analysis/hospitalisation_prediction_urti.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": 25,
   "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",
    "# import sklearn\n",
    "# from sklearn.model_selection import train_test_split\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",
    "import io\n",
    "import sys\n",
    "from contextlib import redirect_stdout\n",
    "# import miceforest as mf\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "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": 28,
   "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": 29,
   "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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def create_lineplot(data, var, title, legend_title, ax1):\n",
    "#     a = data.groupby(['date', var]).size().reset_index()\n",
    "#     a.columns = ['date', var, 'Freq']\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('Frequency')\n",
    "#     ax1.xaxis.set_tick_params(which='both', labelbottom=True, labeltop=False, rotation=90)\n",
    "#     ax1.legend(title=legend_title)\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": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def create_lineplot_hue(var, hue, title, legend_title, ax1):\n",
    "#     a = data_inclusions_exlusions.groupby(['date', var, hue]).size().reset_index()\n",
    "#     a.columns = ['date', var, hue, 'Freq']\n",
    "#     a = a[a[var] != 0]\n",
    "#     sns.lineplot(x='date', y='Freq', \n",
    "#             hue=hue, \n",
    "#             data=a,\n",
    "#             ax=ax1)\n",
    "#     ax1.set_title(title)\n",
    "#     ax1.set_ylabel('Frequency')\n",
    "#     ax1.xaxis.set_tick_params(which='both', labelbottom=True, labeltop=False, rotation=90)\n",
    "#     ax1.legend(title=legend_title)\n",
    "#     ax1.axvline(x=\"2020-01\", color='black', ls='--', lw=1.5)\n",
    "#     ax1.axvline(x=\"2021-03\", color='black', ls='--', lw=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## 1- Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data2019_month = {}\n",
    "# data2020_month = {}\n",
    "# data2021_month = {}\n",
    "# data2019 = []\n",
    "# data2020 = []\n",
    "# data2021 = []\n",
    "# for  i in range(1, 13):\n",
    "#     # data of 2019\n",
    "#     input_file_path = makeFilePath(\"output/hospitalisation_data/\", \"input_hospitalisation_2019-\"+str(i).zfill(2)+\"-01.csv.gz\")\n",
    "#     data2019_month[\"%s\" %i] = pd.read_csv(input_file_path, compression='gzip')\n",
    "#     data2019_month[\"%s\" %i]['date'] = \"2019-\"+str(i).zfill(2)\n",
    "#     if (i<3) or (i>11):\n",
    "#         data2019_month[\"%s\" %i]['season'] = \"Winter\"\n",
    "#     elif (i>2) and (i<6):\n",
    "#         data2019_month[\"%s\" %i]['season'] = \"Spring\"\n",
    "#     elif (i>5) and (i<9):\n",
    "#         data2019_month[\"%s\" %i]['season'] = \"Summer\"\n",
    "#     elif (i>8) and (i<12):\n",
    "#         data2019_month[\"%s\" %i]['season'] = \"Autumn\"\n",
    "#     data2019.append(data2019_month[\"%s\" %i])\n",
    "#     # data of 2020\n",
    "#     input_file_path = makeFilePath(\"output/hospitalisation_data/\", \"input_hospitalisation_2020-\"+str(i).zfill(2)+\"-01.csv.gz\")\n",
    "#     data2020_month[\"%s\" %i] = pd.read_csv(input_file_path, compression='gzip')\n",
    "#     data2020_month[\"%s\" %i]['date'] = \"2020-\"+str(i).zfill(2)\n",
    "#     if (i<3) or (i>11):\n",
    "#         data2020_month[\"%s\" %i]['season'] = \"Winter\"\n",
    "#     elif (i>2) and (i<6):\n",
    "#         data2020_month[\"%s\" %i]['season'] = \"Spring\"\n",
    "#     elif (i>5) and (i<9):\n",
    "#         data2020_month[\"%s\" %i]['season'] = \"Summer\"\n",
    "#     elif (i>8) and (i<12):\n",
    "#         data2020_month[\"%s\" %i]['season'] = \"Autumn\"\n",
    "#     data2020.append(data2020_month[\"%s\" %i])\n",
    "#     # data of 2021\n",
    "#     input_file_path = makeFilePath(\"output/hospitalisation_data/\", \"input_hospitalisation_2021-\"+str(i).zfill(2)+\"-01.csv.gz\")\n",
    "#     data2021_month[\"%s\" %i] = pd.read_csv(input_file_path, compression='gzip')\n",
    "#     data2021_month[\"%s\" %i]['date'] = \"2021-\"+str(i).zfill(2)\n",
    "#     if (i<3) or (i>11):\n",
    "#         data2021_month[\"%s\" %i]['season'] = \"Winter\"\n",
    "#     elif (i>2) and (i<6):\n",
    "#         data2021_month[\"%s\" %i]['season'] = \"Spring\"\n",
    "#     elif (i>5) and (i<9):\n",
    "#         data2021_month[\"%s\" %i]['season'] = \"Summer\"\n",
    "#     elif (i>8) and (i<12):\n",
    "#         data2021_month[\"%s\" %i]['season'] = \"Autumn\"\n",
    "#     data2021.append(data2021_month[\"%s\" %i])\n",
    "    \n",
    "# data2019 = pd.concat(data2019_month, axis=0, ignore_index=True)\n",
    "# data2020 = pd.concat(data2020_month, axis=0, ignore_index=True)\n",
    "# data2021 = pd.concat(data2021_month, axis=0, ignore_index=True)\n",
    "\n",
    "# data = data2019.append([data2020, data2021], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data2019_month = {}\n",
    "# # data2020_month = {}\n",
    "# # data2021_month = {}\n",
    "# # data2022_month = {}\n",
    "# data2019 = []\n",
    "# # data2020 = []\n",
    "# # data2021 = []\n",
    "# # data2022 = []\n",
    "# i=1\n",
    "# while  i < 13:\n",
    "#     # data of 2019\n",
    "#     input_file_path = makeFilePath(\"output/hospitalisation_data/\", \"input_hospitalisation_2019-\"+str(i).zfill(2)+\"-01.csv.gz\")\n",
    "#     data2019_month[\"%s\" %i] = pd.read_csv(input_file_path, compression='gzip')\n",
    "#     data2019_month[\"%s\" %i]['date'] = \"2019-\"+str(i).zfill(2)\n",
    "#     if (i<3) or (i>11):\n",
    "#         data2019_month[\"%s\" %i]['season'] = \"Winter\"\n",
    "#     elif (i>2) and (i<6):\n",
    "#         data2019_month[\"%s\" %i]['season'] = \"Spring\"\n",
    "#     elif (i>5) and (i<9):\n",
    "#         data2019_month[\"%s\" %i]['season'] = \"Summer\"\n",
    "#     elif (i>8) and (i<12):\n",
    "#         data2019_month[\"%s\" %i]['season'] = \"Autumn\"\n",
    "#     data2019.append(data2019_month[\"%s\" %i])\n",
    "#     i=i+1\n",
    "    \n",
    "# # i=1\n",
    "# # while  i < 13:    \n",
