREUkaM7sTWODu96U6lvpmZrsRTPWVBSwDegIFwLHuPieVsUn1zOxId/9XquOQ+qHEVkREksbM3iO4fekKguSv/EPI3Q9OVVz1wczeIpib909lwyvM7DfA0bryfudmZoMI5iCufGMNzWPbyCixFRGRpKnpVqaN/falZrYO6OTuJTHrmgHfuXu71EUmNTGzawjuhjeT4GLGMprHthHSGFsREUmaxp681mIlcAjwVsy6n4TrZef1a2A/d/+/VAciiVNiKyIiSRXewnQc0I0g6ZsCTI7A7AjXAC+b2SvAUqA3cDRwakqjktrkA3NTHYTUDw1FEBGRpDGzPwLHA3cTJH+9gEuAf7j7lSkMrV6Y2UBgLNuS9qnuPj+1UUlNzOx04ECC2TrWxJa5e2kqYpIdp8RWRESSxsy+BfZx9+Ux63oCn7l7p9RFljgzywBK3X1rzLrmQJq7F6YuMqmJmZUlr7EJkRGMsU1PQUiSAN1SV0REkmlzuFRetykFsdS3N4EfVlr3Q0BTSe3c+oZLv5il7Lk0MuqxFRGRpDGzi4HRwO0ENzDoCVwBvAS8VlbP3RelIr5EmNl6oH3sWGEzSyO4NatmRRBJAiW2IiKSNDFf+9akUX4FbGZLgB+5++qYdV2Bj929R8oCk1qZ2XEEM1p0JBiGAIC7n56yoGSHaCiCiIgkjbun1WFpdElt6DngaTPbw8yyzWxP4HFgaorjkhqY2Q3AgwQ50RhgLXAksCGFYckOUo+tiIhIPTCzTGAicCaQQXA73cnAb3Tx2M7LzJYS3B3uKzPb4O5tzWw/4Dp3Py7V8cn2UWIrIiJSj8zMCL7S/j4Cc/NGnpltdPc24eNvge7uvjV2vTQeukGDiIhIPQqT2e9SHYfU2UIz293dZwFfAeeHFwKuT3FcsgOU2IqIiEhTdh3QIXz8W+BpIAe4IGURyQ7TUAQRERERiQT12IqISIMys2VUvKtTXO7eKwnhiEiEKbEVEZGGdmqqA2hIZjYIeATYA/gMONPdF6c2KpGmSUMRREREEmBmbwDfEozNPAVo6e6jUxqUSBOlxFZERJLKzIYCP6HqXZ6uT1VMiTCz74Ee7l5gZjnAfHfvluq4pG7MrEvs3eJqWy87N915TEREksbMJgDvA4cBVwF7ApcDA1IZV4JauHsBgLtvATJTHI9sn/nVrJ+d1CikXmiMrYiIJNOVwFHu/h8zW+/uJ5jZT4GTUh1YAjLM7Pcxz7MqPW+0vdFNhFVZYdYaKE1BLJIgDUUQEZGkMbNN7t46fLwW6OTupWa2zt3bpzi8HWJmj9RWx93PTEYsUncxs3V0A1ZWKu4APOPuv0x6YJIQ9diKiEgyLTezPu6+hOAr4OPDMapFqQ1rxylpbbROJeitfQ04LWa9A2vcfV5KopKEqMdWRESSxszGEyQNr4dDEKYBLYBfufv9KQ0uAWbW3N23ho8PouI1LP919+LURCa1MbNsd89LdRxSP5TYiohIyphZC4KLr7akOpYdZWbnAz9299PC53nA2rA4G7jS3R9OVXxSs/A9eB0wjm3DEqYAt5RdFCiNhxJbERFpUGZmHn7YmFm1s/G4e6O8WMfMPgDOc/eZ4fP17t4ufDwUuN/dD0hhiFIDM3sYGATcAiwFegPXAF+7+1mpjE22nxJbERFpUJUuGCul6u11DXB3T096cPXAzFa7e5eY5++7+4HhYwNWu3vnlAUoNQovYuzv7hti1rUHFjTWCxqbMl08JiIiDW33mMd9UxZFw8kxs5bungtQltSGWoaL7LxWEwwZ2RCzLgtYlZJoJCFKbEVEpEG5+zIAM0sHHgOOdPfC1EZVr74CRgIvxCn7/+3da7CdZXnG8f8VCHKIHKJEICQZQ50qTqpoG5ACOjIjSFscqWhxABlbFKQtM8DYwVaoUFspVm2lLWkdxwINMhZpAQX5gEWx9CQKM8iIUQ6hTcIhJG0R5JC7H9bacROIk7129vuw9vv/zWTy5nnXh+vDnsm9n3U/93MUcFe3cTRFlwM3Jvks8CCwCDgDuCzJWyc+VFU3N8qnKbAVQZLUmST3A6+uqidaZ9lekvwG8GngdODa4VzeOcA7gL8CzqqqK1tm1NYluXcbPlZVtXTGw2jaLGwlSZ1J8n7gCOB8Brtjm/8TGtfDYwBJzgY+xmB02SPAy4GfABdU1cUts0l9YmErSerM8PAYPPcA2VgfHpswvIb1TQyK2keB26pqY9tU2hZJ5gKHAPtV1VVJdgOY6JvW+LCwlSR1JsmSrb2rqvu7zCIBJFkGXMtgh33/qpqX5BjgfVX1nrbpNFUWtpKkzg17UF9RVZ48V1NJbgVWVNXlEzOIhzu291TVwtb5NDVbHZQtSdL2lmTPJCuBJ4FVw7Vjk/xR22TqsdcCVwyfCza3IOzSLJFGZmErSerSpcBGBrc7PTVcuw3wK1+1ch/wxskLSZYz/MVL48U5tpKkLh3J4IDO00kmdsceTrKgcS7110eBryS5FNgpybnAacCpbWNpFO7YSpK6tJHB1IDNkizGW57USFVdDxwN7A3cwuDbhOOq6qamwTQSd2wlSV36HHB1kt8H5iR5E/DHDFoUpCaq6jvAh1rn0PS5YytJ6tJFwFXAXwJzgc8D/wT8ectQ6q8kX05y+BZrhyf5h1aZNDrHfUmSOpNkn6pau63r0kxL8iiwoKqenbS2I7Cuql7WLplG4Y6tJKlL92xl/XudppB+6klgty3W5gFPN8iiabKwlSR1Kc9bGFxFu+kFPit14WvAiuHP4cTP4yXAjU1TaSQeHpMkzbgkqxkMv98lyQNbvH4ZcGX3qSQAzmZwQcP6JOuB+cANwMlNU2kk9thKkmZckjcz2K39KvD2Sa+KQS/j95sEk4aS7AMsAlbb7z2+LGwlSZ1JsmtV/bh1DmlCkj8APl6TCqIkuwKfrqoPtkumUdhjK0nq0hWOVtKLzNHAt5IsBUhyKHAnsHvTVBqJO7aSpM44WkkvNknmAOcCZzFolTkKOLOq7PseQ+7YSpK65GglvahU1SbgauBh4F3APzO4NERjyMJWktQlRyvpRSXJbwPfAlYA+zM40HhHkkOaBtNIbEWQJHUmyV4MRisdBUwerXRSVW1oGE09leQ7wIlVddektZOAT1XV3u2SaRQWtpKkziXZl8HumKOV1FSSuVX1vFaYJIuqanWLTBqdFzRIkjpXVWuSrAUyPLwz0esode2ZJKcCJwAvr6pfSHIEsA9gYTtm7LGVJHUmyX5JrhlOR3iGwaGxiT9SCxcAvwn8DbB4uPYg8HvNEmlkFraSpC6tAJ4CjgT+D3gDcC1wWstQ6rVTgF+tqi8yODgGcC+wtFkijcweW0lSZ4Y7tYur6vEkG6pqzyTzgX+pqle3zqf+SfLfwNKqejLJ+qqan+SlwPeqalHrfJoad2wlSV16lkELAsCGJHsDjwML20VSz30V+FSSl8Cg6Ru4ELiuaSqNxMJWktSlfwOOGT5/DbgK+DLwn80Sqe/OAvYFNgJ7MGiRWYI9tmPJVgRJUmeS7AnMqar1SXYBzmFw89hnqmpN03DqtSQLGBS0jqAbYxa2kiRJmhVsRZAkdSbJTkkuSPKDJI8P/74wyc6ts0kaf17QIEnq0l8DPw/8LnA/g69+P8Lg8Nj7G+aSNAvYiiBJ6sxw3NcBVbVh0tp8YFVVzW8WTNKsYCuCJKlLa4Fdt1jbBfDgmJpIcmCSVwyf5yX5WJLzk2z5c6ox4I6tJGlGJXnrpH8uB94LfJbBtaWLgDOAlVV1UYN46rkkdwDvrqrvJ7mUQavMk8AjVXVS23SaKgtbSdKMSnLvNnysqsorTNW5JBurao/hxQzrgAOBJ4B7q2pB23SaKg+PSZJmVFW9snUG6Wd4cniF7oHAA1X1SJIdASd1jCELW0mS1GcrgZuBlwKXDNfeAGzLNw16kbEVQZI0o5LcXVWvGT6vBl7wP56qWtxpMGkoyduAp6vq68N//yKwe1Xd3DaZpsrCVpI0o5IcVlW3Dp/fvLXPVdUt3aWSnivJImBhVf1r6ywana0IkqQZNVHUDi2oqi9t+Zkk7+owkrRZksXAlcDrGXybMG/483h0Vf1Wy2yaOndsJUmdSfI/VbX7C6yv94IGtZDkBuCbwCeAR6tqryR7AHdW1ZK26TRV7thKkmZckolRXnOSvBLIpNdLGcwNlVpYDvxKVW1KUgBVtXFY3GrMWNhKkrqwisHXvAF+uMW7tcAfdh1IGloH/Bxwz8RCkgOBB5ol0sgsbCVJM66q5gAkuaWqtnqATGrgk8D1Sf4E2DHJCcBHGLQmaMzYYytJknotyTuADwJLgNXApVX1j01DaSQWtpKkzgz7az/O4AT6vMnvnGMrabpsRZAkdWklgx7bs4EfN84iAZsvaHg9z/9l67wmgTQyC1tJUpdeC/xyVW1qHUQCSHIJ8G7g6zz3ly2/0h5DFraSpC59AzgI+HbrINLQe4HXVdXq1kE0fRa2kqQu3QfcmOQaBmO+NvNrXzXyCLChdQhtHxa2kqQu7QZcD8wFFjXOIgH8GfD3w3Ff6ya/qKoftYmkUTkVQZLUXJI59t2qhSRb+7mrqtqh0zCatjmtA0iS+ivJsiQXAw+2zqJ+qqo5W/ljUTuGLGwlSZ1KsneSM5PcDnwXWA6c2TaVpNnAHltJ0oxLMhc4FjgFOApYBVzJ4Kan46vqoXbp1DdJbqyqo4fP32Qro72q6ohOg2naLGwlSV1YB2wCvgCcX1W3AyT5UMtQ6q3LJj1/rlkKbXcWtpKkLtwJHAYcDPwgyb1V9VjjTOqpqlo56fnvWmbR9mWPrSRpxlXVW4ADgJuAc4C1Sa5jMP5rbsNo6rkkf5Hk0C3WDk3ymUaRNA2O+5IkdS7JYcDJDK4yfQb4fFV9uG0q9VGSh4GFVfXUpLWXAKurakG7ZBqFO7aSpM5V1a1V9QFgH+B3gGWNI6m/iufXQzu8wJrGgDu2kiSpt5JcDdwLfLiqNiWZA3wCeFVVvbNtOk2Vha0kSeqtJPszuOZ5X+B+YDGwBvi1qvLikDFjYStJknptuEt7MLA/sBr4d694Hk8WtpIkSZoVnGMrSZJ6JcndVfWa4fNqtn7z2OJOg2naLGwlSVLfnDrp+cRmKbTd2YogSZKkWcEdW0mS1CtJLtiWz1XVeTOdRduXha0kSeqbRZOedwZ+HfgPfjruazlwdYNcmiZbESRJUm8l+SLwpaq6etLaccDxVXVCu2QahYWtJEnqrSQbgflV9eyktR2BR6tqj3bJNArvQZYkSX22Cjhji7XTgR82yKJpcsdWkiT1VpKDgGsYnDv6L2Ah8AxwXFXd3jKbps7CVpIk9VqSucAhwH7AGuC2qnq6bSqNwlYESZKkoar6BrBTkt1aZ9HUuWMrSZJ6K8ky4FrgJ8D+VTUvyTHA+6rqPW3TaaosbCVJUm8luRVYUVWXJ3msqvYa7tbeU1ULW+fT1FjYSpKk3kryGINxX5VkfVXNH65vftb4sMdWkiT12X3AGycvJFnOYAyYxoxX6kqSpD77KPCVJJcyODR2LnAacGrbWBqFrQiSJKnXhrNsTwWWAKuBv62qb7dNpVFY2EqSJGlWsBVBkiT1VpIdgROAg4B5k99V1QeahNLILGwlSVKfXQEsA24A1jXOommyFUGSJPVWkg3Aoqr639ZZNH2O+5IkSX12F+C82lnCHVtJktRbSZYCK4Cb2KIVoaouaxJKI7PHVpIk9dkpwOHAXsATk9YLsLAdM+7YSpKk3kqyETikqu5unUXTZ4+tJEnqs3XAA61DaPtwx1aSJPVWktOBtwEXAQ9NfldVP2oSSiOzsJUkSb2VZNNWXlVV7dBpGE2bha0kSZJmBXtsJUmSNCtY2EqSJGlWsLCVJEnSrGBhK0mSpFnBwlaSJEmzglfqSpKkXkmymsGVuT9TVS3uII62IwtbSZLUNye2DqCZ4RxbSZIkzQru2EqSpF5JcsG2fK6qzpvpLNq+LGwlSVLfLGodQDPDVgRJkqQtJJlTVZta59DUOO5LkiRpKMmyJBcDD7bOoqmzsJUkSb2WZO8kZya5HfgusBw4s20qjcIeW0mS1DtJ5gLHAqcARwGrgCuBJcDxVfVQu3QalT22kiSpd5KsBzYBXwBWVtXtw/U1wOssbMeTrQiSJKmP7gT2BA4GfinJXm3jaHuwsJUkSb1TVW8BDgBuAs4B1ia5DtgNmNswmqbBwlaSJPVSVd1fVRdW1auAI4E1DNoT7kjyp23TaRT22EqSJA0l2Rl4J3ByVb29dR5NjYWtJEmSZgVbESRJkjQrWNhKkiRpVrCwlSRJ0qxgYStJkqRZ4f8BNv1IBQxD/rMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(data[['age', 'bmi', 'CCI', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs']].corr(), \n",
    "            annot=True, fmt=\".2f\", annot_kws={\"size\":12},\n",
    "            vmin=-1.0, vmax=1.0)\n",
    "\n",
    "ax.set_xticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=90, fontsize=12)\n",
    "ax.set_yticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=0, fontsize=12)                        \n",
    "plt.tight_layout()\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/corr_urti.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1-3-2- Imputation of missing values\n",
    "We impute variables with missing values that at most 50 pecent of their values are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prep before plot\n",
    "data['age'][data['age'] < 0] = np.nan\n",
    "\n",
    "## remove died cases\n",
    "#data.died_date.value_counts()\n",
    "\n",
    "# remove cases with bmi equal 0\n",
    "data['bmi'][data['bmi'] <= 0] = 0\n",
    "\n",
    "# remove cases with negative gp_count\n",
    "data['gp_count'][data['gp_count'] < 0] = np.nan\n",
    "\n",
    "# remove cases with negative antibacterial_brit\n",
    "data['antibacterial_brit'][data['antibacterial_brit'] < 0] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dict_sex = {'F': 0, 'M': 1}\n",
    "dict_smoking = {'S': 1, 'E': 2, 'N':3, 'M':np.nan}\n",
    "\n",
    "# data = data.replace({\"sex\": dict_sex})\n",
    "data = data.replace({\"smoking_status\": dict_smoking})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(data[['age', 'bmi', 'CCI', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs']].corr(), \n",
    "            annot=True, fmt=\".2f\", annot_kws={\"size\":12},\n",
    "            vmin=-1.0, vmax=1.0)\n",
    "\n",
    "ax.set_xticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=90, fontsize=12)\n",
    "ax.set_yticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=0, fontsize=12)                        \n",
    "plt.tight_layout()\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/corr_urti10.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of missing values in variable bmi: 0\n",
      "Mean of bmi values: 26.241\n",
      "Max of bmi values: 78.844\n",
      "Min of bmi values: 0.000\n",
      "Count of missing values in variable bmi after imputation: 0\n",
      "Mean of bmi values after imputation: 26.241\n",
      "Max of bmi values after imputation: 78.844\n",
      "Min of bmi values after imputation: 0.000\n"
     ]
    }
   ],
   "source": [
    "if ('bmi' in data.columns) and (data['bmi'].isnull().sum() > 0):\n",
    "    # # iterative imputation with sklearn package\n",
    "    imputer = IterativeImputer(max_iter=10, random_state=0, missing_values=0)\n",
    "    # imputer.fit(data[['age', 'sex', 'bmi']])\n",
    "    imputer.fit(data[['age', 'sex', 'CCI', 'bmi']])# 'CCI']])\n",
    "    # data['bmi_imp'] = np.nan\n",
    "    # data['bmi_imp'] = np.round(imputer.transform(data[['age', 'sex', 'bmi']]))[:,2]\n",
    "    data['bmi_imp'] = np.round(imputer.transform(data[['age', 'sex', 'CCI', 'bmi']]))[:,3]\n",
    "elif ('bmi' in data.columns) and (data['bmi'].isnull().sum() == 0):\n",
    "    data['bmi_imp'] = data['bmi']\n",
    "\n",
    "# \n",
    "print(f'Count of missing values in variable bmi: {data.bmi.isnull().sum()}')\n",
    "print(f'Mean of bmi values: {data.bmi.mean():.3f}')\n",
    "print(f'Max of bmi values: {data.bmi.max():.3f}')\n",
    "print(f'Min of bmi values: {data.bmi.min():.3f}')\n",
    "print(f'Count of missing values in variable bmi after imputation: {data.bmi_imp.isnull().sum()}')\n",
    "print(f'Mean of bmi values after imputation: {data.bmi_imp.mean():.3f}')\n",
    "print(f'Max of bmi values after imputation: {data.bmi_imp.max():.3f}')\n",
    "print(f'Min of bmi values after imputation: {data.bmi_imp.min():.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cound of missing values in variable imd: 0\n",
      "Count of categories of variable imd: \n",
      "1    38482\n",
      "4    38199\n",
      "5    38014\n",
      "2    37732\n",
      "3    37698\n",
      "0     9875\n",
      "Name: imd, dtype: int64\n",
      "\n",
      "Cound of missing values in variable imd after imputation: 0\n",
      "Count of categories of variable imd after imputation: \n",
      "1    38482\n",
      "4    38199\n",
      "5    38014\n",
      "2    37732\n",
      "3    37698\n",
      "0     9875\n",
      "Name: imd_imp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "if ('imd' in data.columns) and (data['imd'].isnull().sum() > 0):\n",
    "    data['imd'] = data['imd'].astype(str)\n",
    "    # # iterative imputation with sklearn package\n",
    "    imputer = IterativeImputer(max_iter=10, random_state=0, missing_values=0)\n",
    "    imputer.fit(data[['age', 'sex', 'CCI', 'imd']])#'bmi_imp']])\n",
    "    # data['imd_imp'] = np.nan\n",
    "    data['imd_imp'] = np.round(imputer.transform(data[['age', 'sex', 'CCI', 'imd']]))[:,3]\n",
    "    # data['imd_imp'] = data['imd_imp'].astype(str)\n",
    "elif ('imd' in data.columns) and (data['imd'].isnull().sum() == 0):\n",
    "    data['imd_imp'] = data['imd']\n",
    "    # data['imd_imp'] = data['imd_imp'].astype(str)\n",
    "\n",
    "print(f'Cound of missing values in variable imd: {data.imd.isnull().sum()}')\n",
    "print(f'Count of categories of variable imd: \\n{data.imd.value_counts()}')\n",
    "print(f'\\nCound of missing values in variable imd after imputation: {data.imd_imp.isnull().sum()}')\n",
    "print(f'Count of categories of variable imd after imputation: \\n{data.imd_imp.value_counts()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dict_smoking_status = {'S':'1', 'N':2, 'E':3, 'M':4}\n",
    "# data = data.replace({\"smoking_status\": dict_smoking_status})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cound of missing values in variable smoking_status: 83028\n",
      "Count of categories of variable smoking_status: \n",
      "1.0    77696\n",
      "3.0    26236\n",
      "2.0    13040\n",
      "Name: smoking_status, dtype: int64\n",
      "\n",
      "Cound of missing values in variable smoking_status after imputation: 0\n",
      "Count of categories of variable smoking_status after imputation: \n",
      "2.0    96068\n",
      "1.0    77696\n",
      "3.0    26236\n",
      "Name: smoking_status_imp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# if ('smoking_status' in data.columns) and ((data['smoking_status'].isnull().sum() > 0) or (int(data['smoking_status'].loc[data['smoking_status']=='nan'].agg(['count']))>0)):\n",
    "if ('smoking_status' in data.columns) and (data['smoking_status'].isnull().sum() > 0):\n",
    "    data['smoking_status'] = data['smoking_status'].astype(str)\n",
    "    # data['smoking_status_imp'] = data['smoking_status'].replace(np.nan, 0)\n",
    "    # # iterative imputation with sklearn package\n",
    "    imputer = IterativeImputer(max_iter=10, random_state=0, missing_values=np.nan)\n",
    "    imputer.fit(data[['age', 'sex', 'CCI', 'bmi_imp', 'smoking_status']])#'imd']]) # 'imd_imp'\n",
    "    # data['smoking_status_imp'] = np.nan\n",
    "    data['smoking_status_imp'] = np.round(imputer.transform(data[['age', 'sex', 'CCI', 'bmi_imp', 'smoking_status']]))[:,4]\n",
    "    # data['smoking_status_imp'] = data['smoking_status_imp'].astype(str)\n",
    "elif ('smoking_status' in data.columns) and (data['smoking_status'].isnull().sum() == 0):\n",
    "    data['smoking_status_imp'] = data['smoking_status']\n",
    "    # data['smoking_status_imp'] = data['smoking_status_imp'].astype(str)\n",
    "\n",
    "print(f'Cound of missing values in variable smoking_status: {data.smoking_status.isnull().sum()}')\n",
    "print(f'Count of categories of variable smoking_status: \\n{data.smoking_status.value_counts()}')\n",
    "print(f'\\nCound of missing values in variable smoking_status after imputation: {data.smoking_status_imp.isnull().sum()}')\n",
    "print(f'Count of categories of variable smoking_status after imputation: \\n{data.smoking_status_imp.value_counts()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cound of missing values in variable ethnicity: 0\n",
      "Count of categories of variable ethnicity: \n",
      "1.0    119911\n",
      "nan     50000\n",
      "5.0     15129\n",
      "3.0     14960\n",
      "Name: ethnicity, dtype: int64\n",
      "\n",
      "Cound of missing values in variable ethnicity after imputation: 0\n",
      "Count of categories of variable ethnicity after imputation: \n",
      "1.0    119911\n",
      "2.0     50000\n",
      "5.0     15129\n",
      "3.0     14960\n",
      "Name: ethnicity_imp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# # if ('ethnicity' in data.columns) and ((data['ethnicity'].isnull().sum() > 0) or (int(data['ethnicity'].loc[data['ethnicity']=='nan'].agg(['count']))>0)):\n",
    "# if ('ethnicity' in data.columns) and (data['ethnicity'].isnull().sum() > 0):\n",
    "#     data['ethnicity'] = data['ethnicity'].astype(str)\n",
    "#     # data['ethnicity_imp'] = data['ethnicity'].replace(np.nan, 0)\n",
    "#     # # iterative imputation with sklearn package\n",
    "#     imputer = IterativeImputer(max_iter=10, random_state=0, missing_values=np.nan)\n",
    "#     imputer.fit(data[['age', 'sex', 'CCI', 'bmi_imp', 'imd', 'ethnicity']])\n",
    "#     # data['ethnicity_imp'] = np.nan\n",
    "#     data['ethnicity_imp'] = np.round(imputer.transform(data[['age', 'sex', 'CCI', 'bmi_imp', 'imd', 'ethnicity']]))[:,5] #'imd_imp'\n",
    "#     # data['ethnicity_imp'] = data['ethnicity_imp'].astype(str)\n",
    "# elif ('ethnicity' in data.columns) and (data['ethnicity'].isnull().sum() == 0):\n",
    "#     data['ethnicity_imp'] = data['ethnicity']\n",
    "#     # data['ethnicity_imp'] = data['ethnicity_imp'].astype(str)\n",
    "\n",
    "# print(f'Cound of missing values in variable ethnicity: {data.ethnicity.isnull().sum()}')\n",
    "# print(f'Count of categories of variable ethnicity: \\n{data.ethnicity.value_counts()}')\n",
    "# print(f'\\nCound of missing values in variable ethnicity after imputation: {data.ethnicity_imp.isnull().sum()}')\n",
    "# print(f'Count of categories of variable ethnicity after imputation: \\n{data.ethnicity_imp.value_counts()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cound of missing values in variable gp_count: 0\n",
      "Mean of gp_count values: 3.312\n",
      "\n",
      "Cound of missing values in variable gp_count after imputation: 0\n",
      "Mean of gp_count values after imputation: 3.312\n"
     ]
    }
   ],
   "source": [
    "if ('gp_count' in data.columns) and (data['gp_count'].isnull().sum() > 0):\n",
    "    # # iterative imputation with sklearn package\n",
    "    # imputer = IterativeImputer(max_iter=10, random_state=0, missing_values=np.nan)\n",
    "    # imputer.fit(data[['age', 'sex', 'CCI', 'bmi_imp', 'imd', 'smoking_status_imp', 'gp_count']])# 'ethnicity_imp']]) # 'imd_imp'\n",
    "    # # data['gp_count_imp'] = np.nan\n",
    "    # data['gp_count_imp'] = np.round(imputer.transform(data[['age', 'sex', 'CCI', 'gp_count']]))[:,3] #'imd_imp'\n",
    "    # drop gp_count value missing\n",
    "    data['gp_count_imp'] = data['gp_count']\n",
    "    data = data[data['gp_count_imp'].notna()]\n",
    "elif ('gp_count' in data.columns) and (data['gp_count'].isnull().sum() == 0):\n",
    "    data['gp_count_imp'] = data['gp_count']\n",
    "\n",
    "print(f'Cound of missing values in variable gp_count: {data.gp_count.isnull().sum()}')\n",
    "print(f'Mean of gp_count values: {data.gp_count.mean():.3f}')\n",
    "print(f'\\nCound of missing values in variable gp_count after imputation: {data.gp_count_imp.isnull().sum()}')\n",
    "print(f'Mean of gp_count values after imputation: {data.gp_count_imp.mean():.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cound of missing values in variable antibacterial_brit: 0\n",
      "Mean of antibacterial_brit values: 1.251\n",
      "\n",
      "Cound of missing values in variable antibacterial_brit after imputation: 0\n",
      "Mean of antibacterial_brit values after imputation: 1.251\n"
     ]
    }
   ],
   "source": [
    "if ('antibacterial_brit' in data.columns) and (data['antibacterial_brit'].isnull().sum() > 0):\n",
    "#     # # iterative imputation with sklearn package\n",
    "#     imputer = IterativeImputer(max_iter=10, random_state=0, missing_values=np.nan)\n",
    "#     imputer.fit(data[['age', 'sex', 'CCI', 'bmi_imp', 'imd', 'smoking_status_imp', 'ethnicity_imp', 'antibacterial_brit']])# 'gp_count']]) #'imd_imp'\n",
    "#     # data['antibacterial_brit_imp'] = np.nan\n",
    "#     data['antibacterial_brit_imp'] = np.round(imputer.transform(data[['age', 'sex', 'CCI', 'bmi_imp', 'imd', 'smoking_status_imp', 'ethnicity_imp', 'antibacterial_brit']]))[:,7] # 'imd_imp'\n",
    "    # drop antibacterial_brit value missing\n",
    "    data['antibacterial_brit_imp'] = data['antibacterial_brit']\n",
    "    data = data[data['antibacterial_brit_imp'].notna()]\n",
    "elif ('antibacterial_brit' in data.columns) and (data['antibacterial_brit'].isnull().sum() == 0):\n",
    "    data['antibacterial_brit_imp'] = data['antibacterial_brit']\n",
    "\n",
    "print(f'Cound of missing values in variable antibacterial_brit: {data.antibacterial_brit.isnull().sum()}')\n",
    "print(f'Mean of antibacterial_brit values: {data.antibacterial_brit.mean():.3f}')\n",
    "print(f'\\nCound of missing values in variable antibacterial_brit after imputation: {data.antibacterial_brit_imp.isnull().sum()}')\n",
    "print(f'Mean of antibacterial_brit values after imputation: {data.antibacterial_brit_imp.mean():.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cound of missing values in variable region: 0\n",
      "Count of categories of variable region: \n",
      "London                      40303\n",
      "East                        20118\n",
      "East Midlands               20053\n",
      "South West                  20027\n",
      "South East                  20018\n",
      "West Midlands               19936\n",
      "North West                  19869\n",
      "Yorkshire and The Humber    19853\n",
      "North East                  19823\n",
      "Name: region, dtype: int64\n",
      "\n",
      "Cound of missing values in variable region after imputation: 0\n",
      "Count of categories of variable region after imputation: \n",
      "London                      40303\n",
      "East                        20118\n",
      "East Midlands               20053\n",
      "South West                  20027\n",
      "South East                  20018\n",
      "West Midlands               19936\n",
      "North West                  19869\n",
      "Yorkshire and The Humber    19853\n",
      "North East                  19823\n",
      "Name: region_imp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "if ('region' in data.columns) and (data['region'].isnull().sum() > 0):\n",
    "#     # iterative imputation with sklearn package\n",
    "#     imputer = IterativeImputer(max_iter=10, random_state=0, missing_values=np.nan)\n",
    "#     imputer.fit(data[['age', 'sex', 'CCI', 'bmi_imp', 'imd', 'ethnicity_imp', 'region']])\n",
    "#     # data['region_imp'] = np.nan\n",
    "#     data['region_imp'] = np.round(imputer.transform(data[['age', 'sex', 'CCI', 'bmi_imp', 'imd', 'ethnicity_imp', 'region']]))[:,7] # 'imd_imp'\n",
    "    # drop region value missing\n",
    "    data['region_imp'] = data['region']\n",
    "    data = data[data['region_imp'].notna()]\n",
    "elif ('region' in data.columns) and (data['region'].isnull().sum() == 0):\n",
    "    data['region_imp'] = data['region']\n",
    "\n",
    "print(f'Cound of missing values in variable region: {data.region.isnull().sum()}')\n",
    "print(f'Count of categories of variable region: \\n{data.region.value_counts()}')\n",
    "print(f'\\nCound of missing values in variable region after imputation: {data.region_imp.isnull().sum()}')\n",
    "print(f'Count of categories of variable region after imputation: \\n{data.region_imp.value_counts()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-4- Translating states with dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_sex = {'F':0, 'M':1}\n",
    "data = data.replace({\"sex\": dict_sex})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(data[['age', 'bmi', 'CCI', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs']].corr(), \n",
    "            annot=True, fmt=\".2f\", annot_kws={\"size\":12},\n",
    "            vmin=-1.0, vmax=1.0)\n",
    "\n",
    "ax.set_xticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=90, fontsize=12)\n",
    "ax.set_yticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=0, fontsize=12)                        \n",
    "plt.tight_layout()\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/corr_urti2.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_region = {'East':1, 'East Midlands':2, 'London':3, 'North East':4, 'North West':5, 'South East': 5, 'South West':6, 'Yorkshire and The Humber':7}\n",
    "data = data.replace({\"region_imp\": dict_region})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(data[['age', 'bmi', 'CCI', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs']].corr(), \n",
    "            annot=True, fmt=\".2f\", annot_kws={\"size\":12},\n",
    "            vmin=-1.0, vmax=1.0)\n",
    "\n",
    "ax.set_xticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=90, fontsize=12)\n",
    "ax.set_yticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=0, fontsize=12)                        \n",
    "plt.tight_layout()\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/corr_urti3.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age_cat'] = pd.cut(data['age'], right=False, bins=[0,5,15,25,35,45,55,65,75,130], labels=[1, 2, 3, 4, 5, 6, 7, 8, 9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(data[['age', 'bmi', 'CCI', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs']].corr(), \n",
    "            annot=True, fmt=\".2f\", annot_kws={\"size\":12},\n",
    "            vmin=-1.0, vmax=1.0)\n",
    "\n",
    "ax.set_xticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=90, fontsize=12)\n",
    "ax.set_yticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=0, fontsize=12)                        \n",
    "plt.tight_layout()\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/corr_urti4.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['bmi_imp_cat'] = pd.cut(data['bmi_imp'], right=False, bins=[0,18.5,25,30,10000000000], labels=[1, 2, 3, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(data[['age', 'bmi', 'CCI', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs']].corr(), \n",
    "            annot=True, fmt=\".2f\", annot_kws={\"size\":12},\n",
    "            vmin=-1.0, vmax=1.0)\n",
    "\n",
    "ax.set_xticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=90, fontsize=12)\n",
    "ax.set_yticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=0, fontsize=12)                        \n",
    "plt.tight_layout()\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/corr_urti5.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['gp_count_imp_cat'] = pd.cut(data['gp_count_imp'], right=False, bins=[0,3,8,10000000000], labels=[1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(data[['age', 'bmi', 'CCI', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs']].corr(), \n",
    "            annot=True, fmt=\".2f\", annot_kws={\"size\":12},\n",
    "            vmin=-1.0, vmax=1.0)\n",
    "\n",
    "ax.set_xticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=90, fontsize=12)\n",
    "ax.set_yticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=0, fontsize=12)                        \n",
    "plt.tight_layout()\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/corr_urti6.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['CCI_cat'] = pd.cut(data['CCI'], right=False, bins=[0,1,3,5,8,10000000000], labels=[1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(data[['age', 'bmi', 'CCI', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs']].corr(), \n",
    "            annot=True, fmt=\".2f\", annot_kws={\"size\":12},\n",
    "            vmin=-1.0, vmax=1.0)\n",
    "\n",
    "ax.set_xticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=90, fontsize=12)\n",
    "ax.set_yticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=0, fontsize=12)                        \n",
    "plt.tight_layout()\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/corr_urti7.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['antibacterial_brit_cat'] = pd.cut(data['antibacterial_brit_imp'], right=False, bins=[0,2,6,10000000000], labels=[1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(data[['age', 'bmi', 'CCI', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs']].corr(), \n",
    "            annot=True, fmt=\".2f\", annot_kws={\"size\":12},\n",
    "            vmin=-1.0, vmax=1.0)\n",
    "\n",
    "ax.set_xticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=90, fontsize=12)\n",
    "ax.set_yticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=0, fontsize=12)                        \n",
    "plt.tight_layout()\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/corr_urti8.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['meds_nonabs_cat'] = pd.cut(data['meds_nonabs'], right=False, bins=[0,2,6,10000000000], labels=[1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(data[['age', 'bmi', 'CCI', 'antibacterial_brit', 'gp_count', 'imd', 'meds_nonabs']].corr(), \n",
    "            annot=True, fmt=\".2f\", annot_kws={\"size\":12},\n",
    "            vmin=-1.0, vmax=1.0)\n",
    "\n",
    "ax.set_xticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=90, fontsize=12)\n",
    "ax.set_yticklabels(['Age', 'BMI', 'CCI', 'Antibacterial prescriptions', 'GP consultations' , 'IMD', 'All medicines except antibacterials'], rotation=0, fontsize=12)                        \n",
    "plt.tight_layout()\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/corr_urti9.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # formula=\"age_cat + sex + CCI_cat + gp_count_imp_cat + flu_vaccine + bmi_imp_cat + region_imp + imd_imp + ethnicity_imp + smoking_status_imp + season\"# + antibacterial_brit_imp + meds_nonabs\" # season +\n",
    "\n",
    "# dict_sex = {'F':0, 'M':1}\n",
