JP_Easy_SIRD.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# JP's Easy SIRD Starter Notebook \n",
    "\n",
    "This notebook is meant to serve as a starter for anyone interested in getting started with SIRD modeling of an outbreak, like COVID-19! \n",
    "\n",
    "- Input: This python code takes in sequential observations of # of infected+recovered+deceased for a given locale. Here we are using data from multiple sources to examine outbreaks in a selection of locales.\n",
    "- Methodology: The analysis below performs non-linear least-squares regression using a bounded trust region (trf) algorithm in order to optimize the parameters of the SIRD system of ordinary differential equations (ODEs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import cufflinks as cf\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.integrate import odeint\n",
    "from typing import List\n",
    "from IPython.display import display\n",
    "from datetime import timedelta\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load datasets\n",
    "\n",
    "See [other notebooks in this repo](https://github.com/jpoles1/open_covid19) for code which can help with scraping these datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded Italy province data!\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>data</th>\n",
       "      <th>stato</th>\n",
       "      <th>codice_regione</th>\n",
       "      <th>region</th>\n",
       "      <th>codice_provincia</th>\n",
       "      <th>province</th>\n",
       "      <th>sigla_provincia</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>total positive cases</th>\n",
       "      <th>note_it</th>\n",
       "      <th>note_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>13</td>\n",
       "      <td>Abruzzo</td>\n",
       "      <td>69</td>\n",
       "      <td>Chieti</td>\n",
       "      <td>CH</td>\n",
       "      <td>42.351032</td>\n",
       "      <td>14.167546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date                 data stato  codice_regione   region  \\\n",
       "0  2020-02-24  2020-02-24T18:00:00   ITA              13  Abruzzo   \n",
       "\n",
       "   codice_provincia province sigla_provincia        lat       long  \\\n",
       "0                69   Chieti              CH  42.351032  14.167546   \n",
       "\n",
       "   total positive cases note_it note_en  \n",
       "0                   0.0     NaN     NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded Italy region data!\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>data</th>\n",
       "      <th>stato</th>\n",
       "      <th>codice_regione</th>\n",
       "      <th>region</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>hospitalized with symptoms</th>\n",
       "      <th>terapia_intensiva</th>\n",
       "      <th>total hospitalized</th>\n",
       "      <th>in home isolation</th>\n",
       "      <th>total currently positive</th>\n",
       "      <th>total newly positive</th>\n",
       "      <th>recovered</th>\n",
       "      <th>deaths</th>\n",
       "      <th>total positive cases</th>\n",
       "      <th>tamponi</th>\n",
       "      <th>note_it</th>\n",
       "      <th>note_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>13</td>\n",
       "      <td>Abruzzo</td>\n",
       "      <td>42.351222</td>\n",
       "      <td>13.398438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "         date                 data stato  codice_regione   region        lat  \\\n",
       "0  2020-02-24  2020-02-24T18:00:00   ITA              13  Abruzzo  42.351222   \n",
       "\n",
       "        long  hospitalized with symptoms  terapia_intensiva  \\\n",
       "0  13.398438                           0                  0   \n",
       "\n",
       "   total hospitalized  in home isolation  total currently positive  \\\n",
       "0                   0                  0                         0   \n",
       "\n",
       "   total newly positive  recovered  deaths  total positive cases  tamponi  \\\n",
       "0                     0          0       0                     0        5   \n",
       "\n",
       "  note_it note_en  \n",
       "0     NaN     NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loaded JHU data!\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Active</th>\n",