    "# #     # data of 2020\n",
    "# #     input_file_path = makeFilePath(\"output/hospitalisation_data/\", \"input_hospitalisation_2020-\"+str(i).zfill(2)+\"-01.csv.gz\")\n",
    "# #     data2020_month[\"%s\" %i] = pd.read_csv(input_file_path, compression='gzip')\n",
    "# #     data2020_month[\"%s\" %i]['date'] = \"2020-\"+str(i).zfill(2)\n",
    "# #     if (i<3) or (i>11):\n",
    "# #         data2020_month[\"%s\" %i]['season'] = \"Winter\"\n",
    "# #     elif (i>2) and (i<6):\n",
    "# #         data2020_month[\"%s\" %i]['season'] = \"Spring\"\n",
    "# #     elif (i>5) and (i<9):\n",
    "# #         data2020_month[\"%s\" %i]['season'] = \"Summer\"\n",
    "# #     elif (i>8) and (i<12):\n",
    "# #         data2020_month[\"%s\" %i]['season'] = \"Autumn\"\n",
    "# #     data2020.append(data2020_month[\"%s\" %i])\n",
    "# #     i=i+1\n",
    "\n",
    "# # i=1\n",
    "# # while  i < 13:\n",
    "# #     # data of 2021\n",
    "# #     input_file_path = makeFilePath(\"output/hospitalisation_data/\", \"input_hospitalisation_2021-\"+str(i).zfill(2)+\"-01.csv.gz\")\n",
    "# #     data2021_month[\"%s\" %i] = pd.read_csv(input_file_path, compression='gzip')\n",
    "# #     data2021_month[\"%s\" %i]['date'] = \"2021-\"+str(i).zfill(2)\n",
    "# #     if (i<3) or (i>11):\n",
    "# #         data2021_month[\"%s\" %i]['season'] = \"Winter\"\n",
    "# #     elif (i>2) and (i<6):\n",
    "# #         data2021_month[\"%s\" %i]['season'] = \"Spring\"\n",
    "# #     elif (i>5) and (i<9):\n",
    "# #         data2021_month[\"%s\" %i]['season'] = \"Summer\"\n",
    "# #     elif (i>8) and (i<12):\n",
    "# #         data2021_month[\"%s\" %i]['season'] = \"Autumn\"\n",
    "# #     data2021.append(data2021_month[\"%s\" %i])\n",
    "# #     i=i+1\n",
    "\n",
    "# # i=1\n",
    "# # while  i < 4:\n",
    "# #     # data of 2022\n",
    "# #     input_file_path = makeFilePath(\"output/hospitalisation_data/\", \"input_hospitalisation_2022-\"+str(i).zfill(2)+\"-01.csv.gz\")\n",
    "# #     data2022_month[\"%s\" %i] = pd.read_csv(input_file_path, compression='gzip')\n",
    "# #     data2022_month[\"%s\" %i]['date'] = \"2022-\"+str(i).zfill(2)\n",
    "# #     if (i<3) or (i>11):\n",
    "# #         data2022_month[\"%s\" %i]['season'] = \"Winter\"\n",
    "# #     elif (i>2) and (i<6):\n",
    "# #         data2022_month[\"%s\" %i]['season'] = \"Spring\"\n",
    "# #     elif (i>5) and (i<9):\n",
    "# #         data2022_month[\"%s\" %i]['season'] = \"Summer\"\n",
    "# #     elif (i>8) and (i<12):\n",
    "# #         data2022_month[\"%s\" %i]['season'] = \"Autumn\"\n",
    "# #     data2022.append(data2022_month[\"%s\" %i])\n",
    "# #     i=i+1\n",
    "    \n",
    "# data2019 = pd.concat(data2019_month, axis=0, ignore_index=True)\n",
    "# # data2020 = pd.concat(data2020_month, axis=0, ignore_index=True)\n",
    "# # data2021 = pd.concat(data2021_month, axis=0, ignore_index=True)\n",
    "# # data2022 = pd.concat(data2022_month, axis=0, ignore_index=True)\n",
    "\n",
    "# # data = data2019.append([data2020, data2021, data2022], ignore_index=True)\n",
    "\n",
    "# data = data2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data2019_month = {}\n",
    "# data2019 = []\n",
    "\n",
    "# i=1\n",
    "# while  i < 13:\n",
    "#     # data of 2019\n",
    "#     # if (i<4):\n",
    "#     data2019_month[\"%s\" %i] = pd.read_csv(f'../output/hospitalisation_data/input_hospitalisation_2019-'+str(i).zfill(2)+'-01.csv.gz')\n",
    "#     data2019_month[\"%s\" %i]['date'] = \"2019-\"+str(i).zfill(2)\n",
    "#     # if (i>3) and (i<7):\n",
    "#     #     data2019_month[\"%s\" %i] = pd.read_csv(f'output/hospitalisation_data/input_hospitalisatio2019-'+str(i).zfill(2)+'-01.csv.gz')\n",
    "#     #     data2019_month[\"%s\" %i]['date'] = \"2019-\"+str(i).zfill(2)\n",
    "#     if (i<3) or (i>11):\n",
    "#         data2019_month[\"%s\" %i]['season'] = \"Winter\"\n",
    "#     elif (i>2) and (i<6):\n",
    "#         data2019_month[\"%s\" %i]['season'] = \"Spring\"\n",
    "#     elif (i>5) and (i<9):\n",
    "#         data2019_month[\"%s\" %i]['season'] = \"Summer\"\n",
    "#     elif (i>8) and (i<12):\n",
    "#         data2019_month[\"%s\" %i]['season'] = \"Autumn\"\n",
    "\n",
    "#     data2019.append(data2019_month[\"%s\" %i])\n",
    "#     i=i+1\n",
    "    \n",
    "# data2019 = pd.concat(data2019_month, axis=0, ignore_index=True)\n",
    "\n",
    "# data = data2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data201901 = pd.read_csv(f'../output/hospitalisation_data/input_hospitalisation_2019-01-01.csv.gz')\n",
    "# data201901['date'] = \"2019-01\"\n",
    "# data201901['season'] = \"Winter\"\n",
    "data201902 = pd.read_csv(f'../output/hospitalisation_data/input_hospitalisation_2019-02-01.csv.gz')\n",
    "# data201902['date'] = \"2019-02\"\n",
    "# data201902['season'] = \"Winter\"\n",
    "data201903 = pd.read_csv(f'../output/hospitalisation_data/input_hospitalisation_2019-03-01.csv.gz')\n",
    "# data201903['date'] = \"2019-03\"\n",
    "# data201903['season'] = \"Spring\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2019 = [data201901, data201902, data201903]\n",
    "data = pd.concat(data2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = data201901"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data2019 = [data201901, data201902]\n",
    "# data = pd.concat(data2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = data.loc[data['sex'] == 'F']\n",
    "# data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <td>697</td>\n",
       "      <td>685</td>\n",
       "      <td>1089</td>\n",
       "      <td>417</td>\n",
       "      <td>331</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>top</th>\n",
       "      <td>2021-05</td>\n",
       "      <td>2021-10</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>2018-05</td>\n",
       "      <td>2019-11-22</td>\n",
       "      <td>2019-03-16</td>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>2022-02-06</td>\n",
       "      <td>2022-03-19</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>223</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>97</td>\n",