    "# data = data.replace({\"sex\": dict_sex})\n",
    "\n",
    "# dict_region = {'East':1, 'East Midlands':2, 'London':3, 'North East':4, 'North West':5, 'South East': 5, 'South West':6, 'Yorkshire and The Humber':7}\n",
    "# data = data.replace({\"region_imp\": dict_region})\n",
    "\n",
    "# data['Age_cat'] = pd.cut(data['age'], right=False, bins=[0,5,15,25,35,45,55,65,75,130], labels=[1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
    "# data['bmi_imp_cat'] = pd.cut(data['bmi_imp'], right=False, bins=[0,18.5,25,30,10000000000], labels=[1, 2, 3, 4])\n",
    "# data['gp_count_imp_cat'] = pd.cut(data['gp_count_imp'], right=False, bins=[0,3,8,10000000000], labels=[1, 2, 3])\n",
    "# data['CCI_cat'] = pd.cut(data['CCI'], right=False, bins=[0,1,3,5,8,10000000000], labels=[1, 2, 3, 4, 5])\n",
    "# data['antibacterial_brit_cat'] = pd.cut(data['antibacterial_brit_imp'], right=False, bins=[0,2,6,10000000000], labels=[1, 2, 3])\n",
    "# data['meds_nonabs_cat'] = pd.cut(data['meds_nonabs'], right=False, bins=[0,2,6,10000000000], labels=[1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data[['Age_cat', 'sex', 'CCI_cat', 'region_imp', 'bmi_imp_cat', 'imd_imp', 'smoking_status_imp', 'ethnicity_imp', 'smoking_status_imp', 'gp_count_imp_cat', 'antibacterial_brit_cat', 'meds_nonabs_cat']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # define dictionaries\n",
    "# dict_sex = {0: 'Female', 1: 'Male'}\n",
    "# # dict_bmi_imp_cat = {'Underweight': 'Underweight', 'Healthy weight': 'Healthy weight', 'Overweight': 'Overweight', 'Obese': 'Obese', \n",
    "# #                 np.nan: 'Unknown'}\n",
    "# # dict_imd = {0: 'Unknown', 1: '1 (most deprived)', 2: '2', 3: '3', 4: '4', 5: '5 (least deprived)', np.nan: 'Unknown'}\n",
    "# # dict_ethnicity = {1: 'White', 2: 'Mixed', 3: 'Asian', 4: 'Black', 5: 'Other', 0: 'Unknown', np.nan: 'Unknown'}\n",
    "# dict_ethnicity_imp = {1: 'White', 2: 'Mixed', 3: 'Asian', 4: 'Black', 5: 'Other', 0: 'Unknown'}\n",
    "# # dict_smoking_imp = {1:'Smoker', 2:'Ex-smoker', 3:'Never smoked'}\n",
    "# dict_smoking_imp = {1:'Smoker', 2:'Ex-smoker', 3:'Never smoked', 0: 'Unknown'}\n",
    "# # dict_smoking = {'E': 'Ex-smoker', 'M': 'Unknown', 'S': 'Smoker', 'N': 'Never smoked'}\n",
    "# # dict_smoking_status= {'Smoker': 'Smoker', 'Never smoked': 'Never smoked', 'Ex-smoker': 'Ex-smoker', 'Unknown': 'Unknown', \n",
    "# #                        np.nan: 'Unknown'}\n",
    "# # dict_gp_count_imp_cat = {'Few': 'Few', 'Some': 'Some', 'Many': 'Many', 'Too many': 'Too many', np.nan: 'Unknown'}\n",
    "# dict_flu = {0: 'No', 1: 'Yes'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # reoplace values of dictionaries with existing ones\n",
    "# data = data.replace({\"sex\": dict_sex})\n",
    "# # data = data.replace({\"bmi_imp_cat\": dict_bmi_imp_cat})\n",
    "# # data = data.replace({\"imd\": dict_imd_imp})\n",
    "# data = data.replace({\"ethnicity_imp\": dict_ethnicity_imp})\n",
    "# data = data.replace({\"smoking_status_imp\": dict_smoking_imp})\n",
    "# # data = data.replace({\"smoking_status\": dict_smoking_status})\n",
    "# # data = data.replace({\"gp_count_imp_cat\": dict_gp_count_imp_cat})\n",
    "# data = data.replace({\"flu_vaccine\": dict_flu})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # categorising\n",
    "# data['bmi_imp_cat'] = pd.cut(data['bmi_imp'], right=False, bins=[0,18.5,25,30,10000000000], labels=['Underweight', 'Healthy weight', 'Overweight', 'Obese'])\n",
    "# data['gp_count_imp_cat'] = pd.cut(data['gp_count_imp'], right=False, bins=[0,3,8,10000000000], labels=['Few', 'Some', 'Many'])\n",
    "# data['CCI_cat'] = pd.cut(data['CCI'], right=False, bins=[0,1,3,5,8,10000000000], labels=['No', 'Low', 'Moderate', 'High', 'Very high'])\n",
    "# data['Age_cat'] = pd.cut(data['age'], right=False, bins=[0,18,60,10000000000], labels=['0-17 years', '18-59 years', '>59 years'])\n",
    "\n",
    "# data['antibacterial_brit_cat'] = pd.cut(data['antibacterial_brit_imp'], right=False, bins=[0,2,6,10000000000], labels=['Few', 'Some', 'Many'])\n",
    "# data['meds_nonabs_cat'] = pd.cut(data['meds_nonabs'], right=False, bins=[0,2,6,10000000000], labels=['Few', 'Some', 'Many'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-5- Polynimial featuring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2- Plots\n",
    "### 2-1- Continuous factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, axes = plt.subplots(3,2, \n",
    "#                         figsize=(15, 15)\n",
    "#                         )\n",
    "# create_histplot('BMI', data['bmi'], axes[0,0])\n",
    "# create_histplot('Age', data['age'], axes[0,1])\n",
    "# create_histplot('Count of GP consultation', data['gp_count'], axes[1,0])\n",
    "# create_histplot('Practice', data['practice'], axes[1,1])\n",
    "# create_histplot('CCI', data['CCI'], axes[2,0])\n",
    "# fig.delaxes(ax=axes[2,1])\n",
    "\n",
    "# fig.suptitle('Risk Factors', fontsize=16)\n",
    "# fig.tight_layout(rect=[0, 0.03, 1, 0.98])\n",
    "# #plt.tight_layout()\n",
    "# plt.savefig('../output/hospitalisation_prediction_urti/risk_factors_cont.jpg')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# p = sns.pairplot(data, vars=['age', 'bmi', 'gp_count', 'CCI'#, 'practice'\n",
    "#                              ], \n",
    "#                 hue = 'admitted', # 'antibacterial_brit', \n",
    "# #                kind='reg', \n",
    "#                 #markers=[\"o\", \"s\"], \n",
    "#                 diag_kind=\"kde\",#\"hist\"\n",
    "# #                plot_kws={#'line_kws':{'color':'red'}, \n",
    "#                 plot_kws={'alpha':0.3},\n",
    "#                 height = 3)\n",
    "\n",
    "# p.axes[0,2].set_xlim((0,20))\n",
    "# p.axes[0,2].set_ylim((0,100))\n",
    "\n",
    "# p.axes[1,1].set_xlim((0,75))\n",
    "# p.axes[1,1].set_ylim((0,75))\n",
    "\n",
    "# p.axes[2,0].set_xlim((0,100))\n",
    "# p.axes[2,0].set_ylim((0,20))\n",
    "\n",
    "# p.axes[0,3].set_xlim((0,20))\n",
    "# p.axes[3,0].set_ylim((0,20))\n",
    "\n",
    "# plt.suptitle('Pairplot of continuous factors', fontsize=16, y=1.05)\n",
    "# plt.savefig('../output/hospitalisation_prediction_urti/risk_factors_cont_pairplot.jpg')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-2- Categorical factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, axes = plt.subplots(7,2,\n",
    "#                         figsize=(15, 10)\n",
    "#                         )\n",
    "# create_lineplot(data, 'sex', 'Sex', 'Sex', axes[0,0])\n",
    "# create_lineplot(data, 'age_cat', 'Age category', 'Age category', axes[0,1])\n",
    "# create_lineplot(data, 'imd_imp', 'IMD', 'IMD', axes[1,0])\n",
    "# create_lineplot(data, 'ethnicity_imp', 'Ethnicity', 'Ethnicity', axes[1,1])\n",
    "# create_lineplot(data, 'smoking_status_imp', 'Smoking status', 'Smoking status', axes[2,0])\n",
    "# create_lineplot(data, 'bmi_imp_cat', 'BMI', 'BMI', axes[2,1])\n",
    "# create_lineplot(data, 'CCI_cat', 'CCI', 'CCI', axes[1,0])\n",
    "# create_lineplot(data, 'gp_count_imp_cat', 'GP consultation', 'GP consultation', axes[3,1])\n",
    "# create_lineplot(data, 'region', 'Region', 'Region', axes[4,0])\n",
    "# create_lineplot(data, 'flu_vaccine', 'Flu vaccine', 'Flu vaccine', axes[4,1])\n",
    "# create_lineplot(data, 'antibacterial_brit_imp', 'Antibacterial prescriptions', 'Antibacterial prescriptions', axes[5,0])\n",
    "# create_lineplot(data, 'msoa', 'msoa', 'MSOA', axes[5,1])\n",
    "# create_lineplot(data, 'meds_nonabs', 'Medicines excpet antibacterials', 'Medicines excpet antibacterials', axes[6,0])\n",
    "# fig.delaxes(ax=axes[6,1])\n",
    "\n",
    "# fig.suptitle('Risk Factors', fontsize=16)\n",
    "# fig.tight_layout(rect=[0, 0.03, 1, 0.98])\n",
    "# #plt.tight_layout()\n",
    "# plt.savefig('../output/hospitalisation_prediction_urti/risk_factors_cat.jpg')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3- Data preparation\n",
    "This includes data type conversion and exclusion of cases that are not considered in the infection-related hospital admission, i.e. those who were diagnosed with Covid19 three months (=90 days) before and one month (=30 days) after hospital admission.\n",
    "\n",
    "### 3-1- URTI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### URTI-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19859, 31)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_1 = data[['Age_cat', 'msoa', #'date',\n",
    "                    'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat', 'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine', \n",
    "                    'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp', 'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
    "                    'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',  #'season', \n",
    "                    'died_date', 'deregistered_date', 'gp_cons_urti_1', 'gp_cons_urti_ab_1',\n",
    "                    'urti_date_1', 'incdt_urti_date_1', 'admitted_urti_date_1', 'sgss_gp_cov_urti_date_1']]\n",
    "\n",
    "data_urti_1 = data_urti_1[data_urti_1['urti_date_1'].notnull()]\n",
    "\n",
    "data_urti_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17875, 31)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exclusion of covid positive 90 days before and 30 days after dx with urti_1\n",
    "data_urti_1 = data_urti_1[data_urti_1['sgss_gp_cov_urti_date_1'] == 0]\n",
    "data_urti_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'msoa', 'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat',\n",
       "       'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine',\n",
       "       'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp',\n",
       "       'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
       "       'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',\n",
       "       'died_date', 'deregistered_date', 'gp_cons_urti_1', 'gp_cons_urti_ab_1',\n",
       "       'urti_date_1', 'incdt_urti_date_1', 'admitted_urti_date_1',\n",
       "       'sgss_gp_cov_urti_date_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1885, 31)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop rows with no gp_cons_urti_1 records\n",
    "data_urti_1 = data_urti_1.dropna(subset=['gp_cons_urti_1'])\n",
    "data_urti_1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### URTI-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19879, 31)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_2 = data[['Age_cat', 'msoa', #'date',\n",
    "                    'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat', 'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine', \n",
    "                    'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp', 'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
    "                    'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',  #'season', \n",
    "                    'died_date', 'deregistered_date', 'gp_cons_urti_2', 'gp_cons_urti_ab_2',\n",
    "                    'urti_date_2', 'incdt_urti_date_2', 'admitted_urti_date_2', 'sgss_gp_cov_urti_date_2']]\n",
    "\n",
    "data_urti_2 = data_urti_2[data_urti_2['urti_date_2'].notnull()]\n",
    "\n",
    "data_urti_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-08-22    8\n",
       "2020-10-18    6\n",
       "2020-06-14    6\n",
       "2019-09-24    6\n",
       "2021-11-22    6\n",
       "             ..\n",
       "2021-11-07    1\n",
       "2020-07-10    1\n",
       "2022-02-07    1\n",
       "2022-04-03    1\n",
       "2019-08-19    1\n",
       "Name: urti_date_1, Length: 960, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_1.urti_date_1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-07-04    31\n",
       "2019-07-22    31\n",
       "2019-10-02    29\n",
       "2020-05-08    29\n",
       "2019-12-22    29\n",
       "              ..\n",
       "2021-01-15     6\n",
       "2019-08-08     5\n",
       "2021-01-14     4\n",
       "2021-09-11     4\n",
       "2019-12-24     4\n",
       "Name: urti_date_2, Length: 1210, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_2.urti_date_2.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17892, 31)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exclusion of covid positive 90 days before and 30 days after dx with urti_2\n",
    "data_urti_2 = data_urti_2[data_urti_2['sgss_gp_cov_urti_date_2'] == 0]\n",
    "data_urti_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'msoa', 'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat',\n",
       "       'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine',\n",
       "       'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp',\n",
       "       'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
       "       'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',\n",
       "       'died_date', 'deregistered_date', 'gp_cons_urti_2', 'gp_cons_urti_ab_2',\n",
       "       'urti_date_2', 'incdt_urti_date_2', 'admitted_urti_date_2',\n",
       "       'sgss_gp_cov_urti_date_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1780, 31)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop rows with no gp_cons_urti_2 records\n",
    "data_urti_2 = data_urti_2.dropna(subset=['gp_cons_urti_2'])\n",
    "data_urti_2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### URTI_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19892, 31)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_3 = data[['Age_cat', 'msoa', #'date',\n",
    "                    'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat', 'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine', \n",
    "                    'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp', 'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
    "                    'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',  #'season', \n",
    "                    'died_date', 'deregistered_date', 'gp_cons_urti_3', 'gp_cons_urti_ab_3',\n",
    "                    'urti_date_3', 'incdt_urti_date_3', 'admitted_urti_date_3', 'sgss_gp_cov_urti_date_3']]\n",
    "\n",
    "data_urti_3 = data_urti_3[data_urti_3['urti_date_3'].notnull()]\n",
    "\n",
    "data_urti_3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17893, 31)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exclusion of covid positive 90 days before and 30 days after dx with urti_3\n",
    "data_urti_3 = data_urti_3[data_urti_3['sgss_gp_cov_urti_date_3'] == 0]\n",
    "data_urti_3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'msoa', 'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat',\n",
       "       'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine',\n",
       "       'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp',\n",
       "       'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
       "       'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',\n",
       "       'died_date', 'deregistered_date', 'gp_cons_urti_3', 'gp_cons_urti_ab_3',\n",
       "       'urti_date_3', 'incdt_urti_date_3', 'admitted_urti_date_3',\n",
       "       'sgss_gp_cov_urti_date_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1745, 31)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop rows with no gp_cons_urti_3 records\n",
    "data_urti_3 = data_urti_3.dropna(subset=['gp_cons_urti_3'])\n",
    "data_urti_3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### URTI_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19873, 31)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_4 = data[['Age_cat', 'msoa', #'date',\n",
    "                    'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat', 'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine', \n",
    "                    'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp', 'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
    "                    'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',  #'season', \n",
    "                    'died_date', 'deregistered_date', 'gp_cons_urti_4', 'gp_cons_urti_ab_4',\n",
    "                    'urti_date_4', 'incdt_urti_date_4', 'admitted_urti_date_4', 'sgss_gp_cov_urti_date_4']]\n",
    "\n",
    "data_urti_4 = data_urti_4[data_urti_4['urti_date_4'].notnull()]\n",
    "\n",
    "data_urti_4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17875, 31)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exclusion of covid positive 90 days before and 30 days after dx with urti_4\n",
    "data_urti_4 = data_urti_4[data_urti_4['sgss_gp_cov_urti_date_4'] == 0]\n",
    "data_urti_4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'msoa', 'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat',\n",
       "       'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine',\n",
       "       'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp',\n",
       "       'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
       "       'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',\n",
       "       'died_date', 'deregistered_date', 'gp_cons_urti_4', 'gp_cons_urti_ab_4',\n",
       "       'urti_date_4', 'incdt_urti_date_4', 'admitted_urti_date_4',\n",
       "       'sgss_gp_cov_urti_date_4'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_4.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17875, 31)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16152"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data_urti_4.gp_cons_urti_4.value_counts()#isnull().sum()\n",
    "data_urti_4.gp_cons_urti_4.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1723, 31)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop rows with no gp_cons_urti_4 records\n",
    "data_urti_4 = data_urti_4.dropna(subset=['gp_cons_urti_4'])\n",
    "data_urti_4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combining URTI_1, URTI_2, URTI_3, and URTI_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7133, 31)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_1.rename(columns={'urti_date_1': 'urti_date',\n",
    "                           'gp_cons_urti_1': 'gp_cons_urti', 'gp_cons_urti_ab_1': 'gp_cons_urti_ab',\n",
    "                           'incdt_urti_date_1': 'incdt_urti_date', #'prevl_urti_date_1': 'prevl_urti_date',\n",
    "                           'admitted_urti_date_1': 'admitted_urti_date',\n",
    "                           'sgss_gp_cov_urti_date_1': 'sgss_gp_cov_urti_date'},\n",
    "                inplace=True)\n",
    "data_urti_2.rename(columns={'urti_date_2': 'urti_date',\n",
    "                           'gp_cons_urti_2': 'gp_cons_urti', 'gp_cons_urti_ab_2': 'gp_cons_urti_ab',\n",
    "                           'incdt_urti_date_2': 'incdt_urti_date', #'prevl_urti_date_2': 'prevl_urti_date', \n",
    "                           'admitted_urti_date_2': 'admitted_urti_date',\n",
    "                           'sgss_gp_cov_urti_date_2': 'sgss_gp_cov_urti_date'},\n",
    "                inplace=True)\n",
    "data_urti_3.rename(columns={'urti_date_3': 'urti_date',\n",
    "                           'gp_cons_urti_3': 'gp_cons_urti', 'gp_cons_urti_ab_3': 'gp_cons_urti_ab',\n",
    "                           'incdt_urti_date_3': 'incdt_urti_date', #'prevl_urti_date_3': 'prevl_urti_date', \n",
    "                           'admitted_urti_date_3': 'admitted_urti_date',\n",
    "                           'sgss_gp_cov_urti_date_3': 'sgss_gp_cov_urti_date'},\n",
    "                 inplace=True)\n",
    "data_urti_4.rename(columns={'urti_date_4': 'urti_date', \n",
    "                           'gp_cons_urti_4': 'gp_cons_urti', 'gp_cons_urti_ab_4': 'gp_cons_urti_ab',\n",
    "                           'incdt_urti_date_4': 'incdt_urti_date', #'prevl_urti_date_4': 'prevl_urti_date',\n",
    "                           'admitted_urti_date_4': 'admitted_urti_date',\n",
    "                           'sgss_gp_cov_urti_date_4': 'sgss_gp_cov_urti_date'}, \n",
    "                 inplace=True)\n",
    "\n",
    "data_urti = pd.concat([data_urti_1, data_urti_2, data_urti_3, data_urti_4])\n",
    "data_urti.reset_index(inplace=True, drop=True)\n",
    "data_urti.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'msoa', 'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat',\n",
       "       'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine',\n",
       "       'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp',\n",
       "       'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
       "       'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',\n",
       "       'died_date', 'deregistered_date', 'gp_cons_urti', 'gp_cons_urti_ab',\n",
       "       'urti_date', 'incdt_urti_date', 'admitted_urti_date',\n",
       "       'sgss_gp_cov_urti_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_4.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7133, 31)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti['urti_date'] = data_urti['urti_date'].astype('datetime64[ns]')\n",
    "data_urti['gp_cons_urti'] = data_urti['gp_cons_urti'].astype('datetime64[ns]')\n",
    "data_urti['admitted_urti_date'] = data_urti['admitted_urti_date'].astype('datetime64[ns]')\n",
    "data_urti.died_date = data_urti.died_date.astype('datetime64[ns]')\n",
    "data_urti.deregistered_date = data_urti.deregistered_date.astype('datetime64[ns]')\n",
    "data_urti.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add a variable called date using gp consultation dates\n",
    "data_urti['date'] = data_urti['gp_cons_urti']\n",
    "data_urti['date'] = data_urti['date'].dt.strftime('%Y-%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spring    2018\n",
       "Winter    1860\n",
       "Summer    1640\n",
       "Autumn    1615\n",
       "Name: season, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add a variable called season based on the month of urti records\n",
    "data_urti['season'] = np.nan\n",
    "data_urti['date_month'] = pd.DatetimeIndex(data_urti['date']).month\n",
    "\n",
    "conditions = [\n",
    "    (data_urti['date_month'] >= 3) & (data_urti['date_month'] <= 5),\n",
    "    (data_urti['date_month'] >= 6) & (data_urti['date_month'] <= 8),\n",
    "    (data_urti['date_month'] >= 9) & (data_urti['date_month'] <= 11),]\n",
    "choices = [1, 2, 3]\n",
    "data_urti['season'] = np.select(conditions, choices, default=4)\n",
    "\n",
    "data_urti['season'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, axes = plt.subplots(6,2,\n",
    "#                         figsize=(20, 40)\n",
    "#                         )\n",
    "# create_lineplot(data_urti, 'sex', 'Sex', axes[0,0])\n",
    "# create_lineplot(data_urti, 'age_cat', 'Age category', axes[0,1])\n",
    "# create_lineplot(data_urti, 'imd_imp', 'IMD', axes[1,0])\n",
    "# create_lineplot(data_urti, 'ethnicity_imp', 'Ethnicity', axes[1,1])\n",
    "# create_lineplot(data_urti, 'smoking_status_imp', 'Smoking status', axes[2,0])\n",
    "# create_lineplot(data_urti, 'bmi_imp_cat', 'BMI', axes[2,1])\n",
    "# create_lineplot(data_urti, 'CCI_cat', 'CCI', axes[3,0])\n",
    "# create_lineplot(data_urti, 'gp_count_imp_cat', 'GP consultation', axes[3,1])\n",
    "# create_lineplot(data_urti, 'region_imp', 'Region', axes[4,0])\n",
    "# create_lineplot(data_urti, 'flu_vaccine', 'Flu vaccine', axes[4,1])\n",
    "# create_lineplot(data_urti, 'antibacterial_brit_cat', 'Antibacterial prescriptions', axes[5,0])\n",
    "# create_lineplot(data_urti, 'meds_nonabs_cat', 'Medicines excpet antibacterials', axes[5,1])\n",
    "# # fig.delaxes(ax=axes[6,1])\n",
    "\n",
    "# fig.suptitle('Risk Factors of URTI patients', fontsize=16)\n",
    "# fig.tight_layout(rect=[0, 0.03, 1, 0.98])\n",
    "# #plt.tight_layout()\n",
    "# plt.savefig('../output/hospitalisation_prediction_urti/risk_factors_cat_urti.jpg')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding event and duration columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    4953\n",
       "Name: event_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scenario 1\n",
    "#not hosped (nothing happened)\n",
    "#data_urti = data_urti[data_urti['admitted_urti_date'].notnull()]\n",
    "data_urti.loc[data_urti['admitted_urti_date'].isnull(), 'event_urti_admitted'] = 0\n",
    "data_urti['event_urti_admitted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    4953\n",
       "1.0      51\n",
       "Name: event_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scenario 2 \n",
    "# become a case (uncensoring)\n",
    "#calculating days between infection gp consultation and hosp\n",
    "# data_urti['delta_urti_admitted'] = (data_urti['admitted_urti_date'] - data_urti['urti_date']).dt.days\n",
    "data_urti['delta_urti_admitted'] = (data_urti['admitted_urti_date'] - data_urti['gp_cons_urti']).dt.days\n",
    "data_urti.loc[((data_urti['delta_urti_admitted'] >= 0) & (data_urti['delta_urti_admitted'] <= 30)), 'event_urti_admitted'] = 1\n",
    "data_urti['event_urti_admitted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "243.0     7\n",
       "100.0     6\n",
       "159.0     6\n",
       "337.0     5\n",
       "40.0      5\n",
       "         ..\n",
       "621.0     1\n",
       "637.0     1\n",
       "442.0     1\n",
       "1034.0    1\n",
       "505.0     1\n",
       "Name: delta_urti_admitted, Length: 642, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scenario 2\n",
    "#drop whoever was admitted before urti consultation, i.e. negative value for delta_urti_admitted\n",
    "data_urti = data_urti[~(data_urti['delta_urti_admitted'] < 0)]\n",
    "data_urti['delta_urti_admitted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1065"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data_urti['event_urti_admitted'].value_counts()\n",
    "data_urti['event_urti_admitted'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    4961\n",
       "1.0      50\n",
       "Name: event_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scenario 3\n",
    "#censor died patients\n",
    "data_urti['delta_admitted_died'] = (data_urti['died_date'] - data_urti['admitted_urti_date']).dt.days\n",
    "data_urti.loc[data_urti['delta_admitted_died'] < 0, 'delta_admitted_died'] = np.NaN\n",
    "# data_urti.loc[data_urti['delta_admitted_died'] >= 0, 'event_urti_admitted'] = 0\n",
    "data_urti.loc[((data_urti['delta_admitted_died'] >= 0) & (data_urti['delta_admitted_died'] <= 30)), 'event_urti_admitted'] = 0\n",
    "data_urti['event_urti_admitted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    4961\n",
       "1.0      50\n",
       "Name: event_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scenario 3\n",
    "#censor deregistered patients\n",
    "data_urti['delta_admitted_deregistered'] = (data_urti['deregistered_date'] - data_urti['admitted_urti_date']).dt.days\n",
    "data_urti.loc[data_urti['delta_admitted_deregistered'] < 0, 'delta_admitted_deregistered'] = np.NaN\n",
    "# data_urti.loc[data_urti['delta_admitted_deregistered'] >= 0, 'event_urti_admitted'] = 0\n",
    "data_urti.loc[((data_urti['delta_admitted_deregistered'] > 0) & (data_urti['delta_admitted_deregistered'] <= 30)), 'event_urti_admitted'] = 0\n",
    "data_urti['event_urti_admitted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5965"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#agg scenario 3s\n",
    "data_urti['delta_admitted_died_deregistered'] = data_urti['delta_admitted_deregistered'].combine_first(data_urti['delta_admitted_died'])\n",
    "data_urti.loc[data_urti['delta_admitted_died_deregistered'] < 0, 'delta_admitted_died_deregistered'] = np.NaN\n",
    "data_urti['delta_admitted_died_deregistered'].isnull().sum()#.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157.0    2\n",
       "34.0     2\n",
       "59.0     2\n",
       "130.0    1\n",
       "234.0    1\n",
       "        ..\n",
       "205.0    1\n",
       "361.0    1\n",
       "113.0    1\n",
       "587.0    1\n",
       "796.0    1\n",
       "Name: delta_admitted_died_deregistered, Length: 101, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti['delta_admitted_died_deregistered'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#censor patients that had a history of hospital admission\n",
    "# data_urti.loc[data_urti['hx_hosp'] == 1, 'event_urti_admitted'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    6019\n",
       "1.0      50\n",
       "Name: event_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scenario 1\n",
    "#any other patients (nothing happened)\n",
    "data_urti['event_urti_admitted'] = data_urti['event_urti_admitted'].replace(np.NaN, 0)\n",