       "      <th>Combined_Key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "         Date Province/State  Country/Region      Last Update  Confirmed  \\\n",
       "0  2020-01-22          Anhui  Mainland China  1/22/2020 17:00        1.0   \n",
       "\n",
       "   Deaths  Recovered  Latitude  Longitude  FIPS Admin2  Active Combined_Key  \n",
       "0     NaN        NaN       NaN        NaN   NaN    NaN     NaN          NaN  "
      ]
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     "text": [
      "\n",
      "Loaded NYTimes data!\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>Washington</td>\n",
       "      <td>53061.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         date     county       state     fips  cases  deaths\n",
       "0  2020-01-21  Snohomish  Washington  53061.0      1       0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "italy_province_data = pd.read_csv(\"output/italy_province_report_data.csv\")\n",
    "print(\"Loaded Italy province data!\")\n",
    "display(italy_province_data.head(1))\n",
    "italy_region_data = pd.read_csv(\"output/italy_region_report_data.csv\")\n",
    "print(\"Loaded Italy region data!\")\n",
    "display(italy_region_data.head(1))\n",
    "jhu_data = pd.read_csv(\"output/daily_jhu_report_data.csv\")\n",
    "print(\"\\nLoaded JHU data!\")\n",
    "display(jhu_data.head(1))\n",
    "nytimes_data = pd.read_csv(\"output/nytime_data.csv\")\n",
    "print(\"\\nLoaded NYTimes data!\")\n",
    "display(nytimes_data.head(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing data for analysis\n",
    "\n",
    "This data analysis pipeline takes in one dataframe per locale. This dataframe should be indexed by date with a column for each of \"Confirmed\", \"Recovered\", and \"Deaths\". See example after next cell.\n",
    "\n",
    "_Note: in light of the formatting of available data at the time of authoring, this analysis pipeline takes in counts of total cases confirmed (rather than # of currently infected individuals)._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nytimes_nyc_data = nytimes_data[nytimes_data[\"county\"] == \"New York City\"][[\"date\", \"cases\", \"deaths\"]]\n",
    "nytimes_nyc_data[\"Recovered\"] = 0\n",
    "nytimes_nyc_data = nytimes_nyc_data[[\"date\", \"cases\", \"Recovered\", \"deaths\"]] \n",
    "nytimes_nyc_data.columns = [\"Date\", \"Confirmed\", \"Recovered\", \"Deaths\"]\n",
    "nytimes_nyc_data.set_index(\"Date\", inplace=True)\n",
    "\n",
    "#JHU data seems more complete for Chinese provinces\n",
    "jhu_hubei_data = jhu_data[jhu_data[\"Province/State\"] == \"Hubei\"][[\"Date\", \"Confirmed\", \"Recovered\", \"Deaths\"]]\n",
    "jhu_hubei_data[\"Date\"] = pd.to_datetime(jhu_hubei_data[\"Date\"])\n",
    "jhu_hubei_data.set_index(\"Date\", inplace=True)\n",
    "\n",
    "jhu_hunan_data = jhu_data[jhu_data[\"Province/State\"] == \"Hunan\"][[\"Date\", \"Confirmed\", \"Recovered\", \"Deaths\"]]\n",
    "jhu_hunan_data[\"Date\"] = pd.to_datetime(jhu_hunan_data[\"Date\"])\n",
    "jhu_hunan_data.set_index(\"Date\", inplace=True)\n",
    "\n",
    "#Italian data for regions\n",
    "lombardia_data = italy_region_data[italy_region_data[\"region\"] == \"Lombardia\"][[\"date\", \"total positive cases\", \"recovered\", \"deaths\"]]\n",
    "lombardia_data.columns = [\"Date\", \"Confirmed\", \"Recovered\", \"Deaths\"]\n",
    "lombardia_data.set_index(\"Date\", inplace=True)\n",
    "\n",
    "emilia_romagna_data = italy_region_data[italy_region_data[\"region\"] == \"Emilia Romagna\"][[\"date\", \"total positive cases\", \"recovered\", \"deaths\"]]\n",
    "emilia_romagna_data.columns = [\"Date\", \"Confirmed\", \"Recovered\", \"Deaths\"]\n",
    "emilia_romagna_data.set_index(\"Date\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>20011</td>\n",
       "      <td>0</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>23112</td>\n",
       "      <td>0</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>25399</td>\n",
       "      <td>0</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Confirmed  Recovered  Deaths\n",
       "Date                                    \n",
       "2020-03-25      20011          0     280\n",
       "2020-03-26      23112          0     365\n",
       "2020-03-27      25399          0     450"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Example clean data\n",
    "nytimes_nyc_data.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ready to Start With SIRD Modeling!\n",