       "      <td>106</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>15</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>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>49932.344700</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>0.300015</td>\n",
       "      <td>0.300015</td>\n",
       "      <td>0.300015</td>\n",
       "      <td>0.300015</td>\n",
       "      <td>0.300015</td>\n",
       "      <td>0.300015</td>\n",
       "      <td>0.300015</td>\n",
       "      <td>0.300015</td>\n",
       "      <td>0.300015</td>\n",
       "      <td>28875.097568</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",
       "      <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>25.000000</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>24878.500000</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>49800.500000</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>74829.500000</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>99998.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bmi_date_measured smoking_status_date  \\\n",
       "count               7500                1000   \n",
       "unique                40                  40   \n",
       "top              2021-05             2021-10   \n",
       "freq                 223                  40   \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",
       "       most_recent_unclear_smoking_cat_date flu_vaccine_med  \\\n",
       "count                                  1000            1000   \n",
       "unique                                   40              13   \n",
       "top                                 2019-04         2018-09   \n",
       "freq                                     35              97   \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 sgss_positive primary_care_covid admitted_date  \\\n",
       "count                  1000          1000               1000          3000   \n",
       "unique                   13           697                685          1089   \n",
       "top                 2018-05    2019-11-22         2019-03-16    2020-07-23   \n",
       "freq                    106             4                  4             9   \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",
       "       deregistered_date   died_date  ... hemiplegia_comor     hiv_comor  \\\n",
       "count                500        1000  ...     10000.000000  10000.000000   \n",
       "unique               417         331  ...              NaN           NaN   \n",
       "top           2022-02-06  2022-03-19  ...              NaN           NaN   \n",
       "freq                   4          15  ...              NaN           NaN   \n",
       "mean                 NaN         NaN  ...         0.100000      0.100000   \n",
       "std                  NaN         NaN  ...         0.300015      0.300015   \n",
       "min                  NaN         NaN  ...         0.000000      0.000000   \n",
       "25%                  NaN         NaN  ...         0.000000      0.000000   \n",
       "50%                  NaN         NaN  ...         0.000000      0.000000   \n",
       "75%                  NaN         NaN  ...         0.000000      0.000000   \n",
       "max                  NaN         NaN  ...         1.000000      1.000000   \n",
       "\n",
       "       metastatic_cancer_comor mild_liver_comor mod_severe_liver_comor  \\\n",
       "count             10000.000000     10000.000000           10000.000000   \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                   0.300015         0.300015               0.300015   \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",
       "       mod_severe_renal_comor      mi_comor peptic_ulcer_comor  \\\n",
       "count            10000.000000  10000.000000       10000.000000   \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                  0.300015      0.300015           0.300015   \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",
       "       peripheral_vascular_comor    patient_id  \n",
       "count               10000.000000  10000.000000  \n",
       "unique                       NaN           NaN  \n",
       "top                          NaN           NaN  \n",
       "freq                         NaN           NaN  \n",
       "mean                    0.100000  49932.344700  \n",
       "std                     0.300015  28875.097568  \n",
       "min                     0.000000     25.000000  \n",
       "25%                     0.000000  24878.500000  \n",
       "50%                     0.000000  49800.500000  \n",
       "75%                     0.000000  74829.500000  \n",
       "max                     1.000000  99998.000000  \n",
       "\n",
       "[11 rows x 85 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "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": 72,
   "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": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age_cat'] = pd.cut(data['age'], right=False, bins=[0,18,60,200], labels=['0-17 years', '18-59 years', '>59 years'])"
   ]
  },
  {
   "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": "code",
   "execution_count": 74,
   "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": [
    "#### URTI-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23, 9)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_1 = data[['Age_cat', #'msoa', #'date', \n",
    "                #     'age', 'age_cat', 'sex', 'bmi_imp', 'BMI_cat', 'gp_count', 'gp_count_cat', 'gp_count_imp', 'flu_vaccine', \n",
    "                #    'region', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp', 'ethnicity_imp', 'antibacterial_brit_imp',\n",
    "                #    'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs', #'season', \n",
    "                   'died_date', 'deregistered_date', \n",
    "                    '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",
    "\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": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22, 9)"
      ]
     },
     "execution_count": 76,
     "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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'died_date', 'deregistered_date', 'gp_cons_urti_1',\n",