    "data_urti['event_urti_admitted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.0    6018\n",
       "1.0        4\n",
       "26.0       4\n",
       "13.0       4\n",
       "6.0        4\n",
       "14.0       3\n",
       "9.0        3\n",
       "27.0       3\n",
       "17.0       3\n",
       "21.0       3\n",
       "18.0       3\n",
       "8.0        3\n",
       "22.0       2\n",
       "20.0       2\n",
       "7.0        2\n",
       "15.0       1\n",
       "25.0       1\n",
       "4.0        1\n",
       "16.0       1\n",
       "2.0        1\n",
       "19.0       1\n",
       "29.0       1\n",
       "12.0       1\n",
       "Name: duration_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assign values for duration column\n",
    "data_urti['duration_urti_admitted'] = data_urti['delta_urti_admitted'].combine_first(data_urti['delta_admitted_died_deregistered'])\n",
    "data_urti['duration_urti_admitted'] = data_urti['duration_urti_admitted'].replace(np.NaN, 30)\n",
    "data_urti.loc[(data_urti['duration_urti_admitted'] > 30), 'duration_urti_admitted'] = 30\n",
    "data_urti['duration_urti_admitted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    6018\n",
       "1.0      51\n",
       "Name: event_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# give value 1 to event_urti_admitted if duration_urti_admitted is greater or equal to 0 and less than 30\n",
    "data_urti.loc[((data_urti['duration_urti_admitted'] >= 0) & (data_urti['duration_urti_admitted'] < 30)), 'event_urti_admitted'] = 1\n",
    "data_urti['event_urti_admitted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop any rows with value 0 in duration column\n",
    "data_urti = data_urti[~(data_urti['duration_urti_admitted'] == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>delta_urti_admitted</th>\n",
       "      <th>urti_date</th>\n",
       "      <th>gp_cons_urti</th>\n",
       "      <th>admitted_urti_date</th>\n",
       "      <th>delta_urti_admitted</th>\n",
       "      <th>delta_admitted_died_deregistered</th>\n",
       "      <th>event_urti_admitted</th>\n",
       "      <th>duration_urti_admitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [delta_urti_admitted, urti_date, gp_cons_urti, admitted_urti_date, delta_urti_admitted, delta_admitted_died_deregistered, event_urti_admitted, duration_urti_admitted]\n",
       "Index: []"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti[data_urti['duration_urti_admitted']==0][['delta_urti_admitted', 'urti_date', 'gp_cons_urti', 'admitted_urti_date', 'delta_urti_admitted', 'delta_admitted_died_deregistered', 'event_urti_admitted', 'duration_urti_admitted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scenario2 (uncensoring) again to prevent conflict with other scenarios\n",
    "data_urti.loc[((data_urti['delta_urti_admitted'] > 0) & (data_urti['delta_urti_admitted'] < 30)), 'event_urti_admitted'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_urti['duration_urti_admitted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_urti_admitted</th>\n",
       "      <th>duration_urti_admitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5843</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4609</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5702</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2389</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2399</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7132</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6069 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      event_urti_admitted  duration_urti_admitted\n",
       "1674                  1.0                     1.0\n",
       "5843                  1.0                     1.0\n",
       "4609                  1.0                     1.0\n",
       "7042                  1.0                     1.0\n",
       "5702                  1.0                     2.0\n",
       "...                   ...                     ...\n",
       "2391                  0.0                    30.0\n",
       "2390                  0.0                    30.0\n",
       "2389                  0.0                    30.0\n",
       "2399                  0.0                    30.0\n",
       "7132                  0.0                    30.0\n",
       "\n",
       "[6069 rows x 2 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data_urti.loc[151:200, ['event_urti_admitted', 'duration_urti_admitted']]\n",
    "# data_urti.loc[201:250, ['event_urti_admitted', 'duration_urti_admitted']].sort_values(by=['duration_urti_admitted'])\n",
    "data_urti[['event_urti_admitted', 'duration_urti_admitted']].sort_values(by=['duration_urti_admitted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separate data of URTI\n",
    "We separate data of urti to form four sub-datasets for 1) no antibiotics and incident admission, 2) with antibiotics and incident admission, 3) no antibiotics and prevalent admission, and 4) with antiobiotics and prevalent admission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data_urti_no_abs_incdt: (4895, 40)\n",
      "shape of data_urti_abs_incdt: (560, 40)\n",
      "shape of data_urti_no_abs_prevl: (554, 40)\n",
      "shape of data_urti_abs_prevl: (60, 40)\n"
     ]
    }
   ],
   "source": [
    "# no antibiotics and incident hospital admission\n",
    "data_urti_no_abs_incdt = data_urti[data_urti['gp_cons_urti_ab'].isnull()]\n",
    "data_urti_no_abs_incdt = data_urti_no_abs_incdt[data_urti_no_abs_incdt['incdt_urti_date'] == 0]\n",
    "\n",
    "# with antibiotics and incident hospital admission\n",
    "data_urti_abs_incdt = data_urti[data_urti['gp_cons_urti_ab'].notnull()]\n",
    "data_urti_abs_incdt = data_urti_abs_incdt[data_urti_abs_incdt['incdt_urti_date'] == 0]\n",
    "\n",
    "# no antibiotics and prevalent hospital admission\n",
    "data_urti_no_abs_prevl = data_urti[data_urti['gp_cons_urti_ab'].isnull()]\n",
    "data_urti_no_abs_prevl = data_urti_no_abs_prevl[data_urti_no_abs_prevl['incdt_urti_date'] == 1]\n",
    "\n",
    "# with antibiotics and prevalent hospital admission\n",
    "data_urti_abs_prevl = data_urti[data_urti['gp_cons_urti_ab'].notnull()]\n",
    "data_urti_abs_prevl = data_urti_abs_prevl[data_urti_abs_prevl['incdt_urti_date'] == 1]\n",
    "\n",
    "print(f'shape of data_urti_no_abs_incdt: {data_urti_no_abs_incdt.shape}')\n",
    "print(f'shape of data_urti_abs_incdt: {data_urti_abs_incdt.shape}')\n",
    "print(f'shape of data_urti_no_abs_prevl: {data_urti_no_abs_prevl.shape}')\n",
    "print(f'shape of data_urti_abs_prevl: {data_urti_abs_prevl.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4- Cox modelling for hospital admissions related to infections \n",
    "### 4-1- URTI\n",
    "#### 4-1-1- Incident hospital admission related to URTI with no antibiotics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'msoa', 'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat',\n",
       "       'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine',\n",
       "       'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp',\n",
       "       'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
       "       'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',\n",
       "       'died_date', 'deregistered_date', 'gp_cons_urti', 'gp_cons_urti_ab',\n",
       "       'urti_date', 'incdt_urti_date', 'admitted_urti_date',\n",
       "       'sgss_gp_cov_urti_date', 'date', 'season', 'date_month',\n",
       "       'event_urti_admitted', 'delta_urti_admitted', 'delta_admitted_died',\n",
       "       'delta_admitted_deregistered', 'delta_admitted_died_deregistered',\n",
       "       'duration_urti_admitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_no_abs_incdt.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4895, 40)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_no_abs_incdt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep relevant variables\n",
    "data_urti_no_abs_incdt = data_urti_no_abs_incdt[['age_cat', 'sex', 'CCI_cat', 'age', 'bmi_imp', 'bmi_imp_cat', \n",
    "                                                 'gp_count_imp', 'gp_count_imp_cat', 'flu_vaccine', 'region_imp', 'season', \n",
    "                                                 'imd_imp', 'antibacterial_brit_imp', 'ethnicity_imp', 'antibacterial_brit_cat',\n",
    "                                                 'smoking_status_imp', 'meds_nonabs', 'meds_nonabs_cat',\n",
    "                                                 'event_urti_admitted', 'duration_urti_admitted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_cat                   0\n",
       "sex                       0\n",
       "CCI_cat                   0\n",
       "age                       0\n",
       "bmi_imp                   0\n",
       "bmi_imp_cat               0\n",
       "gp_count_imp              0\n",
       "gp_count_imp_cat          0\n",
       "flu_vaccine               0\n",
       "region_imp                0\n",
       "season                    0\n",
       "imd_imp                   0\n",
       "antibacterial_brit_imp    0\n",
       "ethnicity_imp             0\n",
       "antibacterial_brit_cat    0\n",
       "smoking_status_imp        0\n",
       "meds_nonabs               0\n",
       "meds_nonabs_cat           0\n",
       "event_urti_admitted       0\n",
       "duration_urti_admitted    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing number of missing values in each variable \n",
    "data_urti_no_abs_incdt.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #drop ros with missing values\n",
    "# data_urti_no_abs_incdt = data_urti_no_abs_incdt.dropna(axis='rows')\n",
    "# # data_urti = data_urti.drop(columns=['smoking_status'])\n",
    "# data_urti_no_abs_incdt.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age_cat', 'sex', 'CCI_cat', 'age', 'bmi_imp', 'bmi_imp_cat',\n",
       "       'gp_count_imp', 'gp_count_imp_cat', 'flu_vaccine', 'region_imp',\n",
       "       'season', 'imd_imp', 'antibacterial_brit_imp', 'ethnicity_imp',\n",
       "       'antibacterial_brit_cat', 'smoking_status_imp', 'meds_nonabs',\n",
       "       'meds_nonabs_cat', 'event_urti_admitted', 'duration_urti_admitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_no_abs_incdt.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data_urti_no_abs_incdt for training: (3671, 21)\n",
      "shape of data_urti_no_abs_incdt for testing: (1224, 21)\n"
     ]
    }
   ],
   "source": [
    "#randomly splitting data into training (%75) and testing (%25)\n",
    "data_urti_no_abs_incdt_train, data_urti_no_abs_incdt_test = train_test_split(data_urti_no_abs_incdt, test_size=0.25, random_state=42)\n",
    "\n",
    "print(f'shape of data_urti_no_abs_incdt for training: {data_urti_no_abs_incdt_train.shape}')\n",
    "print(f'shape of data_urti_no_abs_incdt for testing: {data_urti_no_abs_incdt_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build LR model\n",
    "lr_urti_no_abs_incdt = LogisticRegression().fit(data_urti_no_abs_incdt_train[['sex', 'Age_cat']], data_urti_no_abs_incdt_train.event_urti_admitted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_urti_no_abs_incdt_test_sex = data_urti_no_abs_incdt_test[['sex', 'Age_cat']]\n",
    "# data_urti_no_abs_incdt_test_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.98958989, 0.01041011],\n",
       "       [0.98870306, 0.01129694],\n",
       "       [0.99396279, 0.00603721],\n",
       "       ...,\n",
       "       [0.99396279, 0.00603721],\n",
       "       [0.99344602, 0.00655398],\n",
       "       [0.99288533, 0.00711467]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_proba_urti_no_abs_incdt_test = lr_urti_no_abs_incdt.predict_proba(data_urti_no_abs_incdt_test_sex)\n",
    "pred_proba_urti_no_abs_incdt_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'duration_urti_admitted'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'event_urti_admitted'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>3671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-232.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2022-05-06 15:22:16 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.Low]</th>\n",
       "      <td>-0.55</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.Moderate]</th>\n",
       "      <td>-0.90</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.61</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.High]</th>\n",
       "      <td>-0.74</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.60</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.Very high]</th>\n",
       "      <td>-0.99</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.68</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.15-24]</th>\n",
       "      <td>0.02</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.65</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>3.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.25-34]</th>\n",
       "      <td>-0.75</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-2.41</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.35-44]</th>\n",
       "      <td>-0.89</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.45-54]</th>\n",
       "      <td>-0.72</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.5-14]</th>\n",
       "      <td>0.20</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.65</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.34</td>\n",
       "      <td>4.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.55-64]</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.68</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.27</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.65-74]</th>\n",
       "      <td>-1.43</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.11</td>\n",
       "      <td>-3.60</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.75+]</th>\n",
       "      <td>0.12</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.65</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.32</td>\n",
       "      <td>4.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi_imp_cat[T.Healthy weight]</th>\n",
       "      <td>0.40</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.38</td>\n",
       "      <td>5.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi_imp_cat[T.Overweight]</th>\n",
       "      <td>1.35</td>\n",
       "      <td>3.86</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.48</td>\n",
       "      <td>10.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi_imp_cat[T.Obese]</th>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnicity_imp[T.Mixed]</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.67</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnicity_imp[T.Other]</th>\n",
       "      <td>0.37</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.34</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnicity_imp[T.White]</th>\n",
       "      <td>-0.69</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.65</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flu_vaccine[T.Yes]</th>\n",
       "      <td>0.19</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.41</td>\n",
       "      <td>3.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gp_count_imp_cat[T.Some]</th>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.39</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gp_count_imp_cat[T.Many]</th>\n",
       "      <td>-0.59</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.64</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imd_imp</th>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.East Midlands]</th>\n",
       "      <td>-1.41</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.13</td>\n",
       "      <td>-3.62</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.London]</th>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.68</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.North East]</th>\n",
       "      <td>0.48</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.66</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>1.77</td>\n",
       "      <td>0.44</td>\n",
       "      <td>5.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.North West]</th>\n",
       "      <td>-0.69</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.88</td>\n",
       "      <td>-2.41</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.South East]</th>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.77</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.19</td>\n",
       "      <td>3.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.South West]</th>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.87</td>\n",
       "      <td>-2.35</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.West Midlands]</th>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.72</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.24</td>\n",
       "      <td>4.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.Yorkshire and The Humber]</th>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.72</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.23</td>\n",
       "      <td>3.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season[T.Spring]</th>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.48</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season[T.Summer]</th>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.48</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season[T.Winter]</th>\n",
       "      <td>-0.89</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>0.11</td>\n",
       "      <td>3.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex[T.Male]</th>\n",
       "      <td>-0.32</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking_status_imp[T.Never smoked]</th>\n",
       "      <td>-1.62</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.03</td>\n",
       "      <td>-3.64</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>0.11</td>\n",
       "      <td>3.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking_status_imp[T.Smoker]</th>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>0.11</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>537.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>43.16 on 36 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrrr}\n",
       "\\toprule\n",
       "{} &  coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &  cmp to &     z &    p &  -log2(p) \\\\\n",
       "covariate                              &       &            &           &                 &                 &                      &                      &         &       &      &           \\\\\n",
       "\\midrule\n",
       "CCI\\_cat[T.Low]                         & -0.55 &       0.57 &      0.57 &           -1.66 &            0.56 &                 0.19 &                 1.75 &    0.00 & -0.98 & 0.33 &      1.61 \\\\\n",
       "CCI\\_cat[T.Moderate]                    & -0.90 &       0.41 &      0.61 &           -2.09 &            0.30 &                 0.12 &                 1.36 &    0.00 & -1.46 & 0.14 &      2.80 \\\\\n",
       "CCI\\_cat[T.High]                        & -0.74 &       0.48 &      0.60 &           -1.92 &            0.43 &                 0.15 &                 1.54 &    0.00 & -1.24 & 0.22 &      2.21 \\\\\n",
       "CCI\\_cat[T.Very high]                   & -0.99 &       0.37 &      0.68 &           -2.32 &            0.34 &                 0.10 &                 1.41 &    0.00 & -1.46 & 0.15 &      2.78 \\\\\n",
       "age\\_cat[T.15-24]                       &  0.02 &       1.02 &      0.65 &           -1.25 &            1.29 &                 0.29 &                 3.63 &    0.00 &  0.03 & 0.98 &      0.03 \\\\\n",
       "age\\_cat[T.25-34]                       & -0.75 &       0.47 &      0.85 &           -2.41 &            0.92 &                 0.09 &                 2.50 &    0.00 & -0.88 & 0.38 &      1.40 \\\\\n",
       "age\\_cat[T.35-44]                       & -0.89 &       0.41 &      0.85 &           -2.55 &            0.77 &                 0.08 &                 2.15 &    0.00 & -1.05 & 0.29 &      1.77 \\\\\n",
       "age\\_cat[T.45-54]                       & -0.72 &       0.48 &      0.85 &           -2.38 &            0.93 &                 0.09 &                 2.54 &    0.00 & -0.86 & 0.39 &      1.35 \\\\\n",
       "age\\_cat[T.5-14]                        &  0.20 &       1.22 &      0.65 &           -1.07 &            1.46 &                 0.34 &                 4.31 &    0.00 &  0.31 & 0.76 &      0.40 \\\\\n",
       "age\\_cat[T.55-64]                       &  0.04 &       1.04 &      0.68 &           -1.29 &            1.38 &                 0.27 &                 3.96 &    0.00 &  0.06 & 0.95 &      0.07 \\\\\n",
       "age\\_cat[T.65-74]                       & -1.43 &       0.24 &      1.11 &           -3.60 &            0.73 &                 0.03 &                 2.08 &    0.00 & -1.30 & 0.19 &      2.36 \\\\\n",
       "age\\_cat[T.75+]                         &  0.12 &       1.13 &      0.65 &           -1.15 &            1.39 &                 0.32 &                 4.03 &    0.00 &  0.19 & 0.85 &      0.23 \\\\\n",
       "bmi\\_imp\\_cat[T.Healthy weight]          &  0.40 &       1.49 &      0.70 &           -0.97 &            1.76 &                 0.38 &                 5.82 &    0.00 &  0.57 & 0.57 &      0.81 \\\\\n",
       "bmi\\_imp\\_cat[T.Overweight]              &  1.35 &       3.86 &      0.49 &            0.39 &            2.31 &                 1.48 &                10.07 &    0.00 &  2.76 & 0.01 &      7.42 \\\\\n",
       "bmi\\_imp\\_cat[T.Obese]                   & -0.24 &       0.79 &      0.50 &           -1.21 &            0.74 &                 0.30 &                 2.09 &    0.00 & -0.48 & 0.63 &      0.66 \\\\\n",
       "ethnicity\\_imp[T.Mixed]                 & -0.10 &       0.91 &      0.67 &           -1.40 &            1.21 &                 0.25 &                 3.36 &    0.00 & -0.14 & 0.89 &      0.17 \\\\\n",
       "ethnicity\\_imp[T.Other]                 &  0.37 &       1.45 &      0.74 &           -1.09 &            1.82 &                 0.34 &                 6.20 &    0.00 &  0.50 & 0.62 &      0.69 \\\\\n",
       "ethnicity\\_imp[T.White]                 & -0.69 &       0.50 &      0.65 &           -1.96 &            0.58 &                 0.14 &                 1.78 &    0.00 & -1.07 & 0.28 &      1.81 \\\\\n",
       "flu\\_vaccine[T.Yes]                     &  0.19 &       1.21 &      0.55 &           -0.89 &            1.27 &                 0.41 &                 3.55 &    0.00 &  0.35 & 0.73 &      0.45 \\\\\n",
       "gp\\_count\\_imp\\_cat[T.Some]               & -0.21 &       0.81 &      0.39 &           -0.98 &            0.56 &                 0.38 &                 1.76 &    0.00 & -0.53 & 0.60 &      0.75 \\\\\n",
       "gp\\_count\\_imp\\_cat[T.Many]               & -0.59 &       0.55 &      0.64 &           -1.84 &            0.65 &                 0.16 &                 1.92 &    0.00 & -0.93 & 0.35 &      1.51 \\\\\n",
       "imd\\_imp                                & -0.00 &       1.00 &      0.12 &           -0.24 &            0.23 &                 0.79 &                 1.26 &    0.00 & -0.04 & 0.97 &      0.05 \\\\\n",
       "region\\_imp[T.East Midlands]            & -1.41 &       0.24 &      1.13 &           -3.62 &            0.80 &                 0.03 &                 2.22 &    0.00 & -1.25 & 0.21 &      2.25 \\\\\n",
       "region\\_imp[T.London]                   & -0.46 &       0.63 &      0.68 &           -1.79 &            0.87 &                 0.17 &                 2.39 &    0.00 & -0.68 & 0.50 &      1.01 \\\\\n",
       "region\\_imp[T.North East]               &  0.48 &       1.62 &      0.66 &           -0.81 &            1.77 &                 0.44 &                 5.87 &    0.00 &  0.73 & 0.47 &      1.10 \\\\\n",
       "region\\_imp[T.North West]               & -0.69 &       0.50 &      0.88 &           -2.41 &            1.03 &                 0.09 &                 2.79 &    0.00 & -0.79 & 0.43 &      1.21 \\\\\n",
       "region\\_imp[T.South East]               & -0.17 &       0.85 &      0.77 &           -1.68 &            1.35 &                 0.19 &                 3.86 &    0.00 & -0.21 & 0.83 &      0.27 \\\\\n",
       "region\\_imp[T.South West]               & -0.64 &       0.53 &      0.87 &           -2.35 &            1.08 &                 0.10 &                 2.94 &    0.00 & -0.73 & 0.47 &      1.10 \\\\\n",
       "region\\_imp[T.West Midlands]            & -0.00 &       1.00 &      0.72 &           -1.41 &            1.41 &                 0.24 &                 4.09 &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "region\\_imp[T.Yorkshire and The Humber] & -0.04 &       0.96 &      0.72 &           -1.45 &            1.37 &                 0.23 &                 3.93 &    0.00 & -0.06 & 0.95 &      0.07 \\\\\n",
       "season[T.Spring]                       & -0.46 &       0.63 &      0.48 &           -1.40 &            0.48 &                 0.25 &                 1.62 &    0.00 & -0.96 & 0.34 &      1.57 \\\\\n",
       "season[T.Summer]                       & -0.18 &       0.83 &      0.48 &           -1.13 &            0.76 &                 0.32 &                 2.14 &    0.00 & -0.38 & 0.70 &      0.51 \\\\\n",
       "season[T.Winter]                       & -0.89 &       0.41 &      0.55 &           -1.97 &            0.20 &                 0.14 &                 1.22 &    0.00 & -1.61 & 0.11 &      3.21 \\\\\n",
       "sex[T.Male]                            & -0.32 &       0.73 &      0.38 &           -1.06 &            0.42 &                 0.35 &                 1.52 &    0.00 & -0.85 & 0.40 &      1.34 \\\\\n",
       "smoking\\_status\\_imp[T.Never smoked]     & -1.62 &       0.20 &      1.03 &           -3.64 &            0.39 &                 0.03 &                 1.48 &    0.00 & -1.58 & 0.11 &      3.13 \\\\\n",
       "smoking\\_status\\_imp[T.Smoker]           & -0.64 &       0.53 &      0.40 &           -1.44 &            0.15 &                 0.24 &                 1.16 &    0.00 & -1.59 & 0.11 &      3.16 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 3671 total observations, 3640 right-censored observations>\n",
       "             duration col = 'duration_urti_admitted'\n",
       "                event col = 'event_urti_admitted'\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 3671\n",
       "number of events observed = 31\n",
       "   partial log-likelihood = -232.75\n",
       "         time fit was run = 2022-05-06 15:22:16 UTC\n",
       "\n",
       "---\n",
       "                                         coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "covariate                                                                                                                                      \n",
       "CCI_cat[T.Low]                          -0.55       0.57       0.57            -1.66             0.56                 0.19                 1.75\n",
       "CCI_cat[T.Moderate]                     -0.90       0.41       0.61            -2.09             0.30                 0.12                 1.36\n",
       "CCI_cat[T.High]                         -0.74       0.48       0.60            -1.92             0.43                 0.15                 1.54\n",
       "CCI_cat[T.Very high]                    -0.99       0.37       0.68            -2.32             0.34                 0.10                 1.41\n",
       "age_cat[T.15-24]                         0.02       1.02       0.65            -1.25             1.29                 0.29                 3.63\n",
       "age_cat[T.25-34]                        -0.75       0.47       0.85            -2.41             0.92                 0.09                 2.50\n",
       "age_cat[T.35-44]                        -0.89       0.41       0.85            -2.55             0.77                 0.08                 2.15\n",
       "age_cat[T.45-54]                        -0.72       0.48       0.85            -2.38             0.93                 0.09                 2.54\n",
       "age_cat[T.5-14]                          0.20       1.22       0.65            -1.07             1.46                 0.34                 4.31\n",
       "age_cat[T.55-64]                         0.04       1.04       0.68            -1.29             1.38                 0.27                 3.96\n",
       "age_cat[T.65-74]                        -1.43       0.24       1.11            -3.60             0.73                 0.03                 2.08\n",