    "\n",
    "With your clean data in hand you can start using the sird_reg() function as a jumping-off point for your SIRD parameter fitting and modeling!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "class Epidemic:\n",
    "    def __init__(self, locale_name, locale_data, locale_pop, hospital_beds = None,\n",
    "                t_beta_override=None, lsq_bounds=([0, 5, 0.02, 0.002], [1, 60, 1, 0.25]), \n",
    "                t_lockdown = None, t_lift = None, forecast_days=60):\n",
    "        \"\"\"\n",
    "        Epidemic class to store the info needed to performs and plot \n",
    "        non-linear least-squares fitting of SIRD parameters on a dataframe\n",
    "        Attributes:\n",
    "            locale_data: dataframe = df with columns [\"Confirmed\", \"Recovered\", \"Deaths\"] and date index \n",
    "            locale_pop: int = total population of the locale from which this dataframe is derived\n",
    "            locale_name: str = name of locale (for tables and plots)\n",
    "            t_lockdown: datetime, t_lift: datetime = datetime of when social-distancing begins and ends, None to ignore\n",
    "            hospital_beds: int: number of hospital beds, if known\n",
    "            t_beta_override = If set, will not attempt to fit t-beta using least squared, None to fit t_beta\n",
    "            ls_bounds: ([int], [int]) =least-squared regression boundary condition for each parameter ([beta_0_lower, t_beta_lower...], [beta_0_upper, t_beta_upper...])\n",
    "            forecast_days: int = number of days past input data to forecast values\n",
    "            show_ci: bool = shows confidence intervals (TODO: not currently implemented)\n",
    "        \"\"\"\n",
    "        self.locale_data = locale_data\n",
    "        self.locale_name = locale_name\n",
    "        self.locale_pop = locale_pop\n",
    "        self.hospital_beds = hospital_beds\n",
    "        self.t_beta_override = t_beta_override\n",
    "        self.lsq_bounds = lsq_bounds\n",
    "        self.forecast_days = forecast_days\n",
    "        self.t_lockdown = pd.to_datetime(t_lockdown)\n",
    "        self.t_lift = pd.to_datetime(t_lift)\n",
    "    #Setup boundary conditions for non-linear least-squared optimization (beta_0, t_beta, gamma, delta)\n",
    "    def sird_reg(self):\n",
    "        locale_sird = self.locale_data.copy()\n",
    "        #Start by converting total confirmed cases to SIRD variables\n",
    "        locale_sird[\"Susceptible\"] = self.locale_pop - locale_sird[\"Confirmed\"]\n",
    "        locale_sird[\"Infected\"] = locale_sird[\"Confirmed\"] - locale_sird[\"Recovered\"] - locale_sird[\"Deaths\"] #- locale_sird[\"Recovered\"]\n",
    "        locale_sird.drop(\"Confirmed\", axis=1, inplace=True)\n",
    "        locale_sird = locale_sird[[\"Susceptible\", \"Infected\", \"Recovered\", \"Deaths\"]]\n",
    "        #Fill NaNs with 0\n",
    "        locale_sird.fillna(0, inplace=True)\n",
    "        #Remove rows from prior to first recorded infection\n",
    "        date_list = locale_sird.index[locale_sird[\"Infected\"] > 1] #for plotting later\n",
    "        locale_sird = locale_sird[locale_sird[\"Infected\"] > 1]\n",
    "        if(self.t_lockdown != None):\n",
    "            self.t_lockdown = (self.t_lockdown - pd.to_datetime(date_list[0])).days\n",
    "        if(self.t_lift != None):\n",
    "            self.t_lift = (self.t_lift - pd.to_datetime(date_list[0])).days\n",
    "        #Reset index to days since first recorded infection\n",
    "        locale_sird.reset_index(drop=True, inplace=True)\n",
    "        def sird_fn(timepoints, beta_0, t_beta, gamma, delta):\n",
    "            \"\"\"\n",
    "            Define function which will be used for non-linear least-sq fitting \n",
    "            Attributes:\n",
    "                timepoints = timepoints at which to calculate integral of ODE\n",
    "                beta_0 = initial infxn coeff | t_beta=infxn coeff decay period \n",
    "                gamma = recovery coeff | delta = death coeff | x = timepoint\n",
    "            Returns:\n",
    "                Flattened integral of [S, I, R, D] at timepoints using initial values from locale_sird\n",
    "                Must be reshaped below before plotting\n",
    "            \"\"\"\n",
    "            def sird_ode(y_t, t):\n",