       "       'gp_cons_urti_ab_1', 'urti_date_1', 'incdt_urti_date_1',\n",
       "       'admitted_urti_date_1', 'sgss_gp_cov_urti_date_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### URTI-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23, 9)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_2 = data[['Age_cat', #'msoa', #'date',\n",
    "                #     'age', 'age_cat', 'sex', 'bmi_imp', 'BMI_cat', 'gp_count', 'gp_count_cat', 'gp_count_imp', 'flu_vaccine', \n",
    "                #    'region', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp', 'ethnicity_imp', 'antibacterial_brit_imp',\n",
    "                #    'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs',  #'season',\n",
    "                   'died_date', 'deregistered_date', \n",
    "                    '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",
    "\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": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 9)"
      ]
     },
     "execution_count": 79,
     "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": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'died_date', 'deregistered_date', 'gp_cons_urti_2',\n",
       "       'gp_cons_urti_ab_2', 'urti_date_2', 'incdt_urti_date_2',\n",
       "       'admitted_urti_date_2', 'sgss_gp_cov_urti_date_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### URTI_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 9)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_3 = data[['Age_cat', #'msoa', #'date',\n",
    "                #     'age', 'age_cat', 'sex', 'bmi_imp', 'BMI_cat', 'gp_count', 'gp_count_cat', 'gp_count_imp', 'flu_vaccine', \n",
    "                #    'region', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp', 'ethnicity_imp', 'antibacterial_brit_imp',\n",
    "                #    'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs',  #'season',\n",
    "                   'died_date', 'deregistered_date', \n",
    "                    '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",
    "\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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 9)"
      ]
     },
     "execution_count": 82,
     "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": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'died_date', 'deregistered_date', 'gp_cons_urti_3',\n",
       "       'gp_cons_urti_ab_3', 'urti_date_3', 'incdt_urti_date_3',\n",
       "       'admitted_urti_date_3', 'sgss_gp_cov_urti_date_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_3.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### URTI_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 9)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_4 = data[['Age_cat', #'msoa', #'date',\n",
    "                #     'age', 'age_cat', 'sex', 'bmi_imp', 'BMI_cat', 'gp_count', 'gp_count_cat', 'gp_count_imp', 'flu_vaccine', \n",
    "                #    'region', 'smoking_status_imp', 'antibacterial_brit', 'imd_imp', 'ethnicity_imp', 'antibacterial_brit_imp',\n",
    "                #    'hx_hosp', 'CCI_cat', 'CCI', 'meds_nonabs',  #'season',\n",
    "                   'died_date', 'deregistered_date', \n",
    "                   '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",
    "\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": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23, 9)"
      ]
     },
     "execution_count": 85,
     "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": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'died_date', 'deregistered_date', 'gp_cons_urti_4',\n",
       "       'gp_cons_urti_ab_4', 'urti_date_4', 'incdt_urti_date_4',\n",
       "       'admitted_urti_date_4', 'sgss_gp_cov_urti_date_4'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_4.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combining URTI_1, URTI_2, URTI_3, and URTI_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83, 9)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_1.rename(columns={'urti_date_1': 'urti_date', 'incdt_urti_date_1': 'incdt_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', 'incdt_urti_date_2': 'incdt_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', 'incdt_urti_date_3': 'incdt_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', 'incdt_urti_date_4': 'incdt_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": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, axes = plt.subplots(7,2,\n",
    "#                         figsize=(15, 33)\n",
    "#                         )\n",
    "# create_lineplot(data_urti, 'sex', 'Sex', 'Sex', axes[0,0])\n",
    "# create_lineplot(data_urti, 'Age_cat', 'Age category', 'Age category', axes[0,1])\n",
    "# create_lineplot(data_urti, 'imd_imp', 'IMD', 'IMD', axes[1,0])\n",
    "# create_lineplot(data_urti, 'ethnicity_imp', 'Ethnicity', 'Ethnicity', axes[1,1])\n",
    "# create_lineplot(data_urti, 'smoking_status_imp', 'Smoking status', 'Smoking status', axes[2,0])\n",
    "# create_lineplot(data_urti, 'BMI_cat', 'BMI', 'BMI', axes[2,1])\n",
    "# create_lineplot(data_urti, 'CCI_cat', 'CCI', 'CCI', axes[3,0])\n",
    "# create_lineplot(data_urti, 'gp_count_cat', 'GP consultation', 'GP consultation', axes[3,1])\n",
    "# create_lineplot(data_urti, 'region', 'Region', 'Region', axes[4,0])\n",
    "# create_lineplot(data_urti, 'flu_vaccine', 'Flu vaccine', 'Flu vaccine', axes[4,1])\n",
    "# create_lineplot(data_urti, 'antibacterial_brit', 'Antibacterial prescriptions', 'Antibacterial prescriptions', axes[5,0])\n",
    "# create_lineplot(data_urti, 'msoa', 'msoa', 'MSOA', axes[5,1])\n",
    "# create_lineplot(data_urti, 'meds_nonabs', 'Medicines excpet antibacterials', 'Medicines excpet antibacterials', axes[6,0])\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": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83, 9)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti['urti_date'] = data_urti['urti_date'].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": 90,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    62\n",
       "1.0     1\n",
       "Name: event_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scenario 2\n",