       "age_cat[T.75+]                           0.12       1.13       0.65            -1.15             1.39                 0.32                 4.03\n",
       "bmi_imp_cat[T.Healthy weight]            0.40       1.49       0.70            -0.97             1.76                 0.38                 5.82\n",
       "bmi_imp_cat[T.Overweight]                1.35       3.86       0.49             0.39             2.31                 1.48                10.07\n",
       "bmi_imp_cat[T.Obese]                    -0.24       0.79       0.50            -1.21             0.74                 0.30                 2.09\n",
       "ethnicity_imp[T.Mixed]                  -0.10       0.91       0.67            -1.40             1.21                 0.25                 3.36\n",
       "ethnicity_imp[T.Other]                   0.37       1.45       0.74            -1.09             1.82                 0.34                 6.20\n",
       "ethnicity_imp[T.White]                  -0.69       0.50       0.65            -1.96             0.58                 0.14                 1.78\n",
       "flu_vaccine[T.Yes]                       0.19       1.21       0.55            -0.89             1.27                 0.41                 3.55\n",
       "gp_count_imp_cat[T.Some]                -0.21       0.81       0.39            -0.98             0.56                 0.38                 1.76\n",
       "gp_count_imp_cat[T.Many]                -0.59       0.55       0.64            -1.84             0.65                 0.16                 1.92\n",
       "imd_imp                                 -0.00       1.00       0.12            -0.24             0.23                 0.79                 1.26\n",
       "region_imp[T.East Midlands]             -1.41       0.24       1.13            -3.62             0.80                 0.03                 2.22\n",
       "region_imp[T.London]                    -0.46       0.63       0.68            -1.79             0.87                 0.17                 2.39\n",
       "region_imp[T.North East]                 0.48       1.62       0.66            -0.81             1.77                 0.44                 5.87\n",
       "region_imp[T.North West]                -0.69       0.50       0.88            -2.41             1.03                 0.09                 2.79\n",
       "region_imp[T.South East]                -0.17       0.85       0.77            -1.68             1.35                 0.19                 3.86\n",
       "region_imp[T.South West]                -0.64       0.53       0.87            -2.35             1.08                 0.10                 2.94\n",
       "region_imp[T.West Midlands]             -0.00       1.00       0.72            -1.41             1.41                 0.24                 4.09\n",
       "region_imp[T.Yorkshire and The Humber]  -0.04       0.96       0.72            -1.45             1.37                 0.23                 3.93\n",
       "season[T.Spring]                        -0.46       0.63       0.48            -1.40             0.48                 0.25                 1.62\n",
       "season[T.Summer]                        -0.18       0.83       0.48            -1.13             0.76                 0.32                 2.14\n",
       "season[T.Winter]                        -0.89       0.41       0.55            -1.97             0.20                 0.14                 1.22\n",
       "sex[T.Male]                             -0.32       0.73       0.38            -1.06             0.42                 0.35                 1.52\n",
       "smoking_status_imp[T.Never smoked]      -1.62       0.20       1.03            -3.64             0.39                 0.03                 1.48\n",
       "smoking_status_imp[T.Smoker]            -0.64       0.53       0.40            -1.44             0.15                 0.24                 1.16\n",
       "\n",
       "                                         cmp to     z    p   -log2(p)\n",
       "covariate                                                            \n",
       "CCI_cat[T.Low]                             0.00 -0.98 0.33       1.61\n",
       "CCI_cat[T.Moderate]                        0.00 -1.46 0.14       2.80\n",
       "CCI_cat[T.High]                            0.00 -1.24 0.22       2.21\n",
       "CCI_cat[T.Very high]                       0.00 -1.46 0.15       2.78\n",
       "age_cat[T.15-24]                           0.00  0.03 0.98       0.03\n",
       "age_cat[T.25-34]                           0.00 -0.88 0.38       1.40\n",
       "age_cat[T.35-44]                           0.00 -1.05 0.29       1.77\n",
       "age_cat[T.45-54]                           0.00 -0.86 0.39       1.35\n",
       "age_cat[T.5-14]                            0.00  0.31 0.76       0.40\n",
       "age_cat[T.55-64]                           0.00  0.06 0.95       0.07\n",
       "age_cat[T.65-74]                           0.00 -1.30 0.19       2.36\n",
       "age_cat[T.75+]                             0.00  0.19 0.85       0.23\n",
       "bmi_imp_cat[T.Healthy weight]              0.00  0.57 0.57       0.81\n",
       "bmi_imp_cat[T.Overweight]                  0.00  2.76 0.01       7.42\n",
       "bmi_imp_cat[T.Obese]                       0.00 -0.48 0.63       0.66\n",
       "ethnicity_imp[T.Mixed]                     0.00 -0.14 0.89       0.17\n",
       "ethnicity_imp[T.Other]                     0.00  0.50 0.62       0.69\n",
       "ethnicity_imp[T.White]                     0.00 -1.07 0.28       1.81\n",
       "flu_vaccine[T.Yes]                         0.00  0.35 0.73       0.45\n",
       "gp_count_imp_cat[T.Some]                   0.00 -0.53 0.60       0.75\n",
       "gp_count_imp_cat[T.Many]                   0.00 -0.93 0.35       1.51\n",
       "imd_imp                                    0.00 -0.04 0.97       0.05\n",
       "region_imp[T.East Midlands]                0.00 -1.25 0.21       2.25\n",
       "region_imp[T.London]                       0.00 -0.68 0.50       1.01\n",
       "region_imp[T.North East]                   0.00  0.73 0.47       1.10\n",
       "region_imp[T.North West]                   0.00 -0.79 0.43       1.21\n",
       "region_imp[T.South East]                   0.00 -0.21 0.83       0.27\n",
       "region_imp[T.South West]                   0.00 -0.73 0.47       1.10\n",
       "region_imp[T.West Midlands]                0.00 -0.00 1.00       0.00\n",
       "region_imp[T.Yorkshire and The Humber]     0.00 -0.06 0.95       0.07\n",
       "season[T.Spring]                           0.00 -0.96 0.34       1.57\n",
       "season[T.Summer]                           0.00 -0.38 0.70       0.51\n",
       "season[T.Winter]                           0.00 -1.61 0.11       3.21\n",
       "sex[T.Male]                                0.00 -0.85 0.40       1.34\n",
       "smoking_status_imp[T.Never smoked]         0.00 -1.58 0.11       3.13\n",
       "smoking_status_imp[T.Smoker]               0.00 -1.59 0.11       3.16\n",
       "---\n",
       "Concordance = 0.80\n",
       "Partial AIC = 537.49\n",
       "log-likelihood ratio test = 43.16 on 36 df\n",
       "-log2(p) of ll-ratio test = 2.38"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cph_urti_no_abs_incdt = CoxPHFitter()\n",
    "# cph_urti_no_abs_incdt.fit(data_urti_no_abs_incdt_train,\n",
    "#         duration_col='duration_urti_admitted',\n",
    "#         event_col='event_urti_admitted',\n",
    "#         formula=\"age_cat + sex + CCI_cat + gp_count_imp_cat + flu_vaccine + bmi_imp_cat + region_imp + imd_imp + ethnicity_imp + smoking_status_imp + season\"# + antibacterial_brit_imp + meds_nonabs\" # season +\n",
    "#         # formula=\"age_cat + sex + CCI_cat + bmi_imp + gp_count_imp\"# + flu_vaccine + region_imp + imd_imp + ethnicity_imp + smoking_status_imp + season\"# + antibacterial_brit_imp + meds_nonabs_cat\"\n",
    "#         #, show_progress=True\n",
    "#         #, step_size=0.01\n",
    "#         )\n",
    "# cph_urti_no_abs_incdt.print_summary()\n",
    "# # cph_urti_no_abs_incdt_summary = cph_urti_no_abs_incdt.print_summary(style=\"html\")\n",
    "# # cph_urti_no_abs_incdt.print_summary(columns=[\"coef\", \"se(coef)\", \"p\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12305\n"
     ]
    }
   ],
   "source": [
    "output = GetPrintSummary(cph_urti_no_abs_incdt)\n",
    "\n",
    "with open('../output/hospitalisation_prediction_urti/cph_urti_no_abs_incdt_summary.html', \"w\") as fo:\n",
    "    print(fo.write(output))\n",
    "    fo.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14ac770b190>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NN2MNN/YpLuFtbqH16000r11H87p1eCoqupwXJlOHGHRUkevyXtv0UVGqqpxi0KMEoKeUbod970POB1CZpR0bMVETgwkrYeRM6EOqh69yq7nzzd2U1rdxw4I07lg2Hqvp+E/wzsJGmtaV4MiqAb3ANjMO28IESg5ls3ft5xRl7sTR3KSZmJJGytTppGbMIOmIdNf9SXlLOetL13Og7gCVrZVUtFRQ0VpBdVt1x2rodsx6c7cCEW+N1/ZtcUSZo/A4JC1+cWhtaBcMZyeh0MTD2Xo8r6J7oRiRHIY97vi5m6SUuIuKcJdXaDmUOm/V7fvV+Boajr5Yr8cQFXVYEI4QCmPH8RHo1FCTIkAoAegLtfmw/yNNEAo3gvRCaByMW655BqPOOGoG0fFocXq4/6N9PL+pkNRoK/dfnMG8USfOweOuaqV5Qykt2yrAK7FMjCbs9EQMyTaqCvIp3L2DwsydHMrJwuvxoDcYGDl+EqlTp5M6dTqxo0b3a3bTbm30ualpq6G8pVwThtYKKloqDu/7N4+v64+4XuiJCYkhzhbX4T0cKRix1lhMehNet4+WRudRXkVLh3A4OwTD5z38/zphTAQTFyQwemYsJkvfh818Tieeqmo8VZVdku0duXlrao+dqfUYnoQpJQXzuHEqnbdiQFACcLK01cGBTyHnfe3V1QRGK4w+W/MOxi6D0BE9utWmgzXc+eZuimpbuW5+Kncun4CtBwvOvM0umjceouXrMnytHvR2M5aJUYRMisacHoHH66I0ey8FmTsp2r2DqqICAEakpnPBL35LRGz8yfwLnDQ+6aPOUddFIDqLQ2VrJeUt5bR52o66NsoS1SEO7d5DZ+8izhqHzaitaJZS4mzx0NLgpHBPDdkby6ivaMVg1jN2ViwTFyQQPzpi4GYueb14amrwHkMgOmds7bI2Qq/HPHq0ln5j0iQskydhHj8BfahtQOxUDB+UAPQnHhcUrIecD7WtsQQQkDz3cNxgxLjj3qLV5eH/Ps7h2Y0FJNpDeODiDBaMienRx/tcXtp2VdGWVYMztx7p9iHMeizjIwmZFI1lfBS6EAMt9XXk7djC2heeQggd5/3kTlKnTj/5/g8gUkqa3c1dhpc6tk4eRb2z/qhrQ42hXQQizhpHxogMFo5cSGV+M9kbD5G7tRK304s9zsqE+fFMmJeAzR6Yp24pJb7GRjxVVTjz8rTV1lnaamtvdbXWSAhMqal+UZiIefx4dLZQdBYzwuzfTCZ0FkvHvgpeK45ECcBAIaW2ujjnQy1uULZLOx49xi8GKyH5NDjGEMyWglp++cZu8qtbuGpuCr8+dwJhvVhY5nN5cebW05ZVg2NfLb5mN+gE5vRwLJOiCZkYTZOrlnf+74/UHirhjGtuZuaK84P+R8LhcVDVWkV5a3mH99AuGO371Y5qfNJHSlgKV028igvHXIjRa+bg9iqyNx6iLLcBISBlSjTj58YTkxRKWLQFwzFmap1K3JWVnQQhC2dWNu5Dh3p0bYcwmE3ozJbu901mhMVy9L7ZjDAfY99kxpgQryUF7EMcTBE4lACcKhpKDnsG+evA5wbbCLjwURh7TreXtLm8/HVNDk9uyGdkRAj3XTSV08f1bDipM9IncZU04ciqoS2rFk+ltoDLMiGK0AtT+PjJv5O75Wsmn7GEc275Qb8lohusuL1uPi36lBezX2R31W5CjaFcOOZCrpp4FclhydRXtJK9qYycTWW0NBweirFFmAiPCSEsRqupEBZtITwmhPBoC6GR5pNaDX0yeOrqcB08iK/NgXQ68DmdSKfriH0nPqejY1+6nPgczu73nS6kw6Fd49L26cHvgS40tCNDbPtwlSk9XRUYGsQoAQgEjkY4+Bms/wtUZmsikHHpMZtvK6zjl2/s4mBVC5fPTua3503sVZqJI/FUt9G6q4rGzwoxjgwl+vpJfPPRm2x642XiR4/l/F/8lrCong07BTuZVZm8mP0inxR8gld6OSP5DK6ZeA2nxZ+G9EkqCpporGqlscZBY3UbjdUOGmvaaKlzdvlNFDpBaKSZ8G7EITwmBGu4CdHLXFGDBSkluN2aGDidSIdDEwmXJho+hxN3STGOvX7PZN++jmpzwmLBMn68FreYOBHLhAkYoqPR2+2IkJCg9ziDHSUAgcTRCK9epcUNlt8P8247dlO3l4c+PcDj6w4SF27hTxdN5azxJ1ecpS2rhpqX92GINBNz8xTyD+zgw4f/itFs5vyf/5bE8RNP6v7BRGVrJa/lvMZ/cv5DnbOOsZFjuXrC1awctRKL4eips16vj+ZaJ401bTRV+8WhxkFTjSYSrY1dE9zpDTp/NTYLYX5h6BCJGAsWm3HI/BhKrxdXfr4mBu2ikJ2Nr7m5SzthMnXNBGs/IivsMfbVCu3+QwlAoHE74K1bIPtdbbXxWb/tSEndHTuL67njP7s4UNnMxTOT+N15k4iw9t0bcOY3UP3sXnQWPTE3T6XBWcU7D/6Rxqoqltx8GxlLlvf53sGI0+vkg7wPeDH7RfbX7SfMGMbYyLGkhqd2bCnhKaSEpXQrDO14XF6aah2ax1DdRlON5jm0exBHrog2mvX+kp2HvYawaAvRiaGEx1iCXhykz4e7uBhnbm6XjLEduZvqtER/Hn8OJ9zuY95LZ7VqmV/tER21Jzpngu1uXx8erhbmdYMSgMGAzwvv/QS2Pw+zboSVfzlmcBi0Cmb//CyXf689SLTNxJ9WTeWcSXF9/njXoWaqn94DPknMjVPwRQre+/v9FO7ewbRvreSs628ZkKRzgxkpJVsrtvJe3nvkN+RT2FhIraO2S5s4a1yHIKSGHRaIpLAkTPrjP6W62jwdw0qdxaHdg3A7vR1tw6IsJI63kzRey74aGjm0M5hKKZGtrUenEu+SVrxTsr/Oif6O9bslBPrw8MMZYu12f7Gi7j0Ng90+LBbpKQEYLEippaPe8FeYdAFc9AQYjj8NMbOkgTve2MW+8iYunD6Su749mUhb3/7DemraqHpqD75mF9HXTsI0Opz1Lz/H1nffInHCZJZ978dExo/s072HCs2uZgqbCjuqqhU1FnW87zz9VCd0JNgSSAlL0cShk/cwMnQkRt3xxbR9vUJDdRuVBY2U5tRRsr+uw2uIiA3pqM2QOC4Sa/jQ/pHqKdLnw9fYeALhOEJEGhrwtbQc857HWqRnjI3t2BfW468oH2gMUVF9DrQrARhsbHoEPv4NpJ8BV7wE5rDjNnd5fDzyRS6PfJGL3WrijxdOYdnkuD4NGXgbXVQ/nYm7qo2oK8ZjnTqC7K/Wsuaxf+L1uJm6ZDnzL75iwPILBTMNzoYOQWgvuVnUqJXgbHI3dbTTCz2JoYldhSFM8yISbAnoj+H5SZ+kurSZ0pw6SnPqOHSgHpdD8xKiRtpIGB2BJdSI0azHYNJjNOsxmvQYzHqMJp3/9fA5g0mH0aQP2sB0fyJdrg4Pol0YPHV1XRfsVR5esCePMzwVCEZ98D7mUaP6dK0SgMHIzlfgnR9A/FS45k2wnXhGzt5DDdzxn91klTWSkRTBzYvSWTE1AWMvpyb6Wt1UP5eFq6gR+4VjCJ2bQEt9HZvefJXMzz5CbzAy+9urmH3eKkwhgX3yCQaklNQ56yhqLKKgseCw99CkvXZe3WzUGUkKS+oYTkoJT2FyzGQmRk1EJ7p+jz6vj6qiZkr311GSU0dlQSPONg/08s/WYOwkDn6xMJr1XY+1t+kiLl2vixgRgi1i6KerkFLia2josoLb1+YIqE3h5y5HH3HimuXdoQRgsJLzEfzneohIgmv/C/aUE17i9vp4bUsxT2/IJ6+6hYQIC9fNT+Oq01J6FSj2ubzUvrwPx75awr+VSthZyQghqCsrZcOrL7D/6w2EhEcw/+IryDhn+bCLD/QXUkqq26oPC0On4aXipmKcXq2+crQlmkWJi1ictJgFIxcQZureK5RS4nX78Lh8uF1e3E4vHv+rtu/reszlxeP04nb58HTsH7ttd9lZOxMZbyXRPyyVON5OSKgamhrsKAEYzBRuglcuB6MNrn0LYns2LdPnk3y5v5KnNuTzVW4NIUY9l85O4saF6aTH9Cx/jPT6qHvjAK07KgldOJKIlaM6hgvKcnNY/9KzFGdlEhEXz6LLr2X8/MVqFWg/4pM+ylvK2VaxjfWl6/mq9CsaXY3ohZ4ZsTNYnLSYxYmLGWMfc8pmCPm8Pk0s2gXF7cXt9OF2eqgpaaEkp46y3PqOAHZ0YqgWp5gQycixdswhakHYYEMJwGCnYi+8cJFWq/jqNyB5Tq8uzzrUyNNf5bN65yHcPh9LJsRy06J05o+KPuEPh/RJGt7Po/mrQxgTQ4lcNQZTkvb0KaWkYOc21r38LNVFBcSNGsPiq24Y9DmFghWPz0NmdSbrS9azvnQ9+2r3AZBgS9Cqs42cT3RINFaDVduM2mbRn9oppF6vj6rCJkr21VG6v46ygw143T6EgBEpYYwcaycs2tKRltvmLx9qNKkpmoFACUAwUFcAL6yCpnK47IVjpo44HpVNDl7cVMiLm4uobXExKSGcXy4fz5knWEwmpaRtdxX17+Xha3Zjm5dAxLI0dP70yT6fl30b1rLhtRdoqq5iRGo68WPGET9qLHGjxhCTkqqGiAaAipYKNpRu6KjO1upp7badTuiOEgWb0XbUMauh0/FujtmMto5jRn3Pv0+P20tFXiMl+7XgdUV+Y5eU3O0YLfouFeBs7XUcjtgfSgvmBgNKAIKF5kp48WKtGM2qx2DqJX26jcPt5e0dpTy+Lo+S+jbe/v5CJo0MP+F1PoeHho8LaPm6DF2oEfvKUYRMG9Hxx+hxudj96Yfk7dhKxcEDOFq0VZ96g4ERqenE+QUhbtQYopNSBqxs5XDE5XWRU5tDk7uJNncbLZ4WWt2ttLhbaPW00upu7Xjt7lj765GFe46FUWfUhMRwWBTSItI6PJEI87EDktInaWs+XDK0xV/kp72OQ0vD4f3OayHa0elFR8lQa4Tfi+hUStQWcbgAkN6ghiRPhBKAYMLRAK9cBYUb4NwHYO53+3yr6mYnK/6+HpvZwLs/XERoD+oOALhKmqh7Oxd3STPmMXbsF4zGOKLrbCApJQ2VFVTkHaD84AEq83MpP5iLq017SjUYTYxI00QhfvRY4tJHE5WUPODFaRTHxid9ODyOLqLQ4vYLiaeFNndb12N+IWnztNHsaiarNosGZwN6oWfaiGksTlrM6UmnM9Y+ts9P7C6Hp1th6FLsp9FJW1P30zLNNkMPvIr+rzMdTCgBCDbcDnjzZq1G8em/hLN+c9zUEcfj67warnria1ZmjOQfV0zv8R+B9ElaNpfR8FEB0uMj7Mxkws9MRhiP/cQlfT7qK8ooz8ul4uABKvJyqcg/iNuhTYM0mM3Epo0mftQY4kZr3kJUQqIKLAcJXp+XzOpM1pWs6xKjiLfFszhRC1jPTZiL1dj/U4e9Xh9tje4OT6JzGdEu+43OE9aZtrXXlbabu6TksIYPzXoKJyUAQojlwN8BPfCklPLPR5xPBZ4GRgC1wDVSyhL/ufuBlf6mf5BSvuY//ixwBtBeaPUGKeXO49kxrAQAwOvRUkfseAFm3wQrHjxu6ojj8cgXufzfxzncu2oKV89N7Z0ZTS7q38+jbWcVhmgL9gvGYBnX80Vi0uejtqxUE4ODByjPy6Wy4CAepzb90WgJIS59tDZ0NHos8aPGYI9LUKIQBFS0VPDVoa9YV7KuI0Zh1BmZGTuT5PDkLiU+463xxNkOV24bKKSUOFs9HWLQXme6Y7/TUNSRdab1Rp0/gV9IR8bX8BhNIEwBnt0UGmnu83BXnwVACKEH9gNLgRJgC3CllDKrU5v/AO9JKZ8TQpwN3CilvFYIsRL4CXAuYAa+BJZIKRv9AvCelPKNnnZi2AkA+FNH/B42/A0mXQgXPX7C1BHd4fNJbnh2C1/n1fDW9xYwJbH3C0ocuXXUv30QT3UbprRwzKMiMKeGY0oN7wgW99weL7WlJVTk5VJ+8AAVeQeoKsjH49aya5pCrB2xhPjRY4kbNZaI2L6tfFacGtxeN9sqt7G+ZD1bK7ZS3lJ+VF4l0Cq3HVnOs3NN6HhbPOGm8FPyXbtdXi3La6cEfp3fu9o8J77JKeKqu+cSGd838TwZAZgP3C2lXOZ//2sAKeV9ndrsBZZLKYuF9q01SCnDhRB3ABYp5R/87Z4CPpZSvq4EoJdsfBg++S2MOhMuf/GEqSO6o6bZycp/bMBi1PHuDxf1qvJYO9Ljo2lDKW2Z1bgPNWsrUgUY46yYUsMxpWmioI809/oP2OvxUFNSREV+LhUHczVRKMzH69H+CC22UGJHjekYPoofNZawmBFKFAYxLq+r+xrQLYdfq9qqjgpMW/SWLqLQvt+53GeUJeqoldP9jaPF3ZHEr7tg9akkfdqIPq+xOBkBuATtx/0W//trgblSyts7tXkZ2Cyl/LsQ4iLgTSAGmAXcheY9WIFvgEeklH/xC8B8wAl8BvxKSuns5vNvBW4FSElJmVVYWNjbvg8d2lNHJEyDq//To9QRR7KloJYrHv+a5VPiefjKGSf14+lzenEVN+IqaMRZ2IirqAnp/yPRhZkwp2negTk1HONIG6IPlbS8HjfVRYWap5CnxRSqiwrwebXPCQkL12IJ6WOIGz2G+FFjCY068doHxeDB7XNT01ZzlDAcWQvaI7s+jRt0hsMCcQxvIiYk5ph5l4YTAy0AI4GHgXRgHXAxMEVKWS+E+C1wKVAFVAJbpJQPCSESgHLABDwOHJRS3nM8W4a1B9BOzofwnxsgIhkufUbLI9RL/vVlLg98lMMfLpjMtfPT+s006ZO4y1twFfoFoaARb72m6cKow5Qc5vcSwjGnhKPr49OMx+WiuqhACzTnHaDi4AGqS4qQPh8A1gg78aPHEps+hnFzFzAiNb3f+qgIDD7po9ZRS0VLhVYHunP9506C0Z5Wox290BMdEt0RfzhSLOJt8cRZ44a8SAzoENAR7UOBfVLKpG7OvQy8KKX84IjjZwK/kFKedzxblAD4KdwEr10DbXUw9zY469e9GhLy+SQ3PbeFjbk1vPX9vsUDeoq3wdkhBs7CRtxlzeADBBhirV28BH1U31ezul1OqgryNUHwxxVqS0vQ6XWc853bmXJm7xfVKYILKSUNzoYOz6G8pbxbb+LIxXRGnZHksGRSwlNIC0/rUvch1ho7JLzJkxEAA1oQeAlQihYEvkpKubdTmxigVkrpE0LcC3illL/zB5DtUsoaIUQG8DIwXUrpEUIkSCnL/DGDvwEOKeWvjmeLEoBOtNVpdQW2PgNhCbD8Pq2+QA//s9a2uFj5j/UY9Tre+9Gik6o93Bu0YaOmw15CYWOnYSOjP6gcgTmt78NG7bQ2NvD+3x+gaM8uZp57PmdcezM6VS1q2NPsau4Qg0Mth7SMrQ1a5taixiJcvsNlPkMMISSHJZMannrchW+ngh9M/wExIX2r4X2y00BXAA+hTQN9Wkp5rxDiHmCrlHK1f5joPrSQ4DrgB1JKpxDCAmz336YRuK19qqcQ4nO0aaMC2Ok/17WY6BEoAeiGkq3w3k+hfDeMOQdW/B9E9Sxn+NaCWi5//GuWTY7jkatmBuRJR/ok7opWXH4xcBY24q31Fxo36jAmhR32ElLC0PWyLKbP62XtC0+x/cPVpEyZxnk/uZOQsBOviFYMT9qT87XXeShoLOgQhmb3cX+eBpxnlz9LanjvpnC3oxaCDWW8HtjyJHz+R/C6YPHPYdFPejRd9LG1B7nvw338/vzJXL8gbcBN7QnexiOGjQ61gE/7f2qIs3ZMPTWn9XzYaM8Xa/j0yUcIjY7hwl/8DzEpaQPcC4Vi8KAEYDjQWKZVGdv7FkSN1moOjz7ruJf4fJJbnt/K+gNVvPm9BWQk2U+Nrb3A5zo8bNTuJUh/lSxdqH/YyO8lmEaGIo6xWObQ/n2s/su9uNraOPf2nzH2tAWnshsKRcBQAjCcOPg5vP9zqM2DKRfDsvsg7NjF5Ov88QC9XvD29xcSHTq4Kz5Jn8RT2drFS2gfNsKgw5QUenjYaJQdnfnwuH9TbTWr//InynP3s+DSq5l30eVqxbFiyKMEYLjhdsBXD8H6v2oxgds2gP7Y0y63FdZx1RNfkxBh4bmbTiM1emCX6/c33kZXR1DZWdiIu7QZfBJ9lIURN0/BEB3S0dbjcrHmiYfJWvc5Y09bwPIf/BSTJeQ4d1coghslAMOVrHfg9evg/H/CzOuO23RbYS03P7cVg07w1PVzmJZsPzU2DgA+lxdnXgN1r+eAXjDi5qkYOy2jl1Ky/YN3WPvC00Qnp3DBL/4He1x8AC1WKAYOJQDDFSnhqW9BQzH8cDuYjp+l8WBVM9c//Q01zS7+dfVMzppw/EIygx13RQvVT+3B5/IRc+NkzKldZwAV7N7B+w/dj0Qybt4iUqfOIGXqNEJCe59qQ6EYrCgBGM4UboJnlsOS32kzhE5AZZODG5/Zwr7yJv60agqXzzlxofrBjKfOQfVTe/A2OIm+ZiKW8VFdzteVH2L9S89SmLlTq2UgBPGjxpCaMZPUjOmMHDdBVTtTBDVKAIY7r1wJBRvgRzvBFn3C5s1OD997cRvrD1Tzk3PG8uMlfS/4MRjwNruofnoP7vJWoi4fh3Xa0Z6Nz+ulLHc/hbt3ULh7B2W5OUifD6PZQvLkqaRmzCB16gyiEpOC+t9CMfxQAjDcqdwH/56vpY5Y3m0Wj6Nwe3386s1M3txewhVzkvnjhVMwnMTK3EDjc3iofm4vroJG7BeMJnTeyOO2d7a2ULR3N4W7d1KUuYO6skMAhEbHkDp1OmkZM0iZOh1reGBXiCoUJ0IJgAJW/1DLKPrDrRCZ1qNLpJT8dc1+/vl5LmdPiOXhq2ZgNQVvnV/p9lLz8j4c2bWEL00l7OzkHj/NN1RWdHgHRXt2afWQhSA2bRRpGTNIzZjByPGTMBjVcJFicKEEQKEtFPvHDJh4Hlz8ZK8ufWlzIf/79h6mJkbw1A1ziBnkawWOh/T6qHvjAK07KgldOJKIlaMQut4N6fh8XirycinctYPCzJ0c2p+Nz+vFYDKTNGmKJghTpxOdnKqGixQBRwmAQuOze2D9X+DWtTByeq8uXZNVwQ9f2U5cuIUnr5vN2LjgnSkjfZKG9/No/uoQ1hmxRF4y9qQSz7naWinO2tPhIdQeKgHAFhnVZbjIZu95KU2For9QAqDQcDTA36dDQgZc906vL99eVMfNz26hrtXNtKQIVkxNYMXUBJKj+r8I+EAjpaTp82Ia1xRimRBF+LI0jPHWfnlib6yuojBzh+Yh7NmFo6kRgBGp6VowOWMGiRMmYTQFryelCB6UACgO8/W/4aNfwTVvwZglvb68otHBf3eU8kFmGbtLGgCCWgyaNx2i/t2D4AN9lIWQiVFYJkVjTotA6E9eDKTPR2VBHgW7tlOYuZPSfVn4vB70RiNJE6eQOnU64+YtJCJWLURTDAxKABSH8Tjh4TlgCYdb18FJ5MIpqmnlgz1lXcQgwy8GK4NIDLxNLtqya3Bk1eLIrQOPRIQYCJmgiYFlXGSXnEIng9vhoCR7DwX+4aKakiJ0ej0Z55zL/IuvwBph75fPUSjaUQKg6ErmG/DmzbDqcZh2eb/csri2lQ8yy3g/yMXA5/TiPFBHW1YNjn21+Fo9YBBYRtuxTIomZGI0+nBTv31eY3Ulm//7Opmff4LBZGbO+Rcxe+UqjBZLv32GYnijBEDRFZ8PnjgLWmvh9i1g7N8fm3Yx+CCzjF1+MZiaGMHKjOASA+mVOAsacGTV0JZd25F11JQcponBpCgMsf0TN6gpLWbDK8+Tu2UTNnsk8y+5iqlnf0tVMVOcNEoAFEeT9yU8fwF8615YcPuAfUxxbSsf7inj/d1dxaDdM0iJDhIxkBJPRStte2toy67BXaJViDJEW/xiEI0pNbzXU0qPpDQnm3UvPcOhnCwiRyax+MrrGDNnvppOqugzSgAU3fPCRVC6DX68E0IGfopihxhklrOruB4ITjEAreB9W3YNbVm1OA/Wg1eisxmxTIgiZFI05rF2dKa+Pb1LKTm4dTPrX36W2kMljBw3kdOvvpHECZP6txOKYYESAEX3lGfCo4th4Y9g6T2n9KO7E4MpieEdYhBMNQl8Dg+O/YfjBtLhRRh1mMfYCZkUjWViFPrQ3scNfF4ve778lE3/eYnmulrSp88iNn0M1gg7NrsdW0QkVrsda4Qds9WmvARFtygBUBybt26FvW/Dj7ZDRFJATCipa+XDzHLezyxjZ5CLgfT6cOY34MiqpS2rBm+9EwSYUsI1MZgcjTGmdwVo3E4H2z9Yzc41H9BSW4uUvqPa6I1GrOH2DnGwRkT6X/3HIuxY7ZHYIiIx25RYDCeUACiOTV0hPDwbpl4KF/4r0NZ0KwaTRx4Wg7SYIBIDKXGXtdC2twZHVg3ushYADLEhmhhMisaUFNaruIHP58XR1ERLQz2t9fW0NtTR0lBPS30drQ31tDbUa+f8m/R1IxYGAyHtohBhx2aPPCwSEe3ioXkXFluoEosgRwmA4vh8/FvY9Ajc8ikkHfX/JGCU1rfxoX9q6Y6ieiB4xQC02gTtM4qceQ3gk+jCjNhmxRN2dnKfYwbHQvp8tDU30Vpf10UU2sWis4i0Njbg83qPuodOb8AaEYE1wk7y5AzmXngpIWHh3XyaYrCiBEBxfNrq4LHTweuGW7+EsMG3KrU7MZiUEN4xtTTYxMDX5sGRU0trZjWOvTXo7WbsF4wmZOKJ6zUMBB1i0dmLaPcw6utpqq2meM9ujBYLp11wCTNXnI/RrNYqBANKABQnpnyPVj4ybhLc8D4YBm+emnYx+CCzjO1HiMGKqQmkB5kYOAsaqPtvLp6KViyTo7F/ezQG++D7968uLmT9K8+Rt+0bQqOiWXDZ1Uw+Ywk6nVqrMJhRAqDoGe1F5KdfAxc8DEEw9nuovq1j0Vm7GExMCOe8jAQumZVEXHhwPKVKr4/mDaU0floEAsLPSSV04ciTylI6UJRk7WHdS89QlptDdFIKi6+6gVEz56hYwSBFCYCi53zxJ1h7Pyy/H+bdFmhresWh+jY+3FPO+7sPsb2onkR7CB/8aDER1uAp0uKpdVC/+iCOfbUY423YV405qpj9YEBKyYHNX7Hh1eepKztE0sQpnH7NjSSMGR9o0xRHoARA0XN8PnjtGtj/EVz7Fow6M9AW9YlthXVc8fgmzhwfy+PXzgqqp1MpJY6sGupXH8Tb4MJ2WjwRy9PQDUIh83o8ZH72MZvefIXWhnrGzVvEoiuvIzL++CU3FacOJQCK3uFsgieXQnM5fOcLiEoPtEV94qkN+fzhvSz+97xJ3Lwo+Prgc3ppXFNI88ZSdCFGIpanYUoLRx9u7rfspP2Fq62Vre/9l63v/he300FkQiJxo8YQP3occaPHEJc2WiW4CxBKABS9pzYPHj8LwkfCzZ+AOfgqgEkpufWFbXyZU8l/blvA9GR7oE3qE65DzdT/NxdXcVPHMWHWow83+Tczuo597b0+3IQ+zIQwnNoYQkt9HXu+WEP5wf2U5+XSXFOt2St0RCUmET96LHGjxxI/aiwjUtMxmPovs6qie5QAKPrGwS/gxYtg/Aq47IWTqh0QKBpa3az4x3qEgPd/GFzxgM5In8RV0IinwYmv0Ym3wYW3yYW30YW3wYm3yQXeo/+edTbjCYVCZzOedBK7Y9FSX0dFXi7lB/f7Xw/Q2lCv2abXE52cSny7pzBqDDEpqegNwfkdDVaUACj6zqZ/wce/hjN/DWf+KtDW9ImdxfVc+uhGzhofy2NBFg/oKdIn8bV5OsTA1+DC26jtHxYLJ75mNxz5Z68T6MOMRwiEuZNQaO+FRX/S/3ZSSpprayjPO0DFwQOUHzxARV4ujmbNu9EbDIxITSdu1FjiRmvCEJ2YrNJinwRKABR9R0p45wew8yXNC5h0fqAt6hNPrs/jj+9n87vzJnFTEMYD+gvp9eFtduPr5Dl4j9p3IR2eo641JocReeEYTImh/WuTlDRWVVB+8LCnUJGXi6utFQCDyUxs2qgOQYgbNYaohEREEHqkgUAJgOLkcDvg2ZVQmQ23rIG4yYG2qNdIKfnO89tYu7+SN25bwLQgjQecKnwuL74O78GJp9ZJ81el+FrchC4YSfjSVHQWw4B9vvT5qCsvo8IfS6jIO0BF/kE8TicAppAQYtNHEzdqLPH+mEJEXPyQ9O5OlpMSACHEcuDvgB54Ukr55yPOpwJPAyOAWuAaKWWJ/9z9wEp/0z9IKV/zH08HXgWigW3AtVJK1/HsUAIQYBrL4PEztRXCt34J1qhAW9Rr6ltdrPzHBi0e8KPFRISosebe4Gvz0PBxAS2by9CFmbB/exQhU2JO2Y+uz+eltrTEP2x0gIqDuVQW5uF1uwEw22yaIHSafRQWPWLYi0KfBUAIoQf2A0uBEmALcKWUMqtTm/8A70kpnxNCnA3cKKW8VgixEvgJcC5gBr4ElkgpG4UQrwNvSSlfFUI8CuySUv77eLYoARgElGyDZ84Fewos/hlMuQQMwTWLY0dRHZc+uoklE2N59JqhGQ8YaFzFTdS9dQB3WQuW8ZHYLxiDISowUzy9Hg/VxYUdglCed4DqooKOxHYh4RHEjxqjzTwaPZa4UWMJjQy+h5eT4WQEYD5wt5Rymf/9rwGklPd1arMXWC6lLBbaX1ODlDJcCHEHYJFS/sHf7ingY+A/QBUQL6X0HPkZx0IJwCAh91P45HdQuRfCRsL878PM68Ey+FarHov2eMBd357EjQuHbzzgZJBeSfOmQzR+Uoj0ScKXpBC2OPGUTzvtDo/LRVVRfocgVOTlUlNc1FFHITQyyi8I40iZkkH86HFDOsh8MgJwCdqP+y3+99cCc6WUt3dq8zKwWUr5dyHERcCbQAwwC7gLzXuwAt8AjwDPAV9LKcf4r08GPpRSTunm828FbgVISUmZVVhY2Nu+KwYCKSH3M/jqIShYD+YImHMTzL1tUGYSPRItHrCVtfurVDzgJPE0OGl49yBte2owxIYQeeFYzKMiAm3WUbgdDioL8qjIO6DFFA4eoLasFKTEbLWRMmUaqRkzSM2YgT1u8P8f7g0DLQAjgYeBdGAdcDEwRUpZL4T4LXAp2hN/JdoQ0ov0UAA6ozyAQUrpdtj4Dy2RnM4AGZfDgh/BiHGBtuy4tMcDdDp474cqHnCytO2rpf6dXLx1TqwzYgnJiMGcGj4o01e009bUSNGeXRTu3kHBrh001VQBYI9L0MRg2gxSJmdgtgZXdtkjGdAhoCPahwL7pJRH1Rb0ewovAh+ihoCGHrV5WlGZHS+CxwHjV8LCH0PK3EBbdky2F9Vx2aObOGdiHI9cPRP9AC2GGi74XF6aPi+iaX1px6I0Q2wI5rQITKnhmFPD0UdbBmXcRUpJXVkpBbt2UJi5g+K9mbgdbQidjoQx47HHJwTU7kVXXt/n2MXJCIABLQi8BChFe4K/Skq5t1ObGKBWSukTQtwLeKWUv/MHkO1SyhohRAbwMjDd/6P/H+DNTkHg3VLK49YjVAIQJLRUwzePa1tbHSTP04rOjzt3UK4kbo8HJEeFcOOCdC6bk0yoeeCmNw4HfC4v7pImnIWNuAoacRY2Ih1aUFYXasScGo4pLRxzWgTGkbZBmfLa63FTtj+HwswdFO7eSUtDXUDtufR/7sUen9Cna092GugK4CG0aaBPSynvFULcA2yVUq72DxPdh7a+cB3wAymlUwhhAbb7b9MI3Cal3Om/5yi0aaBRwA60qaPO49mhBCDIcLVo3sDGh6GhCKLHakKQcfmgKjYjpeSTrAqeXJ/HloI6wswGrpybwvUL0ki09654u6J7pE/iqWztIgjeWgcAwqjDlByGqV0UUsLRhSgB7k/UQjBF4PB6IOtt+OrvUL4bQuNg3vdg1o0QYg+0dV3YWVzPUxvy+SCzDIAVUxO4ZVG6ChIPAN5GJ86CRlyFmiC4DzWDDxBgjLNqQ0b+oSN9pHlQDhsFC0oAFIFHSsj7UhOCvC/AFAazrod534eIxEBb14XS+jae/SqfV78ppsnpYU5aJDcvGsXSSXEqTjBA+JxeXMVNuAoaNE+hqAnp9A8bhZswp4V3xBGMCaEIvfoeeooSAMXgomwXfPUP2Ptfrezk1MtgwQ+1esSDiCaHm9e3lvDMV/mU1LWREmXlpoVpXDo7GZuKEwwo0idxl7doHkKBNnTkbdBGiYVJhynFLwhp4ZhSwtCp7+OYKAFQDE7qCrRsozteAHcrjF2mxQlSFw6qesQer49Psip4Yn0eO4rqCbcYuGpuKtcvSCUhQsUJThWeeieuwoYOQXCXt2iRRwHGBNthQUiLwBAxeOJMgUYJgGJw01oLW56EzY9Caw0kztKmkE44D3SDa4XmtsI6nt6Qz4d7ytAJwXkZCdyyeBRTEgff4qehjs/hwVXkn21U2IirqBHp0lb76u3mw4KQGo4x3jZgNQ8GO0oAFMGBqxV2vQwb/6l5B1GjtKGhaVeCcXA9aRfXtvLsxgJe21JMs9PD3PQoblk8iiUTYtEN0x+aQCO9EndZc5fZRr5GLcekMOs7YgimVP+wkWlwPVwMFEoAFMGFzwvZq7WA8aEdYBsBc78Ls28edFlIGx1uXvummGe+yudQg4P0GBs3LUzj4llJWE1qXDqQSCnx1jn9gqANHXkqW7VhIx0YE8MIGR+JZVI0xgTbkJ1pNGQFwO12U1JSgsPhCJBVA4PFYiEpKQmjcfAuoz8lSAkFGzQhyF0DRhvMvE5LQGdPCbR1XfB4fXy4p5wn1+exq6SBiBAjV/vXE8SFq2LogwVfmwdnkd9DOFiv1VmWoI80EzIxGsvkaMxpEUNqltGQFYD8/HzCwsKIjo4eMuotpaSmpoampibS01Wmyg7K92hDQ3ve0IRhysVawDh+aqAt64KUkm2FdTy5Pp+Ps8ox6ATfzhjJzYvTmTxSxQkGG94mF47sWtqyanDk1oFHIkIMhEyIwjIpCsu4KHTm4B4qGrICkJ2dzYQJE4bMj387Ukr27dvHxIkTA23K4KOhBL7+N2x7FlzNMPpsLWCcfsagmjkEUFjTwjNfFfD61mJaXV7mj4rmlsXpnDVexQkGIz6XF+f+Ok0M9tXia/WAQWAZbccQZwUC952FnZ6IPrRvtTeGtAAM1R/Jody3fqGtDrY+DV8/Ci2VkDBNE4KJF4B+cI29N7S5efWbIp7dWEBZg4NRMTZuWpTOxTOTCBkmgchgQ3olrsIG2rJqacuu6QgmB4rYH83AOMLap2uVAAQhQ7lv/YrbAbtf1YaHanLBnqrNHJp+NZj69gczULi9Pj7ILOPJ9flkljYQaTVy9dxUbliYRkyomreuGBiOJQCDLwVfEJKTk8P06dM7tvDwcB566CHuuOMOJkyYQEZGBqtWraK+vr7jmvvuu48xY8Ywfvx4Pv7448AZPxQwWmDWDfCDb+DyFyE0Fj74BfxtMux8JdDWdcGo13HB9ERW376Q1787nzlpUTzyZS4r/7Ge7LLGQJunGGYoD6Cf8Xq9JCYmsnnzZnJycjj77LMxGAzceeedANx///1kZWVx5ZVX8s0333Do0CHOOecc9u/fj/6IknSDrW9Bg5RQ9DV88j9QsRd+tAPC+5ZG91Sw91ADNz+7lRanh8euncWCMTGBNkkxxDiWBzC4BkpPkt+/u5esQ/37FDVpZDh3fXtyj9t/9tlnjB49mtTUVFJTUzuOz5s3jzfeeAOAd955hyuuuAKz2Ux6ejpjxozhm2++Yf78+f1q+7BFCEidDxc/CQ/PgS/vg/P/EWirjsnkkRG89f0F3PDMN1z/zDc8eOk0Lpg+uJLjKYYmagion3n11Ve58sorjzr+9NNPc+655wJQWlpKcnJyx7mkpCRKS0tPmY3Dhqh0mHOzlmeoan+grTkuI+0h/Oe2BcxIieTHr+7kiXV5BJN3rghOhpQH0Jsn9YHA5XKxevVq7ruva7XMe++9F4PBwNVXXx0gy4Yxp98BO16Cz34PV7wUaGuOS0SIkedvOo2fv76Lez/I5lBDG/+7cpKaLqoYMIaUAASaDz/8kJkzZxIXF9dx7Nlnn+W9997js88+61irkJiYSHFxcUebkpISEhOVyz8g2GK0qaFf/FGLC6TMC7RFx8Vi1PPPK2cQF27h6a/yqWx08pfLpmExqqmiiv5HDQH1I6+88kqX4Z+PPvqIBx54gNWrV2O1Hp6OeP755/Pqq6/idDrJz8/nwIEDnHbaaYEweXgw//sQGg9r7tICxIMcnU7wu29P4rcrJvJ+ZhnXPf0NDa3uQJulGIIoAegnWlpaWLNmDRdddFHHsdtvv52mpiaWLl3K9OnTue222wCYPHkyl112GZMmTWL58uU88sgjR80AUvQjJhuc+Sso/hpyPgi0NT3mO6eP4u9XTGdHUR2XPraRQ/VtgTZJMcRQ00AHMUO5b6ccrwf+NU+bIfS9TYNupfDx2JhbzXdf2IbNbODZm+YwIT480CYpggy1EEwxvNEb4Jy7oHo/7BzcweAjWTAmhtdvm49EctG/NnLF45v49VuZPLEuj0+zKjhY1YzL4wu0mYogJHgegxSKk2XCeZB0mrYuYOqlgy5NxPGYmBDOW99fyD8/O8CBymY+3ltObcvh3DR6nSA5MoT0GBvpMaGkj7AxKsZGeoyN+HCLmkmk6BYlAIrhgxCw9B54Zjl8/S84/ReBtqhXJNpD+PPFGR3v61td5Fe3dGx51S3kVbXwdV4tbW5vRzuLUUdatI1RI2yHBSJGE4hIW9+ySyqGBkoAFMOL1PkwfoVWYGbWjWCLDrRFfcZuNTEjxcSMlMgux30+SUWTg/wqTRTaBSK7rImP91bg9clO9zD6RaHdY9DEIS3GqqqZDQPUN6wYfiy5C/49H9Y/CMvvO3H7IEOnEyREhJAQEXJUXiG310dxbWsXryG/qoWNuTW8tb3ravSECEuHOKTHaB7E7LQowi3DvErdEEIJgGL4ETtBSxX9zRNaneHItEBbdMow6nWMGhHKqBGhR51rcXooqPF7DFWHBeLdXYdodHgAbRjquZvmMCY27FSbrhgAlAD0Azk5OVx++eUd7/Py8rjnnnsoLS3l3XffxWQyMXr0aJ555hnsdjs1NTVccsklbNmyhRtuuIGHH344gNYPU876DWS+AZ/fCxc/EWhrBgU2s4HJIyOOKlsppaSu1c2uknru+M9uLv73Jp68fjZz0qICZKmiv1DTQPuB8ePHs3PnTnbu3Mm2bduwWq2sWrWKpUuXsmfPHnbv3s24ceM6cgRZLBb+8Ic/8OCDDwbY8mFM+EiY9z3IfB3KdgXamkGNEIIom4mzxsfy3+8vINpm4ponN/PRnvJAm6Y4SYaWB/Dhr6A8s3/vGT8Vzv1zj5v3JB20zWZj0aJF5Obm9q+tit6x6Cew7RktRcR1bwfamqAgOcrKG99bwM3PbeF7L23j9+dP5rr5aYE2S9FHlAfQz/QkHbRikGCJ0LKF5n0BBz8PtDVBQ5TNxMu3zGPJhFh+985eHvhon0pdHaQMLQ+gF0/qA4FKBx2EzLkFNj+qeQHpZ4BO5WTqCSEmPY9eM4v/fWcv//ryIOWNDu6/OAOjXj1TBhPq2+pHjpcO+qWXXupIB60YRBjMcPbvoHw3PL2s/4cQhzAGvY4/rZrCz5aO463tpdz07BaanZ5Am6XoBUoA+pGepoNWDDKmXgKrHofafHjsDPjoN+BsCrRVQYEQgh8tGcsDF2ew8WANlz+2icomR6DNUvQQlQ20n2hpaSElJYW8vDwiIrRpdGPGjMHpdBIdra02nTdvHo8++igAaWlpNDY24nK5sNvtfPLJJ0yaNKnLPQdL34YNbXXw6e+1wHDYSDj3fpj4bS2FhOKEfJFTyfdf3E50qInnbjqN0d2sNVAEhmNlA+2RAAghlgN/B/TAk1LKPx9xPhV4GhgB1ALXSClL/OceAFaieRtrgB9LKaUQ4ksgAWhPcv4tKWXl8ewYzAIwEAzlvg1qirfAez+FikwYuwxWPDCsFoudDLuK67np2S14fJJLZiWxYmoCM5LtKhldgOlzOmghhB54BDgXmARcKYSYdESzB4HnpZQZwD3Aff5rFwALgQxgCjAHOKPTdVdLKaf7t+P++CsUp4zkOXDrl7DsT1D4FTwyD9b/BTyuE1463JmWbOet7y9gTloUL2wq5OJ/b2TR/Z/zh/ey2F5Up2YLDTJ6MgvoNCBXSpkHIIR4FbgAyOrUZhLwM//+F8Db/n0JWAATIAAjUHHSVisUA43eAPN/AJMuhI9+BZ/dA7teg/P+CmmLAm3doCY12saT18+m0eHm06wK3t9dxgubCnlqQz4jIyysmJrAigzNM1ATIwJLTwQgESju9L4EmHtEm13ARWjDRKuAMCFEtJRykxDiC6AMTQAellJmd7ruGSGEF3gT+KPs5vFACHErcCtASkpKz3qlUPQXEYlw+Quw/2P44Bfw7EqYdhV86w9awXnFMQm3GLloZhIXzUyioU0Tgw8yy3huUwFPbsgn0R7CuVPiWZmRwHQlBgGhv9YB/AJ4WAhxA7AOKAW8QogxwEQgyd9ujRBisZRyPdrwT6kQIgxNAK4Fnj/yxlLKx4HHQYsB9JO9CkXvGLcM0hbDuv+Djf/Uagsv/T3MuA50ajLdiYgIMXLxrCQunnVYDN4/QgxWTI1nxVQlBqeSnghAKZDc6X2S/1gHUspDaB4AQohQ4GIpZb0Q4jvA11LKZv+5D4H5wHopZan/2iYhxMtoQ01HCYBCMWgwWbWykhmXw/s/g3d/DDtegvP+BvFTAm1d0HCkGKzxewbPbizgifWHxWBlxkimJUUoMRhAevLosgUYK4RIF0KYgCuA1Z0bCCFihBDt9/o12owggCLgDCGEQQhhRAsAZ/vfx/ivNQLnAXtOvjsKxSkgdgLc8D5c+G+oPQiPnQ4f/xaczYG2LOiICDFyyawknr5hDlt/u5QHL53GuLhQnt1YwIWPfMWi+7/gTx9ks7O4XgWQB4ATegBSSo8Q4nbgY7RpoE9LKfcKIe4BtkopVwNnAvcJISTaENAP/Je/AZwNZKIFhD+SUr4rhLABH/t//PXAp0DQ5uTtbTrob775hltvvRXQUu3efffdrFq1KlDmK/qCEDD9Khi3HD69GzY9DHv/q60dmHCeWjvQByKsmhhcMiuJhlY3n2SV80FmGU9vyOfxdXkkRYawYmoCK6cmkKE8g35BLQTrZ7xeL4mJiWzevJmcnBzOPvtsDAYDd955JwD3338/ra2tmEwmDAYDZWVlTJs2jUOHDmEwdNXjwdY3xXEo2qwNC1Xs0UTh3AcgMvXE1ylOSLsYvJ9ZxoYD1Xh8kqTIEFZOTWCFEoMecax1AEMqGdz939zPvtp9/XrPCVETuPO0O3vcvifpoDunhXA4HOo/71AgZS7culZLLPfFn+CRuXDGL2H+7WBQhddPhgirkUtnJ3Pp7GTqW1184o8ZPLUhn8f8nsHKqQmszEhgaqISg94wpARgMHC8dNCdh4k2b97MTTfdRGFhIS+88MJRT/+KIERvgAW3w+QL4cM74bPfw+7XYOVfIW1hoK0bEtitJi6bncxlncTg/d2HxSA56vAwkRKDE6OGgPoRl8vFyJEj2bt3b5eMoPfeey9bt27lrbfeOuo/ZHZ2Ntdffz3r1q3DYrEcdW6w9E3RB3I+gg/ugIYirQbx0nvU2oEBor7VxSd7K3gvs4yNudowUbsYnDd1JFMSw4e1GAyLIaBAc7x00J999lm3/wEnTpxIaGgoe/bsYfbso74fRTAzfjmknw7rHji8duCc38OMa9XagX7GbjVx2ZxkLpuTTF2Lyx8zKOep9fk8tjaPlChrh2cw3MWgM0oA+pFjpYNeu3Ztl3H//Px8kpOTMRgMFBYWsm/fPtLS0gJgsWLAMVnhnLv9awd+Du/+CHb61w7ETQ60dUOSSJuJy+ekcPmclA4xeG93GU+sz+PRtQc7xOC8jAQmjxzeYqCGgPqJ3qSDfuGFF/jzn/+M0WhEp9Pxu9/9jgsvvPCoew6Wvin6CSlh1yvwyf9AWz3M/z6c8Sswq7TJp4LaFhef7NVmE208WIPXJ0mLtnLt/DQun5NMqHnoPg+fVDrowcJgFoCBYCj3bVjTWguf3gXbn4fwJP/agZVq7cAppF0M3thWwtbCOsLMBq6cm8L1C9JItIcE2rx+p8/poBUKRT9jjYLz/wk3fawVpn/talj9Q81DUJwSomwmrjgthTe+t4C3f7CQM8aP4KkN+Zz+wBf