    "# become a case (uncensoring)\n",
    "#calculating days between infection dx and hosp\n",
    "data_urti['delta_urti_admitted'] = (data_urti['admitted_urti_date'] - data_urti['urti_date']).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": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "685.0     1\n",
       "43.0      1\n",
       "506.0     1\n",
       "240.0     1\n",
       "1073.0    1\n",
       "861.0     1\n",
       "1107.0    1\n",
       "374.0     1\n",
       "865.0     1\n",
       "878.0     1\n",
       "1061.0    1\n",
       "873.0     1\n",
       "379.0     1\n",
       "427.0     1\n",
       "129.0     1\n",
       "444.0     1\n",
       "393.0     1\n",
       "964.0     1\n",
       "13.0      1\n",
       "88.0      1\n",
       "1008.0    1\n",
       "Name: delta_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 92,
     "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": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    64\n",
       "1.0     1\n",
       "Name: event_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 93,
     "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['event_urti_admitted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    64\n",
       "1.0     1\n",
       "Name: event_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 94,
     "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) & (data_urti['delta_admitted_deregistered'] < 30)), 'event_urti_admitted'] = 0\n",
    "data_urti['event_urti_admitted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 95,
     "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": 96,
   "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": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    82\n",
       "1.0     1\n",
       "Name: event_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 97,
     "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": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.0    82\n",
       "13.0     1\n",
       "Name: duration_urti_admitted, dtype: int64"
      ]
     },
     "execution_count": 98,
     "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": 99,
   "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>delta_urti_admitted</th>\n",
       "      <th>urti_date</th>\n",
       "      <th>admitted_urti_date</th>\n",
       "      <th>delta_urti_admitted</th>\n",
       "      <th>delta_admitted_died_deregistered</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, admitted_urti_date, delta_urti_admitted, delta_admitted_died_deregistered, duration_urti_admitted]\n",
       "Index: []"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti[data_urti['duration_urti_admitted']<0][['delta_urti_admitted', 'urti_date', 'admitted_urti_date', 'delta_urti_admitted', 'delta_admitted_died_deregistered', 'duration_urti_admitted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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": "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": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data_urti_no_abs_incdt: (65, 15)\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": "code",
   "execution_count": 102,
   "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>Age_cat</th>\n",
       "      <th>died_date</th>\n",
       "      <th>deregistered_date</th>\n",
       "      <th>gp_cons_urti</th>\n",
       "      <th>gp_cons_urti_ab</th>\n",
       "      <th>urti_date</th>\n",
       "      <th>incdt_urti_date</th>\n",
       "      <th>admitted_urti_date</th>\n",
       "      <th>sgss_gp_cov_urti_date</th>\n",
       "      <th>event_urti_admitted</th>\n",
       "      <th>delta_urti_admitted</th>\n",
       "      <th>delta_admitted_died</th>\n",
       "      <th>delta_admitted_deregistered</th>\n",
       "      <th>delta_admitted_died_deregistered</th>\n",
       "      <th>duration_urti_admitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18-59 years</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2019-06-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&gt;59 years</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18-59 years</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18-59 years</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18-59 years</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-12-06</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>18-59 years</td>\n",
       "      <td>2022-03-26</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0-17 years</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-19</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>18-59 years</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-26</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>18-59 years</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-29</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>18-59 years</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age_cat  died_date deregistered_date gp_cons_urti gp_cons_urti_ab  \\\n",
       "0   18-59 years        NaT               NaT   2019-06-02             NaN   \n",
       "1     >59 years        NaT               NaT          NaN             NaN   \n",
       "2   18-59 years        NaT               NaT   2020-03-29             NaN   \n",
       "3   18-59 years        NaT               NaT          NaN             NaN   \n",
       "5   18-59 years        NaT               NaT          NaN             NaN   \n",
       "..          ...        ...               ...          ...             ...   \n",
       "77  18-59 years 2022-03-26               NaT          NaN             NaN   \n",
       "78   0-17 years        NaT               NaT   2020-02-19             NaN   \n",
       "79  18-59 years        NaT               NaT          NaN             NaN   \n",
       "81  18-59 years        NaT               NaT          NaN             NaN   \n",
       "82  18-59 years        NaT               NaT          NaN             NaN   \n",
       "\n",
       "    urti_date  incdt_urti_date admitted_urti_date  sgss_gp_cov_urti_date  \\\n",
       "0  2019-01-03                0                NaT                      0   \n",
       "1  2019-01-07                0                NaT                      0   \n",
       "2  2019-01-31                0                NaT                      0   \n",
       "3  2019-01-05                0                NaT                      0   \n",
       "5  2019-01-21                0         2020-12-06                      0   \n",
       "..        ...              ...                ...                    ...   \n",
       "77 2019-01-10                0                NaT                      0   \n",
       "78 2019-01-19                0                NaT                      0   \n",
       "79 2019-01-26                0                NaT                      0   \n",
       "81 2019-01-29                0                NaT                      0   \n",
       "82 2019-01-04                0                NaT                      0   \n",
       "\n",
       "    event_urti_admitted  delta_urti_admitted  delta_admitted_died  \\\n",
       "0                   0.0                  NaN                  NaN   \n",
       "1                   0.0                  NaN                  NaN   \n",
       "2                   0.0                  NaN                  NaN   \n",
       "3                   0.0                  NaN                  NaN   \n",
       "5                   0.0                685.0                  NaN   \n",
       "..                  ...                  ...                  ...   \n",
       "77                  0.0                  NaN                  NaN   \n",
       "78                  0.0                  NaN                  NaN   \n",
       "79                  0.0                  NaN                  NaN   \n",
       "81                  0.0                  NaN                  NaN   \n",
       "82                  0.0                  NaN                  NaN   \n",
       "\n",
       "    delta_admitted_deregistered  delta_admitted_died_deregistered  \\\n",
       "0                           NaN                               NaN   \n",
       "1                           NaN                               NaN   \n",
       "2                           NaN                               NaN   \n",
       "3                           NaN                               NaN   \n",
       "5                           NaN                               NaN   \n",
       "..                          ...                               ...   \n",
       "77                          NaN                               NaN   \n",
       "78                          NaN                               NaN   \n",
       "79                          NaN                               NaN   \n",
       "81                          NaN                               NaN   \n",
       "82                          NaN                               NaN   \n",
       "\n",
       "    duration_urti_admitted  \n",
       "0                     30.0  \n",
       "1                     30.0  \n",
       "2                     30.0  \n",
       "3                     30.0  \n",
       "5                     30.0  \n",
       "..                     ...  \n",
       "77                    30.0  \n",
       "78                    30.0  \n",
       "79                    30.0  \n",
       "81                    30.0  \n",
       "82                    30.0  \n",
       "\n",
       "[65 rows x 15 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_no_abs_incdt"
   ]
  },
  {
   "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": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'died_date', 'deregistered_date', 'gp_cons_urti',\n",
       "       'gp_cons_urti_ab', 'urti_date', 'incdt_urti_date', 'admitted_urti_date',\n",
       "       'sgss_gp_cov_urti_date', 'event_urti_admitted', 'delta_urti_admitted',\n",
       "       'delta_admitted_died', 'delta_admitted_deregistered',\n",
       "       'delta_admitted_died_deregistered', 'duration_urti_admitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_no_abs_incdt.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65, 15)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_no_abs_incdt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep relevant variables\n",
    "# data_urti_no_abs_incdt = data_urti_no_abs_incdt[['age', 'age_cat', 'sex', 'bmi_imp', 'BMI_cat', 'gp_count_imp', 'flu_vaccine',\n",
    "#                                                'region', 'antibacterial_brit_imp', 'imd_imp', 'ethnicity_imp', 'CCI_cat', 'CCI',\n",
    "#                                                'event_urti_admitted', 'duration_urti_admitted']]\n",
    "\n",
    "data_urti_no_abs_incdt = data_urti_no_abs_incdt[['Age_cat',\n",
    "                                                #  'age', 'bmi_imp', 'gp_count_imp', 'flu_vaccine', 'sex', 'region', #'season', \n",
    "                                                #  'imd_imp', 'antibacterial_brit_imp', 'ethnicity_imp', 'smoking_status_imp',\n",
    "                                                #  'meds_nonabs',\n",
    "                                                 'event_urti_admitted', 'duration_urti_admitted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_cat                   0\n",
       "event_urti_admitted       0\n",
       "duration_urti_admitted    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "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": 107,
   "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": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age_cat', 'event_urti_admitted', 'duration_urti_admitted'], dtype='object')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_urti_no_abs_incdt.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data_urti_no_abs_incdt for training: (49, 3)\n",
      "shape of data_urti_no_abs_incdt for testing: (16, 3)\n"
     ]
    }
   ],
   "source": [
    "fold_thre = len(data_urti_no_abs_incdt)-round(len(data_urti_no_abs_incdt)/4)\n",
    "data_urti_no_abs_incdt_train = data_urti_no_abs_incdt.iloc[:fold_thre]\n",
    "data_urti_no_abs_incdt_test = data_urti_no_abs_incdt.iloc[fold_thre:]\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": 119,
   "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>penalizer</th>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l1 ratio</th>\n",