88JUd7CquD7SJp4Sh6/MoFIOdlHnw3bVaqumN/4CYsbDwx4G2atgxPdnOw1fNpKSulec2FvDqN8W8u+sQc9IiuXnRKJZOikM/RAvaKAFQKAKJ3qhND60vgjV3QexkGHtOoK0aliRFWvntykn8aMlYXt9awtMb8rntxW2kRFm5aWEaC8bEEEgZSIm2Yjbo+/WeSgAUikAjBFz4L6g5CG/cBN/5TPMGFAEhzGLk5kXpXD8/lU+yKnhifR53v5t14gsHmE9/dgZjYvt3woASAIViMGCywRUvwRNnwStXaiJgiQi0VcMag16nVS+bmsCu4nqK61oDak9cuLnf76kEQKEYLESmwmXPw/MXwJvfgStfAV3/uvyKvjEt2c60ZHugzeh31CygfiAnJ4fp06d3bOHh4Tz00EPccccdTJgwgYyMDFatWkV9fX2X64qKiggNDeXBBx8MjOGKwUfaIlj+ZzjwMXz+x0BboxjiKAHoB8aPH8/OnTvZuXMn27Ztw2q1smrVKpYuXcqePXvYvXs348aN47777uty3c9+9jPOPffcAFmtGLTMuQVm3QAb/gp73gy0NYohzJAaAir/059wZvdvOmjzxAnE/+Y3PW7fk3TQAG+//Tbp6enYbLZ+tVcxBBACzv0/qNwHb/8AosdAwrRAW6UYgigPoJ85Xjro9qf95uZm7r//fu66665TbZ4iWDCY4PIXwBoNr1wFzVWBtkgxBBlSHkBvntQHApfLxerVq48a6rn33nsxGAxcffXVANx999389Kc/JTRU5YBRHIfQWG1m0NPL4PXr4Lp3VHEZRb8ypAQg0PQ0HfTmzZt54403+OUvf0l9fT06nQ6LxcLtt98eKNMVg5WR0+GCR+DNm+GjO7UsogpFP6EEoB/paTro9evXd+zffffdhIaGqh9/xbGZegmUZ8JXD0HcFJhzc6AtUgwRlAD0Ey0tLaxZs4bHHnus49jtt9+O0+lk6dKlwOF00ApFr1nyO6jMgg9+oWURTZwJI2fAyJkwYoJWjlKh6CUqHfQgZij3TdEHHI3w1d+h5Bs4tBOcjdpxQwgkZBwWhJEztJlDquqYwo8qCalQBDuWcFjyv9q+zwe1eXBoOxzaoW3bn4fNfg/TFKbFD0bOOLxFpqmaA4ouKAFQKIIRnQ5ixmhbxmXaMa8HqvcfFoXS7ZogeF3a+ZDIrl5C4kwIHxm4PigCjhIAhWKooDdA3CRtm3GNdszjgsq9hwXh0E7Y8DeQXu18aPxhD6E9rmCLCVgXFKcWJQAKxVDGYDr8Az/7Ju2Yu02bVdQhCjtg/0eAPx4YkaINH7ULQsJ0CLEHxn7FgKIEQKEYbhhDIPk0bWvH0Qjluw8LwqHtkL368Pmo0ZogzLwe0hefepsVA4ISAIVCoQWY0xZpWzuttYcDzId2QN6XkP0e3PyJNutIEfQoAegHcnJyuPzyyzve5+Xlcc8991BaWsq7776LyWRi9OjRPPPMM9jtdgB2797Nd7/7XRobG9HpdGzZsgWLxRKgHigU3WCNgjFLtA2guRIePxNevQq+8wWEjgioeYqTR00U7gd6mw7a4/FwzTXX8Oijj7J3716+/PJLjEZjgHuhUJyA9txELVVabiKPK9AWKU6SIeUBrH99P9XFzf16z5jkUBZfNq7H7XuSDvqTTz4hIyODadO0FL/R0dH9arNCMWCMnAHnPwxv3QIf/QrO+2ugLVKcBMoD6Gd6kg56//79CCFYtmwZM2fO5IEHHjjVZioUfSfjUlj4Y9j6FGx9OtDWKE6CIeUB9OZJfSDoaTpoj8fDhg0b2LJlC1arlSVLljBr1iyWLFkSCLMVit6z5C6o2Asf3KHlIkpdEGiLFH2gRx6AEGK5ECJHCJErhPhVN+dThRCfCSF2CyG+FEIkdTr3gBBirxAiWwjxD+HPiSyEmCWEyPTfs+N4MHO8dNAvvfRSRzropKQkTj/9dGJiYrBaraxYsYLt27cHymyFovfo9HDxU1p6ideuhfriQFuk6AMnFAAhhB54BDgXmARcKYSYdESzB4HnpZQZwD3Aff5rFwALgQxgCjAHOMN/zb+B7wBj/dvyk+1MoDlWOujVq1d3SQe9bNkyMjMzaW1txePxsHbtWiZNOvKfVKEY5ITY4YpXtFQTr14FrtZAW6ToJT3xAE4DcqWUeVJKF/AqcMERbSYBn/v3v+h0XgIWwASYASNQIYRIAMKllF9LLR3p88CFJ9ORQNOeDvqiiy7qOHb77bfT1NTE0qVLmT59OrfddhsAkZGR/OxnP2POnDlMnz6dmTNnsnLlykCZrlD0nRHj4OIntZXFq2+HIMourOhZDCAR6OzflQBzj2izC7gI+DuwCggTQkRLKTcJIb4AygABPCylzBZCzPbfp/M9E/vYh0GBzWajpqamy7Hc3Nxjtr/mmmu45pprBtoshWLgGbdMq1fw2e8hfios+mmgLVL0kP6aBfQL4AwhxA60IZ5SwCuEGANMBJLQfuDPFkL0ah25EOJWIcRWIcTWqipVGFuhGJQs+ilMvgg+/T3s/zjQ1ih6SE8EoBRI7vQ+yX+sAynlISnlRVLKGcBv/cfq0byBr6WUzVLKZuBDYL7/+qTj3bPTvR+XUs6WUs4eMUKtPFQoBiVCaLWL46fCm7dA1f5AW6ToAT0RgC3AWCFEuhDCBFwBrO7cQAgRI4Rov9evgfbJwUVonoFBCGFE8w6ypZRlQKMQYp5/9s91wDv90B+FQhEoTFa44mXQm+CVK6D4m0BbpDgBJxQAKaUHuB34GMgGXpdS7hVC3COEON/f7EwgRwixH4gD7vUffwM4CGSixQl2SSnf9Z/7PvAkkOtv82G/9EihUAQOezJc/iK01cJTS+Hp5ZDzoVbBTDHoUDWBBzFDuW+KIY6zGXa8AJsegYZiiBkPC38EUy8FgznQ1g07jlUTWKWCUCgU/Y85FOZ9D360Ay56UhsWeucH8PdpWmF7R0OgLVSgBKBfyMnJYfr06R1beHg4Dz30EHfccQcTJkwgIyODVatWUV9fD8BLL73Upb1Op2Pnzp0B7YNCMSDojVruoNvWwzVvQcw4WPM7+NsU7bWxLNAWDmvUEFA/4/V6SUxMZPPmzeTk5HD22WdjMBi48847Abj//vu7tM/MzOTCCy/k4MGDR91rsPVNoegXDu2Ar/4BWW+D0MO0y2HBj2DE+EBbNmQ51hDQkEoG98Wzj1NZmNev94xNHcVZN9za4/Y9SQfdmVdeeYUrrriiX2xVKIKCkTPg0meg9ndajGDHi9o2foWWZTRlXqAtHDaoIaB+pifpoDvz2muvddteoRjyRKXDygfhp3vgjF9B0dfw9DJ46luw7301c+gUMKQ8gN48qQ8EPU0H3c7mzZuxWq1MmTLlVJqpUAwubDFw1q+1WUI7XoJN/9SSy0WPhQU/hGlXqJlDA4TyAPqRnqaDbudY3oJCMSwx2WDurfDDHVqqaWMIvPsjeGgqrP8rtNUH2sIhx5DyAALNsdJBr127tks6aACfz8frr7/O+vXrT7WZCsXgRm+AqZfAlIsh70tt2uhnv9dEYNb1MO/7EBHUuSMHDcoD6Cd6kw4aYN26dSQnJzNq1KhAmKtQDH6EgNFnwXVvw3fXaVlHv/43/D0D/vs9qMwOtIVBj5oGOogZyn1TKPpEXQFs+pe2ytjdCmOXaTOHUhdogqHoFrUSWKFQBD+RabDiAfjpXjjrt1C6FZ5dAU+eA1mrwecNtIVBhRIAhUIRfFij4Ixfwk/2wMq/QGs1vH4tPDwHtj4DbkegLQwKlAAoFIrgxWSFObfAD7fDpc+COQze+4k2c2jdg9BWF2gLBzVKABQKRfCj08PkVXDrl3D9u5CQAZ//Af46GT76DdQXn/AWwxE1DVShUAwdhID007WtfA9s/AdsfhS+eQymXKItNoubHGgrBw3KA1AoFEOT+Clw0ePw451w2q2Q/S78ewG8eAnkr4cgmgE5UCgB6Ad6mw7a5XJx4403MnXqVKZNm8aXX34ZUPsViiGNPQWW36flHDr7f6BsJzx3HjxxNux9e1gLgRKAfmD8+PHs3LmTnTt3sm3bNqxWK6tWrWLp0qXs2bOH3bt3M27cuI4cQU888QSgpYJes2YNP//5z/GpxFcKxcBijYLT74CfZMJ5fwNHPfznenhmxbBdVDakYgD17x7EdailX+9pGmnD/u3RPW7fk3TQWVlZnH322QDExsZit9vZunUrp512Wr/arlAousEYArNvgpnXa2moP70LHl2kJZ47/ZfazKJhgvIA+pmepIOeNm0aq1evxuPxkJ+fz7Zt2yguVrMUFIpTik6v5Ra6fStkXA4b/gaPzIWcjwJt2SljSHkAvXlSHwh6mg76pptuIjs7m9mzZ5OamsqCBQvQ6/WBMFmhUNhi4MJ/wYxr4L2fwSuXw4TzYPmfwZ4caOsGlCElAIHmeOmgP/vss4500AaDgb/97W8dbRYsWMC4ceNOub0KhaITqQu0pHNfPwJf3q95A2f+SiturzcG2roBQQ0B9SPHSge9evXqLumgW1tbaWnRYhVr1qzBYDAwadKkU26vQqE4AoMJFv0UfrBZW0uw5n/hsTO0amVDEOUB9BPt6aAfe+yxjmO33347TqeTpUuXAlog+NFHH6WyspJly5ah0+lITEzkhRdeCJTZCoWiOyJT4apXtdKUH/xSK1UZMw5EAJ+Zr3xVK6PZjygB6CdsNhs1NTVdjuXm5nbbNi0tjZycnFNhlkKhOBkmrIT0M7QVxVX7AmvLAJTFVAKgUCgUx8McCmf9JtBWDAgqBqBQKBTDlCEhAMFU1aynDMU+KRSKwUXQC4DFYqGmpmZI/WBKKampqcFisQTaFIVCMYQJ+hhAUlISJSUlVFVVBdqUfsVisZCUlBRoMxQKxRAm6AXAaDSSnt6/U6MUCoViOBD0Q0AKhUKh6BtKABQKhWKYogRAoVAohikimGbPCCGqgMIjDscA1QEwZ6AYav2Bodcn1Z/Bz1Dr08n2J1VKOeLIg0ElAN0hhNgqpZwdaDv6i6HWHxh6fVL9GfwMtT4NVH/UEJBCoVAMU5QAKBQKxTBlKAjA44E2oJ8Zav2Bodcn1Z/Bz1Dr04D0J+hjAAqFQqHoG0PBA1AoFApFH1ACoFAoFMOUoBUAIcRyIUSOECJXCPGrQNvTHwghCoQQmUKInUKIrYG2p7cIIZ4WQlQKIfZ0OhYlhFgjhDjgf40MpI295Rh9ulsIUer/nnYKIVYE0sbeIIRIFkJ8IYTIEkLsFUL82H88KL+n4/QnmL8jixDiGyHELn+ffu8/ni6E2Oz/zXtNCGE66c8KxhiAEEIP7AeWAiXAFuBKKWVWQA07SYQQBcBsKWVQLmARQpwONAPPSymn+I89ANRKKf/sF+pIKeWdgbSzNxyjT3cDzVLKBwNpW18QQiQACVLK7UKIMGAbcCFwA0H4PR2nP5cRvN+RAGxSymYhhBHYAPwY+BnwlpTyVSHEo8AuKeW/T+azgtUDOA3IlVLmSSldwKvABQG2adgjpVwH1B5x+ALgOf/+c2h/nEHDMfoUtEgpy6SU2/37TUA2kEiQfk/H6U/QIjWa/W+N/k0CZwNv+I/3y3cUrAKQCBR3el9CkH/pfiTwiRBimxDi1kAb00/ESSnL/PvlQFwgjelHbhdC7PYPEQXFcMmRCCHSgBnAZobA93REfyCIvyMhhF4IsROoBNYAB4F6KaXH36RffvOCVQCGKouklDOBc4Ef+IcfhgxSG28MvjHHo/k3MBqYDpQBfwmoNX1ACBEKvAn8RErZ2PlcMH5P3fQnqL8jKaVXSjkdSEIb8ZgwEJ8TrAJQCiR3ep/kPxbUSClL/a+VwH/Rvvhgp8I/Tts+XlsZYHtOGillhf8P1Ac8QZB9T/5x5TeBl6SUb/kPB+331F1/gv07akdKWQ98AcwH7EKI9iJe/fKbF6wCsAUY64+Km4ArgNUBtumkEELY/EEshBA24FvAnuNfFRSsBq73718PvBNAW/qF9h9KP6sIou/JH2B8CsiWUv6106mg/J6O1Z8g/45GCCHs/v0QtMku2WhCcIm/Wb98R0E5CwjAP63rIUAPPC2lvDewFp0cQohRaE/9oJXqfDnY+iSEeAU4Ey11bQVwF/A28DqQgpbK+zIpZdAEVY/RpzPRhhYkUAB8t9P4+aBGCLEIWA9kAj7/4d+gjZsH3fd0nP5cSfB+RxloQV492kP661LKe/y/Ea8CUcAO4BoppfOkPitYBUChUCgUJ0ewDgEpFAqF4iRRAqBQKBTDFCUACoVCMUxRAqBQKBTDFCUACoVCMUxRAqBQKBTDFCUACoVCMUz5f5jEYQW3TWDLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cph_urti_no_abs_incdt.predict_survival_function(data_urti_no_abs_incdt)#_train, conditional_after=data_urti_no_abs_incdt_train.duration_urti_admitted)\n",
    "survival_func_plot = cph_urti_no_abs_incdt.predict_survival_function(data_urti_no_abs_incdt)\n",
    "plt.plot(survival_func_plot.loc[:, 720:729])\n",
    "plt.legend(labels=survival_func_plot.loc[:, 720:729].columns, loc =\"lower left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# survival_probability_calibration(cph_urti_no_abs_incdt, data_urti_no_abs_incdt, t0=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>...</th>\n",
       "      <th>7107</th>\n",
       "      <th>7113</th>\n",
       "      <th>7114</th>\n",
       "      <th>7115</th>\n",
       "      <th>7116</th>\n",
       "      <th>7117</th>\n",
       "      <th>7118</th>\n",
       "      <th>7122</th>\n",
       "      <th>7124</th>\n",
       "      <th>7132</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.000425</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.000741</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000851</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.000563</td>\n",
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       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.000637</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.001112</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>0.001318</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>0.001277</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000493</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.000845</td>\n",
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       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.000851</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.001485</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.001760</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>0.001705</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.000658</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.001128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.001492</td>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.002604</td>\n",
       "      <td>0.000606</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>0.003085</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>0.002990</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>0.001154</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.000232</td>\n",
       "      <td>0.001978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.001707</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>0.002978</td>\n",
       "      <td>0.000693</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.003529</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.003420</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.001320</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>0.002262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.002353</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.004105</td>\n",
       "      <td>0.000955</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.000562</td>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.004864</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.004715</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.001820</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.003118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.000836</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>0.004858</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000836</td>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.000941</td>\n",
       "      <td>0.005757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.005580</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.003691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>0.003433</td>\n",
       "      <td>0.001030</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.005989</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.001030</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.001160</td>\n",
       "      <td>0.007097</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000655</td>\n",
       "      <td>0.001109</td>\n",
       "      <td>0.006879</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>0.002655</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.004550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>0.004084</td>\n",
       "      <td>0.001226</td>\n",
       "      <td>0.000658</td>\n",
       "      <td>0.007126</td>\n",
       "      <td>0.001658</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.001226</td>\n",
       "      <td>0.000975</td>\n",
       "      <td>0.001380</td>\n",
       "      <td>0.008444</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>0.001320</td>\n",
       "      <td>0.008184</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>0.003159</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>0.000369</td>\n",
       "      <td>0.000634</td>\n",
       "      <td>0.005413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>0.004302</td>\n",
       "      <td>0.001291</td>\n",
       "      <td>0.000693</td>\n",
       "      <td>0.007506</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.001291</td>\n",
       "      <td>0.001027</td>\n",
       "      <td>0.001454</td>\n",
       "      <td>0.008894</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000821</td>\n",
       "      <td>0.001390</td>\n",
       "      <td>0.008620</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>0.003328</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>0.000389</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>0.005702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.001422</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>0.008266</td>\n",
       "      <td>0.001923</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.001422</td>\n",
       "      <td>0.001131</td>\n",
       "      <td>0.001601</td>\n",
       "      <td>0.009795</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000904</td>\n",
       "      <td>0.001531</td>\n",
       "      <td>0.009493</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>0.003665</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>0.000584</td>\n",
       "      <td>0.000428</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>0.006279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>0.005173</td>\n",
       "      <td>0.001553</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.009026</td>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>0.001553</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>0.001748</td>\n",
       "      <td>0.010695</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000987</td>\n",
       "      <td>0.001672</td>\n",
       "      <td>0.010366</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.004002</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>0.006857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>0.005391</td>\n",
       "      <td>0.001618</td>\n",
       "      <td>0.000869</td>\n",
       "      <td>0.009406</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.001618</td>\n",
       "      <td>0.001287</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.011147</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001029</td>\n",
       "      <td>0.001743</td>\n",
       "      <td>0.010804</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>0.004170</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.000487</td>\n",
       "      <td>0.000837</td>\n",
       "      <td>0.007146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>0.005828</td>\n",
       "      <td>0.001749</td>\n",
       "      <td>0.000939</td>\n",
       "      <td>0.010169</td>\n",
       "      <td>0.002366</td>\n",
       "      <td>0.000311</td>\n",
       "      <td>0.001749</td>\n",
       "      <td>0.001392</td>\n",
       "      <td>0.001969</td>\n",
       "      <td>0.012050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001112</td>\n",
       "      <td>0.001884</td>\n",
       "      <td>0.011679</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>0.004508</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>0.000719</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.000904</td>\n",
       "      <td>0.007725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.0</th>\n",
       "      <td>0.006484</td>\n",
       "      <td>0.001946</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.011313</td>\n",
       "      <td>0.002632</td>\n",
       "      <td>0.000346</td>\n",
       "      <td>0.001946</td>\n",
       "      <td>0.001548</td>\n",
       "      <td>0.002191</td>\n",
       "      <td>0.013405</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001237</td>\n",
       "      <td>0.002096</td>\n",
       "      <td>0.012993</td>\n",
       "      <td>0.000719</td>\n",
       "      <td>0.005015</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.000586</td>\n",
       "      <td>0.001006</td>\n",
       "      <td>0.008594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.0</th>\n",
       "      <td>0.006703</td>\n",
       "      <td>0.002012</td>\n",
       "      <td>0.001080</td>\n",
       "      <td>0.011695</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.002012</td>\n",
       "      <td>0.001601</td>\n",
       "      <td>0.002265</td>\n",
       "      <td>0.013859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001279</td>\n",
       "      <td>0.002166</td>\n",
       "      <td>0.013432</td>\n",
       "      <td>0.000743</td>\n",
       "      <td>0.005185</td>\n",
       "      <td>0.000645</td>\n",
       "      <td>0.000827</td>\n",
       "      <td>0.000606</td>\n",
       "      <td>0.001040</td>\n",
       "      <td>0.008885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>0.006703</td>\n",
       "      <td>0.002012</td>\n",
       "      <td>0.001080</td>\n",
       "      <td>0.011695</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.002012</td>\n",
       "      <td>0.001601</td>\n",
       "      <td>0.002265</td>\n",
       "      <td>0.013859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001279</td>\n",
       "      <td>0.002166</td>\n",
       "      <td>0.013432</td>\n",
       "      <td>0.000743</td>\n",
       "      <td>0.005185</td>\n",
       "      <td>0.000645</td>\n",
       "      <td>0.000827</td>\n",
       "      <td>0.000606</td>\n",
       "      <td>0.001040</td>\n",
       "      <td>0.008885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17 rows × 4895 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         8         9         10        11        13    \\\n",
       "1.0   0.000425  0.000128  0.000068  0.000741  0.000172  0.000023  0.000128   \n",
       "2.0   0.000637  0.000191  0.000103  0.001112  0.000259  0.000034  0.000191   \n",
       "4.0   0.000851  0.000255  0.000137  0.001485  0.000345  0.000045  0.000255   \n",
       "6.0   0.001492  0.000448  0.000240  0.002604  0.000606  0.000080  0.000448   \n",
       "7.0   0.001707  0.000512  0.000275  0.002978  0.000693  0.000091  0.000512   \n",
       "8.0   0.002353  0.000706  0.000379  0.004105  0.000955  0.000126  0.000706   \n",
       "9.0   0.002784  0.000836  0.000449  0.004858  0.001130  0.000149  0.000836   \n",
       "13.0  0.003433  0.001030  0.000553  0.005989  0.001394  0.000183  0.001030   \n",
       "14.0  0.004084  0.001226  0.000658  0.007126  0.001658  0.000218  0.001226   \n",
       "15.0  0.004302  0.001291  0.000693  0.007506  0.001746  0.000230  0.001291   \n",
       "17.0  0.004737  0.001422  0.000763  0.008266  0.001923  0.000253  0.001422   \n",
       "18.0  0.005173  0.001553  0.000833  0.009026  0.002100  0.000276  0.001553   \n",
       "21.0  0.005391  0.001618  0.000869  0.009406  0.002189  0.000288  0.001618   \n",
       "22.0  0.005828  0.001749  0.000939  0.010169  0.002366  0.000311  0.001749   \n",
       "26.0  0.006484  0.001946  0.001045  0.011313  0.002632  0.000346  0.001946   \n",
       "27.0  0.006703  0.002012  0.001080  0.011695  0.002721  0.000358  0.002012   \n",
       "30.0  0.006703  0.002012  0.001080  0.011695  0.002721  0.000358  0.002012   \n",
       "\n",
       "          14        15        16    ...      7107      7113      7114  \\\n",
       "1.0   0.000101  0.000144  0.000878  ...  0.000081  0.000137  0.000851   \n",
       "2.0   0.000152  0.000215  0.001318  ...  0.000122  0.000206  0.001277   \n",
       "4.0   0.000203  0.000288  0.001760  ...  0.000162  0.000275  0.001705   \n",
       "6.0   0.000356  0.000504  0.003085  ...  0.000285  0.000482  0.002990   \n",
       "7.0   0.000408  0.000577  0.003529  ...  0.000326  0.000552  0.003420   \n",
       "8.0   0.000562  0.000795  0.004864  ...  0.000449  0.000760  0.004715   \n",
       "9.0   0.000665  0.000941  0.005757  ...  0.000531  0.000900  0.005580   \n",
       "13.0  0.000820  0.001160  0.007097  ...  0.000655  0.001109  0.006879   \n",
       "14.0  0.000975  0.001380  0.008444  ...  0.000779  0.001320  0.008184   \n",
       "15.0  0.001027  0.001454  0.008894  ...  0.000821  0.001390  0.008620   \n",
       "17.0  0.001131  0.001601  0.009795  ...  0.000904  0.001531  0.009493   \n",
       "18.0  0.001235  0.001748  0.010695  ...  0.000987  0.001672  0.010366   \n",
       "21.0  0.001287  0.001822  0.011147  ...  0.001029  0.001743  0.010804   \n",
       "22.0  0.001392  0.001969  0.012050  ...  0.001112  0.001884  0.011679   \n",
       "26.0  0.001548  0.002191  0.013405  ...  0.001237  0.002096  0.012993   \n",
       "27.0  0.001601  0.002265  0.013859  ...  0.001279  0.002166  0.013432   \n",
       "30.0  0.001601  0.002265  0.013859  ...  0.001279  0.002166  0.013432   \n",
       "\n",
       "          7115      7116      7117      7118      7122      7124      7132  \n",
       "1.0   0.000047  0.000329  0.000041  0.000052  0.000038  0.000066  0.000563  \n",
       "2.0   0.000071  0.000493  0.000061  0.000079  0.000058  0.000099  0.000845  \n",
       "4.0   0.000094  0.000658  0.000082  0.000105  0.000077  0.000132  0.001128  \n",
       "6.0   0.000165  0.001154  0.000144  0.000184  0.000135  0.000232  0.001978  \n",
       "7.0   0.000189  0.001320  0.000164  0.000211  0.000154  0.000265  0.002262  \n",
       "8.0   0.000261  0.001820  0.000227  0.000290  0.000213  0.000365  0.003118  \n",
       "9.0   0.000309  0.002154  0.000268  0.000344  0.000252  0.000432  0.003691  \n",
       "13.0  0.000381  0.002655  0.000330  0.000423  0.000310  0.000533  0.004550  \n",
       "14.0  0.000453  0.003159  0.000393  0.000504  0.000369  0.000634  0.005413  \n",
       "15.0  0.000477  0.003328  0.000414  0.000531  0.000389  0.000668  0.005702  \n",
       "17.0  0.000525  0.003665  0.000456  0.000584  0.000428  0.000735  0.006279  \n",
       "18.0  0.000574  0.004002  0.000498  0.000638  0.000468  0.000803  0.006857  \n",
       "21.0  0.000598  0.004170  0.000519  0.000665  0.000487  0.000837  0.007146  \n",
       "22.0  0.000646  0.004508  0.000561  0.000719  0.000527  0.000904  0.007725  \n",
       "26.0  0.000719  0.005015  0.000624  0.000800  0.000586  0.001006  0.008594  \n",
       "27.0  0.000743  0.005185  0.000645  0.000827  0.000606  0.001040  0.008885  \n",
       "30.0  0.000743  0.005185  0.000645  0.000827  0.000606  0.001040  0.008885  \n",
       "\n",
       "[17 rows x 4895 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph_urti_no_abs_incdt.predict_cumulative_hazard(data_urti_no_abs_incdt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>8</th>\n",
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       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>...</th>\n",
       "      <th>7107</th>\n",
       "      <th>7113</th>\n",
       "      <th>7114</th>\n",
       "      <th>7115</th>\n",
       "      <th>7116</th>\n",
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       "      <th>7122</th>\n",
       "      <th>7124</th>\n",
       "      <th>7132</th>\n",
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       "      <th>1.0</th>\n",
       "      <td>0.999575</td>\n",
       "      <td>0.999873</td>\n",
       "      <td>0.999932</td>\n",
       "      <td>0.999259</td>\n",
       "      <td>0.999828</td>\n",
       "      <td>0.999977</td>\n",
       "      <td>0.999873</td>\n",
       "      <td>0.999899</td>\n",
       "      <td>0.999856</td>\n",
       "      <td>0.999122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999919</td>\n",
       "      <td>0.999863</td>\n",
       "      <td>0.999149</td>\n",
       "      <td>0.999953</td>\n",
       "      <td>0.999671</td>\n",
       "      <td>0.999959</td>\n",
       "      <td>0.999948</td>\n",
       "      <td>0.999962</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>0.999437</td>\n",
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       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.999363</td>\n",
       "      <td>0.999809</td>\n",
       "      <td>0.999897</td>\n",
       "      <td>0.998889</td>\n",
       "      <td>0.999741</td>\n",
       "      <td>0.999966</td>\n",
       "      <td>0.999809</td>\n",
       "      <td>0.999848</td>\n",
       "      <td>0.999785</td>\n",
       "      <td>0.998683</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999878</td>\n",
       "      <td>0.999794</td>\n",
       "      <td>0.998724</td>\n",
       "      <td>0.999929</td>\n",
       "      <td>0.999507</td>\n",
       "      <td>0.999939</td>\n",
       "      <td>0.999921</td>\n",
       "      <td>0.999942</td>\n",
       "      <td>0.999901</td>\n",
       "      <td>0.999156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.999149</td>\n",
       "      <td>0.999745</td>\n",
       "      <td>0.999863</td>\n",
       "      <td>0.998516</td>\n",
       "      <td>0.999655</td>\n",
       "      <td>0.999955</td>\n",
       "      <td>0.999745</td>\n",
       "      <td>0.999797</td>\n",
       "      <td>0.999712</td>\n",
       "      <td>0.998242</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999838</td>\n",
       "      <td>0.999725</td>\n",
       "      <td>0.998296</td>\n",
       "      <td>0.999906</td>\n",
       "      <td>0.999342</td>\n",
       "      <td>0.999918</td>\n",
       "      <td>0.999895</td>\n",
       "      <td>0.999923</td>\n",
       "      <td>0.999868</td>\n",
       "      <td>0.998873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.998509</td>\n",
       "      <td>0.999552</td>\n",
       "      <td>0.999760</td>\n",
       "      <td>0.997400</td>\n",
       "      <td>0.999394</td>\n",
       "      <td>0.999920</td>\n",
       "      <td>0.999552</td>\n",
       "      <td>0.999644</td>\n",
       "      <td>0.999496</td>\n",
       "      <td>0.996919</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999715</td>\n",
       "      <td>0.999518</td>\n",
       "      <td>0.997014</td>\n",
       "      <td>0.999835</td>\n",
       "      <td>0.998846</td>\n",
       "      <td>0.999856</td>\n",
       "      <td>0.999816</td>\n",
       "      <td>0.999865</td>\n",
       "      <td>0.999768</td>\n",
       "      <td>0.998024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.998295</td>\n",
       "      <td>0.999488</td>\n",
       "      <td>0.999725</td>\n",
       "      <td>0.997026</td>\n",
       "      <td>0.999307</td>\n",
       "      <td>0.999909</td>\n",
       "      <td>0.999488</td>\n",
       "      <td>0.999592</td>\n",
       "      <td>0.999423</td>\n",
       "      <td>0.996477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999674</td>\n",
       "      <td>0.999448</td>\n",
       "      <td>0.996585</td>\n",
       "      <td>0.999811</td>\n",
       "      <td>0.998681</td>\n",
       "      <td>0.999836</td>\n",
       "      <td>0.999789</td>\n",
       "      <td>0.999846</td>\n",
       "      <td>0.999735</td>\n",
       "      <td>0.997740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.997650</td>\n",
       "      <td>0.999294</td>\n",
       "      <td>0.999621</td>\n",
       "      <td>0.995904</td>\n",
       "      <td>0.999045</td>\n",
       "      <td>0.999874</td>\n",
       "      <td>0.999294</td>\n",
       "      <td>0.999438</td>\n",
       "      <td>0.999205</td>\n",
       "      <td>0.995148</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999551</td>\n",
       "      <td>0.999240</td>\n",
       "      <td>0.995297</td>\n",
       "      <td>0.999739</td>\n",
       "      <td>0.998182</td>\n",
       "      <td>0.999774</td>\n",
       "      <td>0.999710</td>\n",
       "      <td>0.999787</td>\n",
       "      <td>0.999635</td>\n",
       "      <td>0.996886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.997219</td>\n",
       "      <td>0.999165</td>\n",
       "      <td>0.999551</td>\n",
       "      <td>0.995154</td>\n",
       "      <td>0.998870</td>\n",
       "      <td>0.999851</td>\n",
       "      <td>0.999165</td>\n",
       "      <td>0.999335</td>\n",
       "      <td>0.999060</td>\n",
       "      <td>0.994260</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999469</td>\n",
       "      <td>0.999100</td>\n",
       "      <td>0.994436</td>\n",
       "      <td>0.999691</td>\n",
       "      <td>0.997848</td>\n",
       "      <td>0.999732</td>\n",
       "      <td>0.999657</td>\n",
       "      <td>0.999748</td>\n",
       "      <td>0.999568</td>\n",
       "      <td>0.996316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>0.996573</td>\n",
       "      <td>0.998970</td>\n",
       "      <td>0.999447</td>\n",
       "      <td>0.994029</td>\n",
       "      <td>0.998607</td>\n",
       "      <td>0.999817</td>\n",
       "      <td>0.998970</td>\n",
       "      <td>0.999181</td>\n",
       "      <td>0.998841</td>\n",
       "      <td>0.992928</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999345</td>\n",
       "      <td>0.998891</td>\n",
       "      <td>0.993145</td>\n",
       "      <td>0.999619</td>\n",
       "      <td>0.997348</td>\n",
       "      <td>0.999670</td>\n",
       "      <td>0.999577</td>\n",
       "      <td>0.999690</td>\n",
       "      <td>0.999467</td>\n",
       "      <td>0.995460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>0.995924</td>\n",
       "      <td>0.998775</td>\n",
       "      <td>0.999342</td>\n",