       "      <td>0.0</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>49</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>-3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2022-04-11 11:25:54 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>Age_cat[T.18-59 years]</th>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.82</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.14</td>\n",
       "      <td>3.56</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>Age_cat[T.&gt;59 years]</th>\n",
       "      <td>-0.29</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.88</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.14</td>\n",
       "      <td>4.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.42</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>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>11.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>0.25 on 2 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>0.18</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",
       "Age\\_cat[T.18-59 years] & -0.34 &       0.71 &      0.82 &           -1.95 &            1.27 &                 0.14 &                 3.56 &    0.00 & -0.42 & 0.68 &      0.56 \\\\\n",
       "Age\\_cat[T.>59 years]   & -0.29 &       0.75 &      0.88 &           -2.00 &            1.43 &                 0.14 &                 4.19 &    0.00 & -0.33 & 0.74 &      0.42 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 49 total observations, 48 right-censored observations>\n",
       "             duration col = 'duration_urti_admitted'\n",
       "                event col = 'event_urti_admitted'\n",
       "                penalizer = 0.1\n",
       "                 l1 ratio = 0.0\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 49\n",
       "number of events observed = 1\n",
       "   partial log-likelihood = -3.76\n",
       "         time fit was run = 2022-04-11 11:25:54 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",
       "Age_cat[T.18-59 years]  -0.34       0.71       0.82            -1.95             1.27                 0.14                 3.56\n",
       "Age_cat[T.>59 years]    -0.29       0.75       0.88            -2.00             1.43                 0.14                 4.19\n",
       "\n",
       "                         cmp to     z    p   -log2(p)\n",
       "covariate                                            \n",
       "Age_cat[T.18-59 years]     0.00 -0.42 0.68       0.56\n",
       "Age_cat[T.>59 years]       0.00 -0.33 0.74       0.42\n",
       "---\n",
       "Concordance = 0.91\n",
       "Partial AIC = 11.53\n",
       "log-likelihood ratio test = 0.25 on 2 df\n",
       "-log2(p) of ll-ratio test = 0.18"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph_urti_no_abs_incdt = CoxPHFitter(penalizer=.2)\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\"\n",
    "        # \"age + sex + bmi_imp + gp_count_imp + flu_vaccine + region + antibacterial_brit_imp + imd_imp + ethnicity_imp + smoking_status_imp + meds_nonabs\" # + season\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": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2870\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": 112,
   "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": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 4)\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.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "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['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'])\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": 169,
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cph_urti_no_abs_incdt_predicted = cph_urti_no_abs_incdt.predict_partial_hazard(data_urti_no_abs_incdt)\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(8, 5))\n",
    "# fig = sm.qqplot(cph_urti_no_abs_incdt_predicted, line='45', ax=ax)\n",
    "# plt.suptitle('Q-Q plot for calibration of CPH model for incident hospital \\n admission related to URTI with no antibiotics', fontsize=16)\n",
    "# plt.savefig('../output/hospitalisation_prediction_urti/qq_calibration_cph_urti_no_abs_incdt.jpg', bbox_inches='tight')\n",
    "# # pylab.show()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_urti_abs_incdt.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_urti_abs_incdt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #keep relevant variables\n",
    "# data_urti_abs_incdt = data_urti_abs_incdt[['age', 'bmi_imp', 'gp_count_imp', 'flu_vaccine', 'sex', 'region', #'season', \n",
    "#                                          'imd_imp', 'antibacterial_brit_imp', 'ethnicity_imp', 'smoking_status_imp',\n",
    "#                                          'meds_nonabs',\n",
    "#                                          'event_urti_admitted', 'duration_urti_admitted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # printing number of missing values in each variable \n",
    "# data_urti_abs_incdt.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fold_thre = len(data_urti_abs_incdt)-round(len(data_urti_abs_incdt)/4)\n",
    "# data_urti_abs_incdt_train = data_urti_abs_incdt.iloc[:fold_thre]\n",
    "# data_urti_abs_incdt_test = data_urti_abs_incdt.iloc[fold_thre:]\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": 5,
   "metadata": {},
   "outputs": [],
   "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 + sex + bmi_imp + gp_count_imp + flu_vaccine + region + 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": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8608\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": null,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # concordance index of cph model with testing data\n",
    "# concord_index_urti_abs_incdt_test = concordance_index(data_urti_abs_incdt_test['event_urti_admitted'], -cph_urti_abs_incdt.predict_partial_hazard(data_urti_abs_incdt_test), data_urti_abs_incdt_test['duration_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": 8,
   "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": 9,
   "metadata": {},
   "outputs": [],
   "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": null,