       "      <td>0.992900</td>\n",
       "      <td>0.998343</td>\n",
       "      <td>0.999782</td>\n",
       "      <td>0.998775</td>\n",
       "      <td>0.999025</td>\n",
       "      <td>0.998621</td>\n",
       "      <td>0.991592</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999221</td>\n",
       "      <td>0.998681</td>\n",
       "      <td>0.991849</td>\n",
       "      <td>0.999547</td>\n",
       "      <td>0.996846</td>\n",
       "      <td>0.999607</td>\n",
       "      <td>0.999496</td>\n",
       "      <td>0.999631</td>\n",
       "      <td>0.999366</td>\n",
       "      <td>0.994601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>0.995708</td>\n",
       "      <td>0.998710</td>\n",
       "      <td>0.999307</td>\n",
       "      <td>0.992522</td>\n",
       "      <td>0.998255</td>\n",
       "      <td>0.999770</td>\n",
       "      <td>0.998710</td>\n",
       "      <td>0.998973</td>\n",
       "      <td>0.998547</td>\n",
       "      <td>0.991145</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999179</td>\n",
       "      <td>0.998611</td>\n",
       "      <td>0.991417</td>\n",
       "      <td>0.999523</td>\n",
       "      <td>0.996678</td>\n",
       "      <td>0.999586</td>\n",
       "      <td>0.999469</td>\n",
       "      <td>0.999611</td>\n",
       "      <td>0.999333</td>\n",
       "      <td>0.994314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>0.995274</td>\n",
       "      <td>0.998579</td>\n",
       "      <td>0.999237</td>\n",
       "      <td>0.991768</td>\n",
       "      <td>0.998079</td>\n",
       "      <td>0.999747</td>\n",
       "      <td>0.998579</td>\n",
       "      <td>0.998869</td>\n",
       "      <td>0.998401</td>\n",
       "      <td>0.990253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999096</td>\n",
       "      <td>0.998470</td>\n",
       "      <td>0.990552</td>\n",
       "      <td>0.999475</td>\n",
       "      <td>0.996342</td>\n",
       "      <td>0.999544</td>\n",
       "      <td>0.999416</td>\n",
       "      <td>0.999572</td>\n",
       "      <td>0.999265</td>\n",
       "      <td>0.993740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>0.994840</td>\n",
       "      <td>0.998449</td>\n",
       "      <td>0.999167</td>\n",
       "      <td>0.991015</td>\n",
       "      <td>0.997902</td>\n",
       "      <td>0.999724</td>\n",
       "      <td>0.998449</td>\n",
       "      <td>0.998765</td>\n",
       "      <td>0.998254</td>\n",
       "      <td>0.989362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999013</td>\n",
       "      <td>0.998329</td>\n",
       "      <td>0.989687</td>\n",
       "      <td>0.999427</td>\n",
       "      <td>0.996006</td>\n",
       "      <td>0.999502</td>\n",
       "      <td>0.999362</td>\n",
       "      <td>0.999533</td>\n",
       "      <td>0.999197</td>\n",
       "      <td>0.993167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>0.994623</td>\n",
       "      <td>0.998383</td>\n",
       "      <td>0.999132</td>\n",
       "      <td>0.990638</td>\n",
       "      <td>0.997814</td>\n",
       "      <td>0.999712</td>\n",
       "      <td>0.998383</td>\n",
       "      <td>0.998713</td>\n",
       "      <td>0.998180</td>\n",
       "      <td>0.988915</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998972</td>\n",
       "      <td>0.998259</td>\n",
       "      <td>0.989255</td>\n",
       "      <td>0.999402</td>\n",
       "      <td>0.995838</td>\n",
       "      <td>0.999481</td>\n",
       "      <td>0.999335</td>\n",
       "      <td>0.999513</td>\n",
       "      <td>0.999164</td>\n",
       "      <td>0.992879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>0.994189</td>\n",
       "      <td>0.998252</td>\n",
       "      <td>0.999061</td>\n",
       "      <td>0.989883</td>\n",
       "      <td>0.997637</td>\n",
       "      <td>0.999689</td>\n",
       "      <td>0.998252</td>\n",
       "      <td>0.998609</td>\n",
       "      <td>0.998033</td>\n",
       "      <td>0.988023</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998889</td>\n",
       "      <td>0.998118</td>\n",
       "      <td>0.988389</td>\n",
       "      <td>0.999354</td>\n",
       "      <td>0.995502</td>\n",
       "      <td>0.999439</td>\n",
       "      <td>0.999281</td>\n",
       "      <td>0.999473</td>\n",
       "      <td>0.999096</td>\n",
       "      <td>0.992305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.0</th>\n",
       "      <td>0.993537</td>\n",
       "      <td>0.998056</td>\n",
       "      <td>0.998956</td>\n",
       "      <td>0.988751</td>\n",
       "      <td>0.997371</td>\n",
       "      <td>0.999654</td>\n",
       "      <td>0.998056</td>\n",
       "      <td>0.998453</td>\n",
       "      <td>0.997812</td>\n",
       "      <td>0.986684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998764</td>\n",
       "      <td>0.997907</td>\n",
       "      <td>0.987091</td>\n",
       "      <td>0.999281</td>\n",
       "      <td>0.994997</td>\n",
       "      <td>0.999376</td>\n",
       "      <td>0.999200</td>\n",
       "      <td>0.999414</td>\n",
       "      <td>0.998994</td>\n",
       "      <td>0.991443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.0</th>\n",
       "      <td>0.993320</td>\n",
       "      <td>0.997990</td>\n",
       "      <td>0.998921</td>\n",
       "      <td>0.988373</td>\n",
       "      <td>0.997283</td>\n",
       "      <td>0.999642</td>\n",
       "      <td>0.997990</td>\n",
       "      <td>0.998401</td>\n",
       "      <td>0.997738</td>\n",
       "      <td>0.986237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998722</td>\n",
       "      <td>0.997836</td>\n",
       "      <td>0.986658</td>\n",
       "      <td>0.999257</td>\n",
       "      <td>0.994828</td>\n",
       "      <td>0.999355</td>\n",
       "      <td>0.999173</td>\n",
       "      <td>0.999394</td>\n",
       "      <td>0.998960</td>\n",
       "      <td>0.991155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>0.993320</td>\n",
       "      <td>0.997990</td>\n",
       "      <td>0.998921</td>\n",
       "      <td>0.988373</td>\n",
       "      <td>0.997283</td>\n",
       "      <td>0.999642</td>\n",
       "      <td>0.997990</td>\n",
       "      <td>0.998401</td>\n",
       "      <td>0.997738</td>\n",
       "      <td>0.986237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998722</td>\n",
       "      <td>0.997836</td>\n",
       "      <td>0.986658</td>\n",
       "      <td>0.999257</td>\n",
       "      <td>0.994828</td>\n",
       "      <td>0.999355</td>\n",
       "      <td>0.999173</td>\n",
       "      <td>0.999394</td>\n",
       "      <td>0.998960</td>\n",
       "      <td>0.991155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17 rows × 4895 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         8         9         10        11        13    \\\n",
       "1.0   0.999575  0.999873  0.999932  0.999259  0.999828  0.999977  0.999873   \n",
       "2.0   0.999363  0.999809  0.999897  0.998889  0.999741  0.999966  0.999809   \n",
       "4.0   0.999149  0.999745  0.999863  0.998516  0.999655  0.999955  0.999745   \n",
       "6.0   0.998509  0.999552  0.999760  0.997400  0.999394  0.999920  0.999552   \n",
       "7.0   0.998295  0.999488  0.999725  0.997026  0.999307  0.999909  0.999488   \n",
       "8.0   0.997650  0.999294  0.999621  0.995904  0.999045  0.999874  0.999294   \n",
       "9.0   0.997219  0.999165  0.999551  0.995154  0.998870  0.999851  0.999165   \n",
       "13.0  0.996573  0.998970  0.999447  0.994029  0.998607  0.999817  0.998970   \n",
       "14.0  0.995924  0.998775  0.999342  0.992900  0.998343  0.999782  0.998775   \n",
       "15.0  0.995708  0.998710  0.999307  0.992522  0.998255  0.999770  0.998710   \n",
       "17.0  0.995274  0.998579  0.999237  0.991768  0.998079  0.999747  0.998579   \n",
       "18.0  0.994840  0.998449  0.999167  0.991015  0.997902  0.999724  0.998449   \n",
       "21.0  0.994623  0.998383  0.999132  0.990638  0.997814  0.999712  0.998383   \n",
       "22.0  0.994189  0.998252  0.999061  0.989883  0.997637  0.999689  0.998252   \n",
       "26.0  0.993537  0.998056  0.998956  0.988751  0.997371  0.999654  0.998056   \n",
       "27.0  0.993320  0.997990  0.998921  0.988373  0.997283  0.999642  0.997990   \n",
       "30.0  0.993320  0.997990  0.998921  0.988373  0.997283  0.999642  0.997990   \n",
       "\n",
       "          14        15        16    ...      7107      7113      7114  \\\n",
       "1.0   0.999899  0.999856  0.999122  ...  0.999919  0.999863  0.999149   \n",
       "2.0   0.999848  0.999785  0.998683  ...  0.999878  0.999794  0.998724   \n",
       "4.0   0.999797  0.999712  0.998242  ...  0.999838  0.999725  0.998296   \n",
       "6.0   0.999644  0.999496  0.996919  ...  0.999715  0.999518  0.997014   \n",
       "7.0   0.999592  0.999423  0.996477  ...  0.999674  0.999448  0.996585   \n",
       "8.0   0.999438  0.999205  0.995148  ...  0.999551  0.999240  0.995297   \n",
       "9.0   0.999335  0.999060  0.994260  ...  0.999469  0.999100  0.994436   \n",
       "13.0  0.999181  0.998841  0.992928  ...  0.999345  0.998891  0.993145   \n",
       "14.0  0.999025  0.998621  0.991592  ...  0.999221  0.998681  0.991849   \n",
       "15.0  0.998973  0.998547  0.991145  ...  0.999179  0.998611  0.991417   \n",
       "17.0  0.998869  0.998401  0.990253  ...  0.999096  0.998470  0.990552   \n",
       "18.0  0.998765  0.998254  0.989362  ...  0.999013  0.998329  0.989687   \n",
       "21.0  0.998713  0.998180  0.988915  ...  0.998972  0.998259  0.989255   \n",
       "22.0  0.998609  0.998033  0.988023  ...  0.998889  0.998118  0.988389   \n",
       "26.0  0.998453  0.997812  0.986684  ...  0.998764  0.997907  0.987091   \n",
       "27.0  0.998401  0.997738  0.986237  ...  0.998722  0.997836  0.986658   \n",
       "30.0  0.998401  0.997738  0.986237  ...  0.998722  0.997836  0.986658   \n",
       "\n",
       "          7115      7116      7117      7118      7122      7124      7132  \n",
       "1.0   0.999953  0.999671  0.999959  0.999948  0.999962  0.999934  0.999437  \n",
       "2.0   0.999929  0.999507  0.999939  0.999921  0.999942  0.999901  0.999156  \n",
       "4.0   0.999906  0.999342  0.999918  0.999895  0.999923  0.999868  0.998873  \n",
       "6.0   0.999835  0.998846  0.999856  0.999816  0.999865  0.999768  0.998024  \n",
       "7.0   0.999811  0.998681  0.999836  0.999789  0.999846  0.999735  0.997740  \n",
       "8.0   0.999739  0.998182  0.999774  0.999710  0.999787  0.999635  0.996886  \n",
       "9.0   0.999691  0.997848  0.999732  0.999657  0.999748  0.999568  0.996316  \n",
       "13.0  0.999619  0.997348  0.999670  0.999577  0.999690  0.999467  0.995460  \n",
       "14.0  0.999547  0.996846  0.999607  0.999496  0.999631  0.999366  0.994601  \n",
       "15.0  0.999523  0.996678  0.999586  0.999469  0.999611  0.999333  0.994314  \n",
       "17.0  0.999475  0.996342  0.999544  0.999416  0.999572  0.999265  0.993740  \n",
       "18.0  0.999427  0.996006  0.999502  0.999362  0.999533  0.999197  0.993167  \n",
       "21.0  0.999402  0.995838  0.999481  0.999335  0.999513  0.999164  0.992879  \n",
       "22.0  0.999354  0.995502  0.999439  0.999281  0.999473  0.999096  0.992305  \n",
       "26.0  0.999281  0.994997  0.999376  0.999200  0.999414  0.998994  0.991443  \n",
       "27.0  0.999257  0.994828  0.999355  0.999173  0.999394  0.998960  0.991155  \n",
       "30.0  0.999257  0.994828  0.999355  0.999173  0.999394  0.998960  0.991155  \n",
       "\n",
       "[17 rows x 4895 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph_urti_no_abs_incdt.predict_survival_function(data_urti_no_abs_incdt)#.min()#.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cph_urti_no_abs_incdt.plot_partial_effects_on_outcome(data_urti_no_abs_incdt.event_urti_admitted, values=[0, 2, 4, 6, 8, 10], cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_urti_no_abs_incdt.loc[data_urti_no_abs_incdt.event_urti_admitted == 1]\n",
    "# survival_func_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = cph_urti_no_abs_incdt.baseline_hazard_\n",
    "# cph_urti_no_abs_incdt.baseline_survival_\n",
    "# cph_urti_no_abs_incdt.predict_cumulative_hazard(data_urti_no_abs_incdt, data_urti_no_abs_incdt['duration_urti_admitted']<=30)\n",
    "# cph_urti_no_abs_incdt.predict_survival_function(data_urti_no_abs_incdt)\n",
    "\n",
    "# save \n",
    "np.savetxt('../output/hospitalisation_prediction_urti/sum_betas_urti_no_abs_incdt.txt', a, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 10)\n",
    "                        )\n",
    "cph_urti_no_abs_incdt.plot()\n",
    "plt.suptitle('Coefficients of CPH model for incident hospital admission \\n related to URTI with no antibiotics', fontsize=16)\n",
    "#_ = ax.set_yticklabels(['Age', 'BMI', 'GP consultations', 'CCI', 'IMD', 'Flu vaccine', 'Antibacterial prescriptions'], \n",
    "#                        rotation=45, fontsize=12, ha='right')\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/coefficients_cph_urti_no_abs_incdt.jpg', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concordance index for urti_no_abs_incdt with testing data: 0.6194697529344169\n"
     ]
    }
   ],
   "source": [
    "# concordance index of cph model with testing data\n",
    "concord_index_urti_no_abs_incdt_test = concordance_index(data_urti_no_abs_incdt_test['duration_urti_admitted'], -cph_urti_no_abs_incdt.predict_partial_hazard(data_urti_no_abs_incdt_test), data_urti_no_abs_incdt_test['event_urti_admitted'])\n",
    "print(f'Concordance index for urti_no_abs_incdt with testing data: {concord_index_urti_no_abs_incdt_test}')\n",
    "# print(concordance_index(data_urti_no_abs_incdt_test['event_urti_admitted'], -cph_urti_no_abs_incdt.predict_partial_hazard(data_urti_no_abs_incdt_test), data_urti_no_abs_incdt_test['duration_urti_admitted']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [concord_index_urti_no_abs_incdt_test]\n",
    "np.savetxt('../output/hospitalisation_prediction_urti/concord_index_urti_no_abs_incdt_test.txt', a, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ICI =  0.0004348292627979522\n",
      "E50 =  0.00031631208516691434\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5)\n",
    "                        )\n",
    "survival_probability_calibration(cph_urti_no_abs_incdt, data_urti_no_abs_incdt_train, t0=30)\n",
    "# plt.suptitle('Calibration of CPH model for incident hospital admission related to urti with no antibiotics', fontsize=16)\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/calibration_cph_urti_no_abs_incdt_train.jpg', bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "# t0 (float) – the time to evaluate the probability of event occurring prior at.\n",
    "# ICI – mean absolute difference between predicted and observed\n",
    "# E50 – median absolute difference between predicted and observed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ICI =  0.004079128504346049\n",
      "E50 =  0.002926833953712682\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5)\n",
    "                        )\n",
    "survival_probability_calibration(cph_urti_no_abs_incdt, data_urti_no_abs_incdt_test, t0=30)\n",
    "# plt.suptitle('Calibration of CPH model for incident hospital admission related to URTI with no antibiotics', fontsize=16)\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/calibration_cph_urti_no_abs_incdt_test.jpg', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(cph_urti_no_abs_incdt.score(data_urti_no_abs_incdt_1_test))\n",
    "# print(cph_urti_no_abs_incdt.concordance_index_)\n",
    "# print(cph_urti_no_abs_incdt.AIC_partial_)\n",
    "# print(cph_urti_no_abs_incdt.concordance_index_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4-1-2- Incident hospital admission related to URTI with antibiotics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'msoa', 'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat',\n",
       "       'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine',\n",
       "       'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp',\n",
       "       'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
       "       'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',\n",
       "       'died_date', 'deregistered_date', 'gp_cons_urti', 'gp_cons_urti_ab',\n",
       "       'urti_date', 'incdt_urti_date', 'admitted_urti_date',\n",
       "       'sgss_gp_cov_urti_date', 'date', 'season', 'date_month',\n",
       "       'event_urti_admitted', 'delta_urti_admitted', 'delta_admitted_died',\n",
       "       'delta_admitted_deregistered', 'delta_admitted_died_deregistered',\n",
       "       'duration_urti_admitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_abs_incdt.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(560, 40)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_abs_incdt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep relevant variables\n",
    "data_urti_abs_incdt = data_urti_abs_incdt[['age_cat', 'sex', 'CCI_cat', 'age', 'bmi_imp', 'bmi_imp_cat', \n",
    "                                           'gp_count_imp', 'gp_count_imp_cat', 'flu_vaccine', 'region_imp', 'season', \n",
    "                                           'imd_imp', 'antibacterial_brit_imp', 'ethnicity_imp', 'antibacterial_brit_cat',\n",
    "                                           'smoking_status_imp', 'meds_nonabs', 'meds_nonabs_cat',\n",
    "                                           'event_urti_admitted', 'duration_urti_admitted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_cat                   0\n",
       "sex                       0\n",
       "CCI_cat                   0\n",
       "age                       0\n",
       "bmi_imp                   0\n",
       "bmi_imp_cat               0\n",
       "gp_count_imp              0\n",
       "gp_count_imp_cat          0\n",
       "flu_vaccine               0\n",
       "region_imp                0\n",
       "season                    0\n",
       "imd_imp                   0\n",
       "antibacterial_brit_imp    0\n",
       "ethnicity_imp             0\n",
       "antibacterial_brit_cat    0\n",
       "smoking_status_imp        0\n",
       "meds_nonabs               0\n",
       "meds_nonabs_cat           0\n",
       "event_urti_admitted       0\n",
       "duration_urti_admitted    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing number of missing values in each variable \n",
    "data_urti_abs_incdt.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #drop ros with missing values\n",
    "# data_urti_abs_incdt = data_urti_abs_incdt.dropna(axis='rows')\n",
    "# #data_urti_abs_incdt = data_urti_abs_incdt.drop(columns=['smoking_status'])\n",
    "# data_urti_abs_incdt.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data_urti_abs_incdt for training: (420, 20)\n",
      "shape of data_urti_abs_incdt for testing: (140, 20)\n"
     ]
    }
   ],
   "source": [
    "#randomly splitting data into training (%75) and testing (%25)\n",
    "data_urti_abs_incdt_train, data_urti_abs_incdt_test = train_test_split(data_urti_abs_incdt, test_size=0.25, random_state=42)\n",
    "\n",
    "print(f'shape of data_urti_abs_incdt for training: {data_urti_abs_incdt_train.shape}')\n",
    "print(f'shape of data_urti_abs_incdt for testing: {data_urti_abs_incdt_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'duration_urti_admitted'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'event_urti_admitted'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-8.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2022-05-05 09:19:52 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.Low]</th>\n",
       "      <td>4.73</td>\n",
       "      <td>113.07</td>\n",
       "      <td>11.33</td>\n",
       "      <td>-17.47</td>\n",
       "      <td>26.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.94e+11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.Moderate]</th>\n",
       "      <td>0.06</td>\n",
       "      <td>1.06</td>\n",
       "      <td>11.80</td>\n",
       "      <td>-23.07</td>\n",
       "      <td>23.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.18e+10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.High]</th>\n",
       "      <td>2.25</td>\n",
       "      <td>9.44</td>\n",
       "      <td>11.44</td>\n",
       "      <td>-20.18</td>\n",
       "      <td>24.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.18e+10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.Very high]</th>\n",
       "      <td>-5.26</td>\n",
       "      <td>0.01</td>\n",
       "      <td>14.87</td>\n",
       "      <td>-34.40</td>\n",
       "      <td>23.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.35e+10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.15-24]</th>\n",
       "      <td>-3.33</td>\n",
       "      <td>0.04</td>\n",
       "      <td>10.20</td>\n",
       "      <td>-23.34</td>\n",
       "      <td>16.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.73e+07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.25-34]</th>\n",
       "      <td>-2.59</td>\n",
       "      <td>0.08</td>\n",
       "      <td>17.22</td>\n",
       "      <td>-36.34</td>\n",
       "      <td>31.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.42e+13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.35-44]</th>\n",
       "      <td>-1.53</td>\n",
       "      <td>0.22</td>\n",
       "      <td>10.12</td>\n",
       "      <td>-21.37</td>\n",
       "      <td>18.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.92e+07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.45-54]</th>\n",
       "      <td>5.25</td>\n",
       "      <td>190.01</td>\n",
       "      <td>8.07</td>\n",
       "      <td>-10.57</td>\n",
       "      <td>21.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.41e+09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.5-14]</th>\n",
       "      <td>-2.30</td>\n",
       "      <td>0.10</td>\n",
       "      <td>11.60</td>\n",
       "      <td>-25.04</td>\n",
       "      <td>20.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.52e+08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.55-64]</th>\n",
       "      <td>4.17</td>\n",
       "      <td>64.74</td>\n",
       "      <td>7.77</td>\n",
       "      <td>-11.06</td>\n",
       "      <td>19.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.67e+08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.65-74]</th>\n",
       "      <td>3.93</td>\n",
       "      <td>51.02</td>\n",
       "      <td>7.92</td>\n",
       "      <td>-11.60</td>\n",
       "      <td>19.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.84e+08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.75+]</th>\n",
       "      <td>2.04</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.93</td>\n",
       "      <td>-13.50</td>\n",
       "      <td>17.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.36e+07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi_imp_cat[T.Healthy weight]</th>\n",
       "      <td>3.67</td>\n",
       "      <td>39.43</td>\n",
       "      <td>4.98</td>\n",
       "      <td>-6.08</td>\n",
       "      <td>13.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.79e+05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi_imp_cat[T.Overweight]</th>\n",
       "      <td>-2.58</td>\n",
       "      <td>0.08</td>\n",
       "      <td>7.68</td>\n",
       "      <td>-17.62</td>\n",
       "      <td>12.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.60e+05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi_imp_cat[T.Obese]</th>\n",
       "      <td>0.43</td>\n",
       "      <td>1.54</td>\n",
       "      <td>3.19</td>\n",
       "      <td>-5.81</td>\n",
       "      <td>6.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>797.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnicity_imp[T.Mixed]</th>\n",
       "      <td>5.76</td>\n",
       "      <td>316.74</td>\n",
       "      <td>7.03</td>\n",
       "      <td>-8.01</td>\n",
       "      <td>19.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.03e+08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnicity_imp[T.Other]</th>\n",
       "      <td>5.83</td>\n",
       "      <td>340.53</td>\n",
       "      <td>21.86</td>\n",
       "      <td>-37.02</td>\n",
       "      <td>48.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.38e+21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnicity_imp[T.White]</th>\n",
       "      <td>4.23</td>\n",
       "      <td>68.72</td>\n",
       "      <td>7.19</td>\n",
       "      <td>-9.87</td>\n",
       "      <td>18.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.13e+07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flu_vaccine[T.Yes]</th>\n",
       "      <td>4.16</td>\n",
       "      <td>63.98</td>\n",
       "      <td>4.00</td>\n",
       "      <td>-3.68</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.62e+05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gp_count_imp_cat[T.Some]</th>\n",
       "      <td>-9.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.13</td>\n",
       "      <td>-23.55</td>\n",
       "      <td>4.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>83.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gp_count_imp_cat[T.Many]</th>\n",
       "      <td>-1.09</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.45</td>\n",
       "      <td>-5.88</td>\n",
       "      <td>3.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imd_imp</th>\n",
       "      <td>0.48</td>\n",
       "      <td>1.62</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>2.45</td>\n",
       "      <td>0.23</td>\n",
       "      <td>11.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.East Midlands]</th>\n",
       "      <td>-9.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.02</td>\n",
       "      <td>-64.16</td>\n",
       "      <td>45.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.93e+19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.London]</th>\n",
       "      <td>6.36</td>\n",
       "      <td>577.72</td>\n",
       "      <td>18.07</td>\n",
       "      <td>-29.07</td>\n",
       "      <td>41.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.40e+18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.North East]</th>\n",
       "      <td>0.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>32.26</td>\n",
       "      <td>-62.32</td>\n",
       "      <td>64.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.11e+27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.North West]</th>\n",
       "      <td>-3.59</td>\n",
       "      <td>0.03</td>\n",
       "      <td>31.54</td>\n",
       "      <td>-65.40</td>\n",
       "      <td>58.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.93e+25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.South East]</th>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.68</td>\n",
       "      <td>47.43</td>\n",
       "      <td>-93.33</td>\n",
       "      <td>92.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.60e+40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.South West]</th>\n",
       "      <td>8.19</td>\n",
       "      <td>3605.61</td>\n",
       "      <td>18.36</td>\n",
       "      <td>-27.80</td>\n",
       "      <td>44.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.55e+19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.West Midlands]</th>\n",
       "      <td>-1.43</td>\n",
       "      <td>0.24</td>\n",
       "      <td>27.81</td>\n",
       "      <td>-55.94</td>\n",
       "      <td>53.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.13e+23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_imp[T.Yorkshire and The Humber]</th>\n",
       "      <td>6.99</td>\n",
       "      <td>1082.63</td>\n",
       "      <td>18.10</td>\n",
       "      <td>-28.48</td>\n",
       "      <td>42.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.75e+18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex[T.Male]</th>\n",
       "      <td>1.96</td>\n",
       "      <td>7.08</td>\n",
       "      <td>2.07</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0.12</td>\n",
       "      <td>409.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>79.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>31.13 on 31 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrrr}\n",
       "\\toprule\n",