   "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": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cph_urti_abs_incdt_predicted = cph_urti_abs_incdt.predict_partial_hazard(data_urti_abs_incdt)\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(8, 5))\n",
    "# fig = sm.qqplot(cph_urti_abs_incdt_predicted, line='45', ax=ax)\n",
    "# plt.suptitle('Q-Q plot for calibration of CPH model for incident hospital \\n admission related to URTI with antibiotics', fontsize=16)\n",
    "# plt.savefig('../output/hospitalisation_prediction_urti/qq_calibration_cph_urti_abs_incdt.jpg', bbox_inches='tight')\n",
    "# # pylab.show()\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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_urti_no_abs_prevl.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_urti_no_abs_prevl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #keep relevant variables\n",
    "# data_urti_no_abs_prevl = data_urti_no_abs_prevl[['age', 'bmi_imp', 'gp_count_imp', 'flu_vaccine', 'sex', 'region', #'season', \n",
    "#                                                 'imd_imp', 'antibacterial_brit_imp', 'ethnicity_imp', 'smoking_status_imp',\n",
    "#                                                 'meds_nonabs',\n",
    "#                                                 'event_urti_admitted', 'duration_urti_admitted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # printing number of missing values in each variable \n",
    "# data_urti_no_abs_prevl.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #drop rows with missing values\n",
    "# data_urti_no_abs_prevl = data_urti_no_abs_prevl.dropna(axis='rows')\n",
    "# #data_urti_no_abs_prevl = data_urti_no_abs_prevl.drop(columns=['smoking_status'])\n",
    "# data_urti_no_abs_prevl.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_urti_no_abs_prevl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fold_thre = len(data_urti_no_abs_prevl)-round(len(data_urti_no_abs_prevl)/4)\n",
    "# data_urti_no_abs_prevl_train = data_urti_no_abs_prevl.iloc[:fold_thre]\n",
    "# data_urti_no_abs_prevl_test = data_urti_no_abs_prevl.iloc[fold_thre:]\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": 16,
   "metadata": {},
   "outputs": [],
   "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 + sex + bmi_imp + gp_count_imp + flu_vaccine + region + antibacterial_brit_imp + imd_imp + ethnicity_imp + smoking_status_imp + meds_nonabs\" # season +\n",
    "#         )\n",
    "# cph_urti_no_abs_prevl.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [],
   "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_urti_no_abs_prevl_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "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['event_urti_admitted'], -cph_urti_no_abs_prevl.predict_partial_hazard(data_urti_no_abs_prevl_test), data_urti_no_abs_prevl_test['duration_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": 22,
   "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": 23,
   "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_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": 24,
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cph_urti_no_abs_prevl_predicted = cph_urti_no_abs_prevl.predict_partial_hazard(data_urti_no_abs_prevl)\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(8, 5))\n",
    "# fig = sm.qqplot(cph_urti_no_abs_prevl_predicted, line='45', ax=ax)\n",
    "# plt.suptitle('Q-Q plot for calibration of CPH model for prevalent hospital \\n admission related to URTI with no antibiotics', fontsize=16)\n",
    "# plt.savefig('../output/hospitalisation_prediction_urti/qq_calibration_cph_urti_no_abs_prevl.jpg', bbox_inches='tight')\n",
    "# # pylab.show()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4-1-4- Prevalent hospital admission related to URTI with antibiotics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_urti_abs_prevl.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #keep relevant variables\n",
    "# data_urti_abs_prevl = data_urti_abs_prevl[['age', 'age_cat', 'sex', 'bmi', 'BMI_cat', 'gp_count', 'flu_vaccine',\n",
    "#                                         'region', 'antibacterial_brit', 'imd', 'ethnicity', 'CCI_cat', 'CCI',\n",
    "#                                         'event_urti_admitted', 'duration_urti_admitted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # print number of missing values in each variable \n",
    "# data_urti_abs_prevl.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #drop ros with missing values\n",
    "# data_urti_abs_prevl = data_urti_abs_prevl.dropna(axis='rows')\n",
    "# #data_urti_abs_prevl = data_urti_abs_prevl.drop(columns=['smoking_status'])\n",
    "# data_urti_abs_prevl.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cph_urti_abs_prevl = CoxPHFitter(penalizer=0.1)\n",
    "# cph_urti_abs_prevl.fit(data_urti_abs_prevl, \n",
    "#         duration_col='duration_urti_admitted',\n",
    "#         event_col='event_urti_admitted',\n",
    "#         formula=\"age_cat + sex + gp_count + flu_vaccine + region + antibacterial_brit + imd + ethnicity\"\n",
    "#         )\n",
    "# cph_urti_abs_prevl.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, ax = plt.subplots(figsize=(5, 9)\n",
    "#                         )\n",
    "# cph_urti_abs_prevl.plot()\n",
    "# plt.suptitle('Coefficients of CPH model for prevalent hospital admission 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/repeat_ABs_outputs/coefficients_cph_urti_abs_prevl.jpg', bbox_inches='tight')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "##save cph model with pickle package\n",
    "\n",
    "# current_working_dir = os.getcwd()\n",
    "# print(current_working_dir)\n",
    "\n",
    "# with open('output/hospitalisation_prediction_urti/cph_urti_abs_prevl.pickle', 'wb') as f:\n",
    "#     pickle.dump(cph_urti_abs_prevl, f)\n",
    "\n",
    "# with open('output/hospitalisation_prediction_urti/cph_urti_abs_prevl.pickle', 'rb') as f:\n",
    "#     cph_urti_abs_prevl = pickle.load(f)\n",
    "\n",
    "# cph_urti_abs_prevl.summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hosp history, ab type history, "
   ]
  }
 ],
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