       "{} &  coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &                          exp(coef) upper 95\\% &  cmp to &     z &    p &  -log2(p) \\\\\n",
       "covariate                              &       &            &           &                 &                 &                      &                                              &         &       &      &           \\\\\n",
       "\\midrule\n",
       "CCI\\_cat[T.Low]                         &  4.73 &     113.07 &     11.33 &          -17.47 &           26.93 &                 0.00 &                              494333919509.35 &    0.00 &  0.42 & 0.68 &      0.56 \\\\\n",
       "CCI\\_cat[T.Moderate]                    &  0.06 &       1.06 &     11.80 &          -23.07 &           23.19 &                 0.00 &                               11809916812.12 &    0.00 &  0.01 & 1.00 &      0.01 \\\\\n",
       "CCI\\_cat[T.High]                        &  2.25 &       9.44 &     11.44 &          -20.18 &           24.67 &                 0.00 &                               51772695821.95 &    0.00 &  0.20 & 0.84 &      0.24 \\\\\n",
       "CCI\\_cat[T.Very high]                   & -5.26 &       0.01 &     14.87 &          -34.40 &           23.88 &                 0.00 &                               23532814823.80 &    0.00 & -0.35 & 0.72 &      0.47 \\\\\n",
       "age\\_cat[T.15-24]                       & -3.33 &       0.04 &     10.20 &          -23.34 &           16.67 &                 0.00 &                                  17299552.50 &    0.00 & -0.33 & 0.74 &      0.43 \\\\\n",
       "age\\_cat[T.25-34]                       & -2.59 &       0.08 &     17.22 &          -36.34 &           31.16 &                 0.00 &                            34220330265442.73 &    0.00 & -0.15 & 0.88 &      0.18 \\\\\n",
       "age\\_cat[T.35-44]                       & -1.53 &       0.22 &     10.12 &          -21.37 &           18.31 &                 0.00 &                                  89193035.69 &    0.00 & -0.15 & 0.88 &      0.18 \\\\\n",
       "age\\_cat[T.45-54]                       &  5.25 &     190.01 &      8.07 &          -10.57 &           21.07 &                 0.00 &                                1410023527.11 &    0.00 &  0.65 & 0.52 &      0.96 \\\\\n",
       "age\\_cat[T.5-14]                        & -2.30 &       0.10 &     11.60 &          -25.04 &           20.44 &                 0.00 &                                 751780954.26 &    0.00 & -0.20 & 0.84 &      0.25 \\\\\n",
       "age\\_cat[T.55-64]                       &  4.17 &      64.74 &      7.77 &          -11.06 &           19.40 &                 0.00 &                                 267277181.43 &    0.00 &  0.54 & 0.59 &      0.76 \\\\\n",
       "age\\_cat[T.65-74]                       &  3.93 &      51.02 &      7.92 &          -11.60 &           19.46 &                 0.00 &                                 283791113.24 &    0.00 &  0.50 & 0.62 &      0.69 \\\\\n",
       "age\\_cat[T.75+]                         &  2.04 &       7.73 &      7.93 &          -13.50 &           17.59 &                 0.00 &                                  43637785.15 &    0.00 &  0.26 & 0.80 &      0.33 \\\\\n",
       "bmi\\_imp\\_cat[T.Healthy weight]          &  3.67 &      39.43 &      4.98 &           -6.08 &           13.43 &                 0.00 &                                    679055.43 &    0.00 &  0.74 & 0.46 &      1.12 \\\\\n",
       "bmi\\_imp\\_cat[T.Overweight]              & -2.58 &       0.08 &      7.68 &          -17.62 &           12.47 &                 0.00 &                                    259976.83 &    0.00 & -0.34 & 0.74 &      0.44 \\\\\n",
       "bmi\\_imp\\_cat[T.Obese]                   &  0.43 &       1.54 &      3.19 &           -5.81 &            6.68 &                 0.00 &                                       797.93 &    0.00 &  0.14 & 0.89 &      0.17 \\\\\n",
       "ethnicity\\_imp[T.Mixed]                 &  5.76 &     316.74 &      7.03 &           -8.01 &           19.53 &                 0.00 &                                 302619422.01 &    0.00 &  0.82 & 0.41 &      1.28 \\\\\n",
       "ethnicity\\_imp[T.Other]                 &  5.83 &     340.53 &     21.86 &          -37.02 &           48.68 &                 0.00 &                    1383739160441291800576.00 &    0.00 &  0.27 & 0.79 &      0.34 \\\\\n",
       "ethnicity\\_imp[T.White]                 &  4.23 &      68.72 &      7.19 &           -9.87 &           18.33 &                 0.00 &                                  91292484.06 &    0.00 &  0.59 & 0.56 &      0.85 \\\\\n",
       "flu\\_vaccine[T.Yes]                     &  4.16 &      63.98 &      4.00 &           -3.68 &           12.00 &                 0.03 &                                    162325.95 &    0.00 &  1.04 & 0.30 &      1.74 \\\\\n",
       "gp\\_count\\_imp\\_cat[T.Some]               & -9.56 &       0.00 &      7.13 &          -23.55 &            4.42 &                 0.00 &                                        83.04 &    0.00 & -1.34 & 0.18 &      2.47 \\\\\n",
       "gp\\_count\\_imp\\_cat[T.Many]               & -1.09 &       0.34 &      2.45 &           -5.88 &            3.71 &                 0.00 &                                        40.81 &    0.00 & -0.44 & 0.66 &      0.61 \\\\\n",
       "imd\\_imp                                &  0.48 &       1.62 &      1.00 &           -1.49 &            2.45 &                 0.23 &                                        11.63 &    0.00 &  0.48 & 0.63 &      0.67 \\\\\n",
       "region\\_imp[T.East Midlands]            & -9.24 &       0.00 &     28.02 &          -64.16 &           45.69 &                 0.00 &                      69334118854002262016.00 &    0.00 & -0.33 & 0.74 &      0.43 \\\\\n",
       "region\\_imp[T.London]                   &  6.36 &     577.72 &     18.07 &          -29.07 &           41.79 &                 0.00 &                       1402852027876021760.00 &    0.00 &  0.35 & 0.72 &      0.46 \\\\\n",
       "region\\_imp[T.North East]               &  0.91 &       2.48 &     32.26 &          -62.32 &           64.13 &                 0.00 &              7114659890142744732437053440.00 &    0.00 &  0.03 & 0.98 &      0.03 \\\\\n",
       "region\\_imp[T.North West]               & -3.59 &       0.03 &     31.54 &          -65.40 &           58.22 &                 0.00 &                19294159445287292461121536.00 &    0.00 & -0.11 & 0.91 &      0.14 \\\\\n",
       "region\\_imp[T.South East]               & -0.38 &       0.68 &     47.43 &          -93.33 &           92.57 &                 0.00 & 15996021469634423007311662264585535094784.00 &    0.00 & -0.01 & 0.99 &      0.01 \\\\\n",
       "region\\_imp[T.South West]               &  8.19 &    3605.61 &     18.36 &          -27.80 &           44.18 &                 0.00 &                      15450875508062918656.00 &    0.00 &  0.45 & 0.66 &      0.61 \\\\\n",
       "region\\_imp[T.West Midlands]            & -1.43 &       0.24 &     27.81 &          -55.94 &           53.08 &                 0.00 &                  113126234104074518659072.00 &    0.00 & -0.05 & 0.96 &      0.06 \\\\\n",
       "region\\_imp[T.Yorkshire and The Humber] &  6.99 &    1082.63 &     18.10 &          -28.48 &           42.46 &                 0.00 &                       2748733694442032640.00 &    0.00 &  0.39 & 0.70 &      0.52 \\\\\n",
       "sex[T.Male]                            &  1.96 &       7.08 &      2.07 &           -2.10 &            6.02 &                 0.12 &                                       409.88 &    0.00 &  0.95 & 0.34 &      1.54 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 420 total observations, 416 right-censored observations>\n",
       "             duration col = 'duration_urti_admitted'\n",
       "                event col = 'event_urti_admitted'\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 420\n",
       "number of events observed = 4\n",
       "   partial log-likelihood = -8.58\n",
       "         time fit was run = 2022-05-05 09:19:52 UTC\n",
       "\n",
       "---\n",
       "                                         coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "covariate                                                                                                                                      \n",
       "CCI_cat[T.Low]                           4.73     113.07      11.33           -17.47            26.93                 0.00             4.94e+11\n",
       "CCI_cat[T.Moderate]                      0.06       1.06      11.80           -23.07            23.19                 0.00             1.18e+10\n",
       "CCI_cat[T.High]                          2.25       9.44      11.44           -20.18            24.67                 0.00             5.18e+10\n",
       "CCI_cat[T.Very high]                    -5.26       0.01      14.87           -34.40            23.88                 0.00             2.35e+10\n",
       "age_cat[T.15-24]                        -3.33       0.04      10.20           -23.34            16.67                 0.00             1.73e+07\n",
       "age_cat[T.25-34]                        -2.59       0.08      17.22           -36.34            31.16                 0.00             3.42e+13\n",
       "age_cat[T.35-44]                        -1.53       0.22      10.12           -21.37            18.31                 0.00             8.92e+07\n",
       "age_cat[T.45-54]                         5.25     190.01       8.07           -10.57            21.07                 0.00             1.41e+09\n",
       "age_cat[T.5-14]                         -2.30       0.10      11.60           -25.04            20.44                 0.00             7.52e+08\n",
       "age_cat[T.55-64]                         4.17      64.74       7.77           -11.06            19.40                 0.00             2.67e+08\n",
       "age_cat[T.65-74]                         3.93      51.02       7.92           -11.60            19.46                 0.00             2.84e+08\n",
       "age_cat[T.75+]                           2.04       7.73       7.93           -13.50            17.59                 0.00             4.36e+07\n",
       "bmi_imp_cat[T.Healthy weight]            3.67      39.43       4.98            -6.08            13.43                 0.00             6.79e+05\n",
       "bmi_imp_cat[T.Overweight]               -2.58       0.08       7.68           -17.62            12.47                 0.00             2.60e+05\n",
       "bmi_imp_cat[T.Obese]                     0.43       1.54       3.19            -5.81             6.68                 0.00               797.93\n",
       "ethnicity_imp[T.Mixed]                   5.76     316.74       7.03            -8.01            19.53                 0.00             3.03e+08\n",
       "ethnicity_imp[T.Other]                   5.83     340.53      21.86           -37.02            48.68                 0.00             1.38e+21\n",
       "ethnicity_imp[T.White]                   4.23      68.72       7.19            -9.87            18.33                 0.00             9.13e+07\n",
       "flu_vaccine[T.Yes]                       4.16      63.98       4.00            -3.68            12.00                 0.03             1.62e+05\n",
       "gp_count_imp_cat[T.Some]                -9.56       0.00       7.13           -23.55             4.42                 0.00                83.04\n",
       "gp_count_imp_cat[T.Many]                -1.09       0.34       2.45            -5.88             3.71                 0.00                40.81\n",
       "imd_imp                                  0.48       1.62       1.00            -1.49             2.45                 0.23                11.63\n",
       "region_imp[T.East Midlands]             -9.24       0.00      28.02           -64.16            45.69                 0.00             6.93e+19\n",
       "region_imp[T.London]                     6.36     577.72      18.07           -29.07            41.79                 0.00             1.40e+18\n",
       "region_imp[T.North East]                 0.91       2.48      32.26           -62.32            64.13                 0.00             7.11e+27\n",
       "region_imp[T.North West]                -3.59       0.03      31.54           -65.40            58.22                 0.00             1.93e+25\n",
       "region_imp[T.South East]                -0.38       0.68      47.43           -93.33            92.57                 0.00             1.60e+40\n",
       "region_imp[T.South West]                 8.19    3605.61      18.36           -27.80            44.18                 0.00             1.55e+19\n",
       "region_imp[T.West Midlands]             -1.43       0.24      27.81           -55.94            53.08                 0.00             1.13e+23\n",
       "region_imp[T.Yorkshire and The Humber]   6.99    1082.63      18.10           -28.48            42.46                 0.00             2.75e+18\n",
       "sex[T.Male]                              1.96       7.08       2.07            -2.10             6.02                 0.12               409.88\n",
       "\n",
       "                                         cmp to     z    p   -log2(p)\n",
       "covariate                                                            \n",
       "CCI_cat[T.Low]                             0.00  0.42 0.68       0.56\n",
       "CCI_cat[T.Moderate]                        0.00  0.01 1.00       0.01\n",
       "CCI_cat[T.High]                            0.00  0.20 0.84       0.24\n",
       "CCI_cat[T.Very high]                       0.00 -0.35 0.72       0.47\n",
       "age_cat[T.15-24]                           0.00 -0.33 0.74       0.43\n",
       "age_cat[T.25-34]                           0.00 -0.15 0.88       0.18\n",
       "age_cat[T.35-44]                           0.00 -0.15 0.88       0.18\n",
       "age_cat[T.45-54]                           0.00  0.65 0.52       0.96\n",
       "age_cat[T.5-14]                            0.00 -0.20 0.84       0.25\n",
       "age_cat[T.55-64]                           0.00  0.54 0.59       0.76\n",
       "age_cat[T.65-74]                           0.00  0.50 0.62       0.69\n",
       "age_cat[T.75+]                             0.00  0.26 0.80       0.33\n",
       "bmi_imp_cat[T.Healthy weight]              0.00  0.74 0.46       1.12\n",
       "bmi_imp_cat[T.Overweight]                  0.00 -0.34 0.74       0.44\n",
       "bmi_imp_cat[T.Obese]                       0.00  0.14 0.89       0.17\n",
       "ethnicity_imp[T.Mixed]                     0.00  0.82 0.41       1.28\n",
       "ethnicity_imp[T.Other]                     0.00  0.27 0.79       0.34\n",
       "ethnicity_imp[T.White]                     0.00  0.59 0.56       0.85\n",
       "flu_vaccine[T.Yes]                         0.00  1.04 0.30       1.74\n",
       "gp_count_imp_cat[T.Some]                   0.00 -1.34 0.18       2.47\n",
       "gp_count_imp_cat[T.Many]                   0.00 -0.44 0.66       0.61\n",
       "imd_imp                                    0.00  0.48 0.63       0.67\n",
       "region_imp[T.East Midlands]                0.00 -0.33 0.74       0.43\n",
       "region_imp[T.London]                       0.00  0.35 0.72       0.46\n",
       "region_imp[T.North East]                   0.00  0.03 0.98       0.03\n",
       "region_imp[T.North West]                   0.00 -0.11 0.91       0.14\n",
       "region_imp[T.South East]                   0.00 -0.01 0.99       0.01\n",
       "region_imp[T.South West]                   0.00  0.45 0.66       0.61\n",
       "region_imp[T.West Midlands]                0.00 -0.05 0.96       0.06\n",
       "region_imp[T.Yorkshire and The Humber]     0.00  0.39 0.70       0.52\n",
       "sex[T.Male]                                0.00  0.95 0.34       1.54\n",
       "---\n",
       "Concordance = 1.00\n",
       "Partial AIC = 79.16\n",
       "log-likelihood ratio test = 31.13 on 31 df\n",
       "-log2(p) of ll-ratio test = 1.12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph_urti_abs_incdt = CoxPHFitter().fit(data_urti_abs_incdt_train,\n",
    "        duration_col='duration_urti_admitted',\n",
    "        event_col='event_urti_admitted',\n",
    "        formula=\"age_cat + sex + CCI_cat + gp_count_imp_cat + flu_vaccine + bmi_imp_cat + region_imp + imd_imp + ethnicity_imp + smoking_status_imp + season\"# + antibacterial_brit_imp + imd_imp + ethnicity_imp + smoking_status_imp + meds_nonabs\" # season +\n",
    "        )\n",
    "cph_urti_abs_incdt.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10231\n"
     ]
    }
   ],
   "source": [
    "output = GetPrintSummary(cph_urti_abs_incdt)\n",
    "\n",
    "with open('../output/hospitalisation_prediction_urti/cph_urti_abs_incdt_summary.html', \"w\") as fo:\n",
    "    print(fo.write(output))\n",
    "    fo.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = cph_urti_abs_incdt.baseline_hazard_\n",
    "# cph_urti_abs_incdt.baseline_survival_\n",
    "# cph_urti_abs_incdt.predict_cumulative_hazard(data_urti_abs_incdt, data_urti_abs_incdt['duration_urti_admitted']<=30)\n",
    "# cph_urti_abs_incdt.predict_survival_function(data_urti_abs_incdt)\n",
    "\n",
    "# save \n",
    "np.savetxt('../output/hospitalisation_prediction_urti/sum_betas_urti_abs_incdt.txt', a, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 9)\n",
    "                        )\n",
    "cph_urti_abs_incdt.plot()\n",
    "plt.suptitle('Coefficients of CPH model for incident hospital admission \\n related to URTI with antibiotics', fontsize=16)\n",
    "#_ = ax.set_yticklabels(['Age', 'BMI', 'GP consultations', 'CCI', 'IMD', 'Flu vaccine', 'Antibacterial prescriptions'], \n",
    "#                        rotation=45, fontsize=12, ha='right')\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/coefficients_cph_urti_abs_incdt.jpg', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concordance index for urti_abs_incdt with testing data: 0.37410071942446044\n"
     ]
    }
   ],
   "source": [
    "# concordance index of cph model with testing data\n",
    "concord_index_urti_abs_incdt_test = concordance_index(data_urti_abs_incdt_test['duration_urti_admitted'], -cph_urti_abs_incdt.predict_partial_hazard(data_urti_abs_incdt_test), data_urti_abs_incdt_test['event_urti_admitted'])\n",
    "print(f'Concordance index for urti_abs_incdt with testing data: {concord_index_urti_abs_incdt_test}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [concord_index_urti_abs_incdt_test]\n",
    "np.savetxt('../output/hospitalisation_prediction_urti/concord_index_urti_abs_incdt_test.txt', a, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ICI =  0.010121532334821657\n",
      "E50 =  4.3300513175026367e-10\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5)\n",
    "                        )\n",
    "survival_probability_calibration(cph_urti_abs_incdt, data_urti_abs_incdt_train, t0=30)\n",
    "# plt.suptitle('Calibration of CPH model for incident hospital admission related to URTI with antibiotics', fontsize=16)\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/calibration_cph_urti_abs_incdt_train.jpg', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ICI =  0.013762422725908805\n",
      "E50 =  2.145939259623475e-09\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:title={'center':'Smoothed calibration curve of \\npredicted vs observed probabilities of t ≤ 30 mortality'}, xlabel='Predicted probability of \\nt ≤ 30 mortality', ylabel='Observed probability of \\nt ≤ 30 mortality'>,\n",
       " 0.013762422725908805,\n",
       " 2.145939259623475e-09)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_probability_calibration(cph_urti_abs_incdt, data_urti_abs_incdt, t0=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ICI =  0.00986652459255793\n",
      "E50 =  3.8355557063773915e-09\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:title={'center':'Smoothed calibration curve of \\npredicted vs observed probabilities of t ≤ 30 mortality'}, xlabel='Predicted probability of \\nt ≤ 30 mortality', ylabel='Observed probability of \\nt ≤ 30 mortality'>,\n",
       " 0.00986652459255793,\n",
       " 3.8355557063773915e-09)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_probability_calibration(cph_urti_abs_incdt, data_urti_abs_incdt_train, t0=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5)\n",
    "                        )\n",
    "survival_probability_calibration(cph_urti_abs_incdt, data_urti_abs_incdt_test, t0=30)\n",
    "# plt.suptitle('Calibration of CPH model for incident hospital admission related to urti with antibiotics', fontsize=16)\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/calibration_cph_urti_abs_incdt_test.jpg', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4-1-3- Prevalent hospital admission related to URTI with no antibiotics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'msoa', 'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat',\n",
       "       'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine',\n",
       "       'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp',\n",
       "       'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
       "       'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',\n",
       "       'died_date', 'deregistered_date', 'gp_cons_urti', 'gp_cons_urti_ab',\n",
       "       'urti_date', 'incdt_urti_date', 'admitted_urti_date',\n",
       "       'sgss_gp_cov_urti_date', 'date', 'season', 'date_month',\n",
       "       'event_urti_admitted', 'delta_urti_admitted', 'delta_admitted_died',\n",
       "       'delta_admitted_deregistered', 'delta_admitted_died_deregistered',\n",
       "       'duration_urti_admitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_no_abs_prevl.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(554, 40)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_no_abs_prevl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep relevant variables\n",
    "data_urti_no_abs_prevl = data_urti_no_abs_prevl[['age_cat', 'sex', 'CCI_cat', 'age', 'bmi_imp', 'bmi_imp_cat', \n",
    "                                                 'gp_count_imp', 'gp_count_imp_cat', 'flu_vaccine', 'region_imp', 'season', \n",
    "                                                 'imd_imp', 'antibacterial_brit_imp', 'ethnicity_imp', 'antibacterial_brit_cat',\n",
    "                                                 'smoking_status_imp', 'meds_nonabs', 'meds_nonabs_cat',\n",
    "                                                 'event_urti_admitted', 'duration_urti_admitted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_cat                   0\n",
       "sex                       0\n",
       "CCI_cat                   0\n",
       "age                       0\n",
       "bmi_imp                   0\n",
       "bmi_imp_cat               0\n",
       "gp_count_imp              0\n",
       "gp_count_imp_cat          0\n",
       "flu_vaccine               0\n",
       "region_imp                0\n",
       "season                    0\n",
       "imd_imp                   0\n",
       "antibacterial_brit_imp    0\n",
       "ethnicity_imp             0\n",
       "antibacterial_brit_cat    0\n",
       "smoking_status_imp        0\n",
       "meds_nonabs               0\n",
       "meds_nonabs_cat           0\n",
       "event_urti_admitted       0\n",
       "duration_urti_admitted    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing number of missing values in each variable \n",
    "data_urti_no_abs_prevl.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data_urti_no_abs_prevl for training: (415, 20)\n",
      "shape of data_urti_no_abs_prevl for testing: (139, 20)\n"
     ]
    }
   ],
   "source": [
    "#randomly splitting data into training (%75) and testing (%25)\n",
    "data_urti_no_abs_prevl_train, data_urti_no_abs_prevl_test = train_test_split(data_urti_no_abs_prevl, test_size=0.25, random_state=42)\n",
    "\n",
    "print(f'shape of data_urti_no_abs_prevl for training: {data_urti_no_abs_prevl_train.shape}')\n",
    "print(f'shape of data_urti_no_abs_prevl for testing: {data_urti_no_abs_prevl_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'duration_urti_admitted'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'event_urti_admitted'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-12.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2022-04-28 19:42:36 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.Low]</th>\n",
       "      <td>17.34</td>\n",
       "      <td>3.38e+07</td>\n",
       "      <td>12063.06</td>\n",
       "      <td>-23625.82</td>\n",
       "      <td>23660.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.Moderate]</th>\n",
       "      <td>17.55</td>\n",
       "      <td>4.20e+07</td>\n",
       "      <td>12063.06</td>\n",
       "      <td>-23625.61</td>\n",
       "      <td>23660.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.High]</th>\n",
       "      <td>17.61</td>\n",
       "      <td>4.47e+07</td>\n",
       "      <td>12063.06</td>\n",
       "      <td>-23625.55</td>\n",
       "      <td>23660.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.Very high]</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.95</td>\n",
       "      <td>14199.85</td>\n",
       "      <td>-27831.25</td>\n",
       "      <td>27831.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.15-24]</th>\n",
       "      <td>-0.32</td>\n",
       "      <td>0.72</td>\n",
       "      <td>18982.08</td>\n",
       "      <td>-37204.51</td>\n",
       "      <td>37203.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.25-34]</th>\n",
       "      <td>-0.90</td>\n",
       "      <td>0.41</td>\n",
       "      <td>21043.47</td>\n",
       "      <td>-41245.35</td>\n",
       "      <td>41243.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.35-44]</th>\n",
       "      <td>-0.76</td>\n",
       "      <td>0.47</td>\n",
       "      <td>19671.10</td>\n",
       "      <td>-38555.41</td>\n",
       "      <td>38553.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.45-54]</th>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.60</td>\n",
       "      <td>19640.29</td>\n",
       "      <td>-38494.77</td>\n",
       "      <td>38493.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.5-14]</th>\n",
       "      <td>18.01</td>\n",
       "      <td>6.63e+07</td>\n",
       "      <td>14375.47</td>\n",
       "      <td>-28157.38</td>\n",
       "      <td>28193.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.55-64]</th>\n",
       "      <td>-0.25</td>\n",
       "      <td>0.78</td>\n",
       "      <td>19048.04</td>\n",
       "      <td>-37333.73</td>\n",
       "      <td>37333.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.65-74]</th>\n",
       "      <td>18.42</td>\n",
       "      <td>9.99e+07</td>\n",
       "      <td>14375.47</td>\n",
       "      <td>-28156.98</td>\n",
       "      <td>28193.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.75+]</th>\n",
       "      <td>18.07</td>\n",
       "      <td>7.03e+07</td>\n",
       "      <td>14375.47</td>\n",
       "      <td>-28157.33</td>\n",
       "      <td>28193.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flu_vaccine[T.Yes]</th>\n",
       "      <td>-17.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10429.64</td>\n",
       "      <td>-20459.40</td>\n",
       "      <td>20424.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gp_count_imp_cat[T.Some]</th>\n",
       "      <td>-17.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5478.31</td>\n",
       "      <td>-10754.83</td>\n",
       "      <td>10719.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gp_count_imp_cat[T.Many]</th>\n",
       "      <td>0.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.26</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>2.53</td>\n",
       "      <td>0.09</td>\n",
       "      <td>12.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex[T.Male]</th>\n",
       "      <td>-0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-3.01</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.05</td>\n",
       "      <td>6.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>57.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>11.04 on 16 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrrr}\n",
       "\\toprule\n",
       "{} &   coef &   exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &  cmp to &     z &    p &  -log2(p) \\\\\n",
       "covariate                &        &             &           &                 &                 &                      &                      &         &       &      &           \\\\\n",
       "\\midrule\n",
       "CCI\\_cat[T.Low]           &  17.34 & 33848253.06 &  12063.06 &       -23625.82 &        23660.50 &                 0.00 &                  inf &    0.00 &  0.00 & 1.00 &      0.00 \\\\\n",
       "CCI\\_cat[T.Moderate]      &  17.55 & 42006688.42 &  12063.06 &       -23625.61 &        23660.72 &                 0.00 &                  inf &    0.00 &  0.00 & 1.00 &      0.00 \\\\\n",
       "CCI\\_cat[T.High]          &  17.61 & 44669368.36 &  12063.06 &       -23625.55 &        23660.78 &                 0.00 &                  inf &    0.00 &  0.00 & 1.00 &      0.00 \\\\\n",
       "CCI\\_cat[T.Very high]     &  -0.05 &        0.95 &  14199.85 &       -27831.25 &        27831.14 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.15-24]         &  -0.32 &        0.72 &  18982.08 &       -37204.51 &        37203.87 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.25-34]         &  -0.90 &        0.41 &  21043.47 &       -41245.35 &        41243.54 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.35-44]         &  -0.76 &        0.47 &  19671.10 &       -38555.41 &        38553.89 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.45-54]         &  -0.52 &        0.60 &  19640.29 &       -38494.77 &        38493.74 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.5-14]          &  18.01 & 66347724.09 &  14375.47 &       -28157.38 &        28193.41 &                 0.00 &                  inf &    0.00 &  0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.55-64]         &  -0.25 &        0.78 &  19048.04 &       -37333.73 &        37333.23 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.65-74]         &  18.42 & 99860873.24 &  14375.47 &       -28156.98 &        28193.81 &                 0.00 &                  inf &    0.00 &  0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.75+]           &  18.07 & 70264735.53 &  14375.47 &       -28157.33 &        28193.46 &                 0.00 &                  inf &    0.00 &  0.00 & 1.00 &      0.00 \\\\\n",
       "flu\\_vaccine[T.Yes]       & -17.69 &        0.00 &  10429.64 &       -20459.40 &        20424.02 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "gp\\_count\\_imp\\_cat[T.Some] & -17.54 &        0.00 &   5478.31 &       -10754.83 &        10719.75 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "gp\\_count\\_imp\\_cat[T.Many] &   0.06 &        1.07 &      1.26 &           -2.40 &            2.53 &                 0.09 &                12.52 &    0.00 &  0.05 & 0.96 &      0.06 \\\\\n",
       "sex[T.Male]              &  -0.56 &        0.57 &      1.25 &           -3.01 &            1.89 &                 0.05 &                 6.61 &    0.00 & -0.45 & 0.65 &      0.61 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 415 total observations, 412 right-censored observations>\n",
       "             duration col = 'duration_urti_admitted'\n",
       "                event col = 'event_urti_admitted'\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 415\n",
       "number of events observed = 3\n",
       "   partial log-likelihood = -12.56\n",
       "         time fit was run = 2022-04-28 19:42:36 UTC\n",
       "\n",
       "---\n",
       "                           coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "covariate                                                                                                                        \n",
       "CCI_cat[T.Low]            17.34   3.38e+07   12063.06        -23625.82         23660.50                 0.00                  inf\n",
       "CCI_cat[T.Moderate]       17.55   4.20e+07   12063.06        -23625.61         23660.72                 0.00                  inf\n",
       "CCI_cat[T.High]           17.61   4.47e+07   12063.06        -23625.55         23660.78                 0.00                  inf\n",
       "CCI_cat[T.Very high]      -0.05       0.95   14199.85        -27831.25         27831.14                 0.00                  inf\n",
       "age_cat[T.15-24]          -0.32       0.72   18982.08        -37204.51         37203.87                 0.00                  inf\n",
       "age_cat[T.25-34]          -0.90       0.41   21043.47        -41245.35         41243.54                 0.00                  inf\n",
       "age_cat[T.35-44]          -0.76       0.47   19671.10        -38555.41         38553.89                 0.00                  inf\n",
       "age_cat[T.45-54]          -0.52       0.60   19640.29        -38494.77         38493.74                 0.00                  inf\n",
       "age_cat[T.5-14]           18.01   6.63e+07   14375.47        -28157.38         28193.41                 0.00                  inf\n",
       "age_cat[T.55-64]          -0.25       0.78   19048.04        -37333.73         37333.23                 0.00                  inf\n",
       "age_cat[T.65-74]          18.42   9.99e+07   14375.47        -28156.98         28193.81                 0.00                  inf\n",
       "age_cat[T.75+]            18.07   7.03e+07   14375.47        -28157.33         28193.46                 0.00                  inf\n",
       "flu_vaccine[T.Yes]       -17.69       0.00   10429.64        -20459.40         20424.02                 0.00                  inf\n",
       "gp_count_imp_cat[T.Some] -17.54       0.00    5478.31        -10754.83         10719.75                 0.00                  inf\n",
       "gp_count_imp_cat[T.Many]   0.06       1.07       1.26            -2.40             2.53                 0.09                12.52\n",
       "sex[T.Male]               -0.56       0.57       1.25            -3.01             1.89                 0.05                 6.61\n",
       "\n",
       "                           cmp to     z    p   -log2(p)\n",
       "covariate                                              \n",
       "CCI_cat[T.Low]               0.00  0.00 1.00       0.00\n",
       "CCI_cat[T.Moderate]          0.00  0.00 1.00       0.00\n",
       "CCI_cat[T.High]              0.00  0.00 1.00       0.00\n",
       "CCI_cat[T.Very high]         0.00 -0.00 1.00       0.00\n",
       "age_cat[T.15-24]             0.00 -0.00 1.00       0.00\n",
       "age_cat[T.25-34]             0.00 -0.00 1.00       0.00\n",
       "age_cat[T.35-44]             0.00 -0.00 1.00       0.00\n",
       "age_cat[T.45-54]             0.00 -0.00 1.00       0.00\n",
       "age_cat[T.5-14]              0.00  0.00 1.00       0.00\n",
       "age_cat[T.55-64]             0.00 -0.00 1.00       0.00\n",
       "age_cat[T.65-74]             0.00  0.00 1.00       0.00\n",
       "age_cat[T.75+]               0.00  0.00 1.00       0.00\n",
       "flu_vaccine[T.Yes]           0.00 -0.00 1.00       0.00\n",
       "gp_count_imp_cat[T.Some]     0.00 -0.00 1.00       0.00\n",
       "gp_count_imp_cat[T.Many]     0.00  0.05 0.96       0.06\n",
       "sex[T.Male]                  0.00 -0.45 0.65       0.61\n",
       "---\n",
       "Concordance = 0.93\n",
       "Partial AIC = 57.11\n",
       "log-likelihood ratio test = 11.04 on 16 df\n",
       "-log2(p) of ll-ratio test = 0.31"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph_urti_no_abs_prevl = CoxPHFitter().fit(data_urti_no_abs_prevl_train,\n",
    "        duration_col='duration_urti_admitted',\n",
    "        event_col='event_urti_admitted',\n",
    "        formula=\"age_cat + sex + CCI_cat + gp_count_imp_cat + flu_vaccine + bmi_imp_cat + region_imp + imd_imp + ethnicity_imp + smoking_status_imp + season\"# + antibacterial_brit_imp + ethnicity_imp + smoking_status_imp + meds_nonabs\" # season +\n",
    "        )\n",
    "cph_urti_no_abs_prevl.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6866\n"
     ]
    }
   ],
   "source": [
    "output = GetPrintSummary(cph_urti_no_abs_prevl)\n",
    "\n",
    "with open('../output/hospitalisation_prediction_urti/cph_urti_no_abs_prevl_summary.html', \"w\") as fo:\n",
    "    print(fo.write(output))\n",
    "    fo.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = cph_urti_no_abs_prevl.baseline_hazard_\n",
    "# cph_urti_no_abs_prevl.baseline_survival_\n",
    "# cph_urti_no_abs_prevl.predict_cumulative_hazard(data_urti_no_abs_prevl, data_urti_no_abs_prevl['duration_urti_admitted']<=30)\n",
    "# cph_urti_no_abs_prevl.predict_survival_function(data_urti_no_abs_prevl)\n",
    "\n",
    "# save \n",
    "np.savetxt('../output/hospitalisation_prediction_urti/sum_betas_urti_no_abs_prevl.txt', a, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 9)\n",
    "                        )\n",
    "cph_urti_no_abs_prevl.plot()\n",
    "plt.suptitle('Coefficients of CPH model for prevalent hospital admission \\n related to URTI with no antibiotics', fontsize=16)\n",
    "#_ = ax.set_yticklabels(['Age', 'BMI', 'GP consultations', 'CCI', 'IMD', 'Flu vaccine', 'Antibacterial prescriptions'], \n",
    "#                        rotation=45, fontsize=12, ha='right')\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/coefficients_cph_urti_no_abs_prevl.jpg', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_urti_no_abs_prevl_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concordance index for cph_urti_no_abs_prevl with testing data: 0.12318840579710146\n"
     ]
    }
   ],
   "source": [
    "# concordance index of cph model with testing data\n",
    "concord_index_urti_no_abs_prevl_test = concordance_index(data_urti_no_abs_prevl_test['duration_urti_admitted'], -cph_urti_no_abs_prevl.predict_partial_hazard(data_urti_no_abs_prevl_test), data_urti_no_abs_prevl_test['event_urti_admitted'])\n",
    "print(f'Concordance index for cph_urti_no_abs_prevl with testing data: {concord_index_urti_no_abs_prevl_test}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [concord_index_urti_no_abs_prevl_test]\n",
    "np.savetxt('../output/hospitalisation_prediction_urti/concord_index_urti_no_abs_prevl_test.txt', a, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ICI =  0.007171901710415653\n",
      "E50 =  3.9823433439778455e-10\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5)\n",
    "                        )\n",
    "survival_probability_calibration(cph_urti_no_abs_prevl, data_urti_no_abs_prevl_train, t0=30)\n",
    "# plt.suptitle('Calibration of CPH model for prevalent hospital admission related to URTI with no antibiotics', fontsize=16)\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/calibration_cph_urti_no_abs_prevl_train.jpg', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, ax = plt.subplots(figsize=(8, 5)\n",
    "#                         )\n",
    "# survival_probability_calibration(cph_urti_no_abs_prevl, data_urti_no_abs_prevl_test, t0=30)\n",
    "# plt.suptitle('Calibration of CPH model for prevalent hospital admission related to URTI with no antibiotics', fontsize=16)\n",
    "# plt.savefig('../output/hospitalisation_prediction_urti/calibration_cph_urti_no_abs_prevl_test.jpg', bbox_inches='tight')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4-1-4- Prevalent hospital admission related to URTI with antibiotics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'msoa', 'age', 'age_cat', 'sex', 'bmi_imp', 'bmi_imp_cat',\n",
       "       'gp_count', 'gp_count_imp_cat', 'gp_count_imp', 'flu_vaccine',\n",
       "       'region_imp', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp',\n",
       "       'ethnicity_imp', 'antibacterial_brit_imp', 'antibacterial_brit_cat',\n",
       "       'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', 'meds_nonabs_cat',\n",
       "       'died_date', 'deregistered_date', 'gp_cons_urti', 'gp_cons_urti_ab',\n",
       "       'urti_date', 'incdt_urti_date', 'admitted_urti_date',\n",
       "       'sgss_gp_cov_urti_date', 'date', 'season', 'date_month',\n",
       "       'event_urti_admitted', 'delta_urti_admitted', 'delta_admitted_died',\n",
       "       'delta_admitted_deregistered', 'delta_admitted_died_deregistered',\n",
       "       'duration_urti_admitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_abs_prevl.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep relevant variables\n",
    "data_urti_abs_prevl = data_urti_abs_prevl[['age_cat', 'sex', 'CCI_cat', 'age', 'bmi_imp', 'bmi_imp_cat', \n",
    "                                           'gp_count_imp', 'gp_count_imp_cat', 'flu_vaccine', 'region_imp', 'season', \n",
    "                                           'imd_imp', 'antibacterial_brit_imp', 'ethnicity_imp', 'antibacterial_brit_cat',\n",
    "                                           'smoking_status_imp', 'meds_nonabs', 'meds_nonabs_cat',\n",
    "                                           'event_urti_admitted', 'duration_urti_admitted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_cat                   0\n",
       "sex                       0\n",
       "CCI_cat                   0\n",
       "age                       0\n",
       "bmi_imp                   0\n",
       "bmi_imp_cat               0\n",
       "gp_count_imp              0\n",
       "gp_count_imp_cat          0\n",
       "flu_vaccine               0\n",
       "region_imp                0\n",
       "season                    0\n",
       "imd_imp                   0\n",
       "antibacterial_brit_imp    0\n",
       "ethnicity_imp             0\n",
       "antibacterial_brit_cat    0\n",
       "smoking_status_imp        0\n",
       "meds_nonabs               0\n",
       "meds_nonabs_cat           0\n",
       "event_urti_admitted       0\n",
       "duration_urti_admitted    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print number of missing values in each variable \n",
    "data_urti_abs_prevl.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data_urti_abs_prevl for training: (48, 20)\n",
      "shape of data_urti_abs_prevl for testing: (12, 20)\n"
     ]
    }
   ],
   "source": [
    "#randomly splitting data into training (%75) and testing (%25)\n",
    "data_urti_abs_prevl_train, data_urti_abs_prevl_test = train_test_split(data_urti_abs_prevl, test_size=0.2)\n",
    "\n",
    "print(f'shape of data_urti_abs_prevl for training: {data_urti_abs_prevl_train.shape}')\n",
    "print(f'shape of data_urti_abs_prevl for testing: {data_urti_abs_prevl_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'duration_urti_admitted'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'event_urti_admitted'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2022-04-28 20:10:14 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.Low]</th>\n",
       "      <td>0.45</td>\n",
       "      <td>1.56</td>\n",
       "      <td>14701.44</td>\n",
       "      <td>-28813.84</td>\n",
       "      <td>28814.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.Moderate]</th>\n",
       "      <td>1.06</td>\n",
       "      <td>2.88</td>\n",
       "      <td>13366.10</td>\n",
       "      <td>-26196.01</td>\n",
       "      <td>26198.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.High]</th>\n",
       "      <td>-3.68</td>\n",
       "      <td>0.03</td>\n",
       "      <td>18754.05</td>\n",
       "      <td>-36760.94</td>\n",
       "      <td>36753.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCI_cat[T.Very high]</th>\n",
       "      <td>10.68</td>\n",
       "      <td>43314.67</td>\n",
       "      <td>23710.37</td>\n",
       "      <td>-46460.79</td>\n",
       "      <td>46482.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.15-24]</th>\n",
       "      <td>-16.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6571.00</td>\n",
       "      <td>-12895.80</td>\n",
       "      <td>12862.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.25-34]</th>\n",
       "      <td>-10.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23996.62</td>\n",
       "      <td>-47042.84</td>\n",
       "      <td>47022.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.35-44]</th>\n",
       "      <td>-3.72</td>\n",
       "      <td>0.02</td>\n",
       "      <td>43114.61</td>\n",
       "      <td>-84506.81</td>\n",
       "      <td>84499.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.45-54]</th>\n",
       "      <td>-6.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19851.11</td>\n",
       "      <td>-38913.75</td>\n",
       "      <td>38901.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.5-14]</th>\n",
       "      <td>-10.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23251.76</td>\n",
       "      <td>-45582.87</td>\n",
       "      <td>45562.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.55-64]</th>\n",
       "      <td>-17.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8341.64</td>\n",
       "      <td>-16366.68</td>\n",
       "      <td>16331.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.65-74]</th>\n",
       "      <td>-4.94</td>\n",
       "      <td>0.01</td>\n",
       "      <td>14261.62</td>\n",
       "      <td>-27957.20</td>\n",
       "      <td>27947.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.75+]</th>\n",
       "      <td>-9.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23015.35</td>\n",
       "      <td>-45118.95</td>\n",
       "      <td>45099.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flu_vaccine[T.Yes]</th>\n",
       "      <td>-4.93</td>\n",
       "      <td>0.01</td>\n",
       "      <td>35687.64</td>\n",
       "      <td>-69951.42</td>\n",
       "      <td>69941.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gp_count_imp_cat[T.Some]</th>\n",
       "      <td>-7.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21887.85</td>\n",
       "      <td>-42906.86</td>\n",
       "      <td>42891.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gp_count_imp_cat[T.Many]</th>\n",
       "      <td>-2.72</td>\n",
       "      <td>0.07</td>\n",
       "      <td>15151.38</td>\n",
       "      <td>-29698.87</td>\n",
       "      <td>29693.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex[T.Male]</th>\n",
       "      <td>-7.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20724.21</td>\n",
       "      <td>-40626.03</td>\n",
       "      <td>40611.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>33.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>6.36 on 16 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrrr}\n",
       "\\toprule\n",
       "{} &   coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &  cmp to &     z &    p &  -log2(p) \\\\\n",
       "covariate                &        &            &           &                 &                 &                      &                      &         &       &      &           \\\\\n",
       "\\midrule\n",
       "CCI\\_cat[T.Low]           &   0.45 &       1.56 &  14701.44 &       -28813.84 &        28814.74 &                 0.00 &                  inf &    0.00 &  0.00 & 1.00 &      0.00 \\\\\n",
       "CCI\\_cat[T.Moderate]      &   1.06 &       2.88 &  13366.10 &       -26196.01 &        26198.13 &                 0.00 &                  inf &    0.00 &  0.00 & 1.00 &      0.00 \\\\\n",
       "CCI\\_cat[T.High]          &  -3.68 &       0.03 &  18754.05 &       -36760.94 &        36753.59 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "CCI\\_cat[T.Very high]     &  10.68 &   43314.67 &  23710.37 &       -46460.79 &        46482.14 &                 0.00 &                  inf &    0.00 &  0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.15-24]         & -16.89 &       0.00 &   6571.00 &       -12895.80 &        12862.03 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.25-34]         & -10.33 &       0.00 &  23996.62 &       -47042.84 &        47022.18 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.35-44]         &  -3.72 &       0.02 &  43114.61 &       -84506.81 &        84499.36 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.45-54]         &  -6.29 &       0.00 &  19851.11 &       -38913.75 &        38901.17 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.5-14]          & -10.26 &       0.00 &  23251.76 &       -45582.87 &        45562.35 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.55-64]         & -17.36 &       0.00 &   8341.64 &       -16366.68 &        16331.95 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.65-74]         &  -4.94 &       0.01 &  14261.62 &       -27957.20 &        27947.32 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "age\\_cat[T.75+]           &  -9.69 &       0.00 &  23015.35 &       -45118.95 &        45099.57 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "flu\\_vaccine[T.Yes]       &  -4.93 &       0.01 &  35687.64 &       -69951.42 &        69941.55 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "gp\\_count\\_imp\\_cat[T.Some] &  -7.46 &       0.00 &  21887.85 &       -42906.86 &        42891.94 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "gp\\_count\\_imp\\_cat[T.Many] &  -2.72 &       0.07 &  15151.38 &       -29698.87 &        29693.43 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "sex[T.Male]              &  -7.33 &       0.00 &  20724.21 &       -40626.03 &        40611.36 &                 0.00 &                  inf &    0.00 & -0.00 & 1.00 &      0.00 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 48 total observations, 47 right-censored observations>\n",
       "             duration col = 'duration_urti_admitted'\n",
       "                event col = 'event_urti_admitted'\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 48\n",
       "number of events observed = 1\n",
       "   partial log-likelihood = -0.69\n",
       "         time fit was run = 2022-04-28 20:10:14 UTC\n",
       "\n",
       "---\n",
       "                           coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "covariate                                                                                                                        \n",
       "CCI_cat[T.Low]             0.45       1.56   14701.44        -28813.84         28814.74                 0.00                  inf\n",
       "CCI_cat[T.Moderate]        1.06       2.88   13366.10        -26196.01         26198.13                 0.00                  inf\n",
       "CCI_cat[T.High]           -3.68       0.03   18754.05        -36760.94         36753.59                 0.00                  inf\n",
       "CCI_cat[T.Very high]      10.68   43314.67   23710.37        -46460.79         46482.14                 0.00                  inf\n",
       "age_cat[T.15-24]         -16.89       0.00    6571.00        -12895.80         12862.03                 0.00                  inf\n",
       "age_cat[T.25-34]         -10.33       0.00   23996.62        -47042.84         47022.18                 0.00                  inf\n",
       "age_cat[T.35-44]          -3.72       0.02   43114.61        -84506.81         84499.36                 0.00                  inf\n",
       "age_cat[T.45-54]          -6.29       0.00   19851.11        -38913.75         38901.17                 0.00                  inf\n",
       "age_cat[T.5-14]          -10.26       0.00   23251.76        -45582.87         45562.35                 0.00                  inf\n",
       "age_cat[T.55-64]         -17.36       0.00    8341.64        -16366.68         16331.95                 0.00                  inf\n",
       "age_cat[T.65-74]          -4.94       0.01   14261.62        -27957.20         27947.32                 0.00                  inf\n",
       "age_cat[T.75+]            -9.69       0.00   23015.35        -45118.95         45099.57                 0.00                  inf\n",
       "flu_vaccine[T.Yes]        -4.93       0.01   35687.64        -69951.42         69941.55                 0.00                  inf\n",
       "gp_count_imp_cat[T.Some]  -7.46       0.00   21887.85        -42906.86         42891.94                 0.00                  inf\n",
       "gp_count_imp_cat[T.Many]  -2.72       0.07   15151.38        -29698.87         29693.43                 0.00                  inf\n",
       "sex[T.Male]               -7.33       0.00   20724.21        -40626.03         40611.36                 0.00                  inf\n",
       "\n",
       "                           cmp to     z    p   -log2(p)\n",
       "covariate                                              \n",
       "CCI_cat[T.Low]               0.00  0.00 1.00       0.00\n",
       "CCI_cat[T.Moderate]          0.00  0.00 1.00       0.00\n",
       "CCI_cat[T.High]              0.00 -0.00 1.00       0.00\n",
       "CCI_cat[T.Very high]         0.00  0.00 1.00       0.00\n",
       "age_cat[T.15-24]             0.00 -0.00 1.00       0.00\n",
       "age_cat[T.25-34]             0.00 -0.00 1.00       0.00\n",
       "age_cat[T.35-44]             0.00 -0.00 1.00       0.00\n",
       "age_cat[T.45-54]             0.00 -0.00 1.00       0.00\n",
       "age_cat[T.5-14]              0.00 -0.00 1.00       0.00\n",
       "age_cat[T.55-64]             0.00 -0.00 1.00       0.00\n",
       "age_cat[T.65-74]             0.00 -0.00 1.00       0.00\n",
       "age_cat[T.75+]               0.00 -0.00 1.00       0.00\n",
       "flu_vaccine[T.Yes]           0.00 -0.00 1.00       0.00\n",
       "gp_count_imp_cat[T.Some]     0.00 -0.00 1.00       0.00\n",
       "gp_count_imp_cat[T.Many]     0.00 -0.00 1.00       0.00\n",
       "sex[T.Male]                  0.00 -0.00 1.00       0.00\n",
       "---\n",
       "Concordance = 0.99\n",
       "Partial AIC = 33.39\n",
       "log-likelihood ratio test = 6.36 on 16 df\n",
       "-log2(p) of ll-ratio test = 0.02"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph_urti_abs_prevl = CoxPHFitter().fit(data_urti_abs_prevl_train,\n",
    "        duration_col='duration_urti_admitted',\n",
    "        event_col='event_urti_admitted',\n",
    "        formula=\"age_cat + sex + CCI_cat + gp_count_imp_cat + flu_vaccine + bmi_imp_cat + region_imp + imd_imp + ethnicity_imp + smoking_status_imp + season\"# + antibacterial_brit_imp + ethnicity_imp + smoking_status_imp + meds_nonabs\" # season +\n",
    "        )\n",
    "cph_urti_abs_prevl.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6868\n"
     ]
    }
   ],
   "source": [
    "output = GetPrintSummary(cph_urti_abs_prevl)\n",
    "\n",
    "with open('../output/hospitalisation_prediction_urti/cph_urti_abs_prevl_summary.html', \"w\") as fo:\n",
    "    print(fo.write(output))\n",
    "    fo.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = cph_urti_abs_prevl.baseline_hazard_\n",
    "\n",
    "# save \n",
    "np.savetxt('../output/hospitalisation_prediction_urti/sum_betas_urti_abs_prevl.txt', a, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 9)\n",
    "                        )\n",
    "cph_urti_no_abs_prevl.plot()\n",
    "plt.suptitle('Coefficients of CPH model for prevalent hospital admission \\n related to URTI with antibiotics', fontsize=16)\n",
    "#_ = ax.set_yticklabels(['Age', 'BMI', 'GP consultations', 'CCI', 'IMD', 'Flu vaccine', 'Antibacterial prescriptions'], \n",
    "#                        rotation=45, fontsize=12, ha='right')\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/coefficients_cph_urti_abs_prevl.jpg', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concordance index of cph model with testing data\n",
    "concord_index_urti_abs_prevl_test = concordance_index(data_urti_abs_prevl_test['duration_urti_admitted'], -cph_urti_abs_prevl.predict_partial_hazard(data_urti_abs_prevl_test), data_urti_abs_prevl_test['event_urti_admitted'])\n",
    "print(f'Concordance index for cph_urti_abs_prevl with testing data: {concord_index_urti_abs_prevl_test}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [concord_index_urti_abs_prevl_test]\n",
    "np.savetxt('../output/hospitalisation_prediction_urti/concord_index_urti_abs_prevl_test.txt', a, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5)\n",
    "                        )\n",
    "survival_probability_calibration(cph_urti_no_abs_prevl, data_urti_abs_prevl_train, t0=30)\n",
    "# plt.suptitle('Calibration of CPH model for prevalent hospital admission related to URTI with antibiotics', fontsize=16)\n",
    "plt.savefig('../output/hospitalisation_prediction_urti/calibration_cph_urti_abs_prevl_train.jpg', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, ax = plt.subplots(figsize=(8, 5)\n",
    "#                         )\n",
    "# survival_probability_calibration(cph_urti_no_abs_prevl, data_urti_abs_prevl_test, t0=30)\n",
    "# plt.suptitle('Calibration of CPH model for prevalent hospital admission related to URTI with antibiotics', fontsize=16)\n",
    "# plt.savefig('../output/hospitalisation_prediction_urti/calibration_cph_urti_abs_prevl_test.jpg', bbox_inches='tight')\n",
    "# plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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