    "                \"\"\"\n",
    "                Define system of ODEs to be integrated for SIRD model\n",
    "                Attributes:\n",
    "                    y_t = y(t) = [S, I, R, D] \n",
    "                    t = timepoint\n",
    "                Returns:\n",
    "                    Derivative of y(t) at timepoint t = [dS(t)/dt, dI(t)/dt, dR(t)/dt, dD(t)/dt] \n",
    "                \"\"\"\n",
    "\n",
    "                #beta attenuates over the course of the outbreak\n",
    "                beta = beta_0\n",
    "                if self.t_lift != None and t > self.t_lift:\n",
    "                    lift_inc_trans_factor = 1.5;\n",
    "                    beta = beta_0 * math.exp(-(t - (self.t_lockdown if self.t_lockdown else 0)) / (t_beta*lift_inc_trans_factor if self.t_beta_override == None else self.t_beta_override*lift_inc_trans_factor))\n",
    "                elif self.t_lockdown == None or t > self.t_lockdown:\n",
    "                    beta = beta_0 * math.exp(-(t - (self.t_lockdown if self.t_lockdown else 0)) / (t_beta if self.t_beta_override == None else self.t_beta_override))\n",
    "                ds_dt = -beta * y_t[0] * y_t[1] / self.locale_pop #Susceptible\n",
    "                dd_dt = delta * y_t[1] #Deaths\n",
    "                dr_dt = gamma * y_t[1] #Recovered\n",
    "                di_dt = -ds_dt - dd_dt - dr_dt #Infected\n",
    "                return [ds_dt, di_dt, dr_dt, dd_dt]\n",
    "            #Calculate the integral of the above ODE over the range of timepoints using first data point as initial value\n",
    "            sird_int = odeint(sird_ode, locale_sird.values[0], timepoints)\n",
    "            return sird_int.ravel()\n",
    "        #Use non-linear least-squares optimization to fit parameters to known data\n",
    "        param_est, param_cov = curve_fit(sird_fn, locale_sird.index.values, locale_sird.values.ravel(),\n",
    "                                         method=\"trf\", bounds=self.lsq_bounds)\n",
    "        #compute 1x StDev errors for params using diagonal of covariance matrix\n",
    "        param_stdev = np.sqrt(np.diag(param_cov))\n",
    "        #Predict SIRD using model, shape will be 1 x (4*# predicted)\n",
    "        predicted_data = sird_fn(range(len(locale_sird.index) + self.forecast_days), *param_est)\n",
    "        #Reshape into (# predicted, 4)\n",
    "        predicted_data = predicted_data.reshape(len(locale_sird.index) + self.forecast_days, 4)\n",
    "        #Rename columns properly\n",
    "        predicted_data = pd.DataFrame(predicted_data, columns=[\"Susceptible\", \"Infected\", \"Recovered\", \"Deaths\"])\n",
    "        #Construct plot dataframe\n",
    "        plot_data = predicted_data[[\"Infected\", \"Recovered\", \"Deaths\"]]\n",
    "        plot_data.columns = [\"Projected Infected\", \"Projected Recovered\", \"Projected Deaths\"]\n",
    "        plot_data = plot_data.join(locale_sird[[\"Infected\", \"Recovered\", \"Deaths\"]])\n",
    "        if self.hospital_beds != None:\n",
    "            plot_data[\"Projected Hospitalized\"] = plot_data[\"Projected Infected\"] * 0.2 #20% of infected estimated to require hospitalization\n",
    "        #Convert index back from days since outbreak to dates\n",
    "        plot_data.index = pd.date_range(date_list[0], pd.to_datetime(date_list[-1]) + timedelta(days=self.forecast_days)).to_pydatetime()\n",
    "        #Change t_beta param_est/stdev if using override\n",
    "        param_est[1] = param_est[1] if self.t_beta_override == None else self.t_beta_override\n",
    "        param_stdev[1] = param_stdev[1] if self.t_beta_override == None else 0\n",
    "        #Plot\n",
    "        plot_modes = {col: (\"markers\" if \"Projected\" in col else \"lines\") for col in plot_data.columns}\n",
    "        fig = plot_data.iplot(asFigure = True, mode=plot_modes, size=4, width=3, colorscale=\"plotly\",\n",
    "                              title=\"SIRD Model of %s | Beta_0=%s | T_beta=%s | Gamma=%s | Delta=%s\" % (self.locale_name, param_est[0].round(2), param_est[1].round(1), param_est[2].round(4), param_est[3].round(4)))\n",
    "        if self.hospital_beds != None:\n",
    "            fig.add_shape(\n",
    "                type=\"line\",\n",
    "                x0=plot_data.index[0],\n",
    "                y0=self.hospital_beds,\n",
    "                x1=plot_data.index[-1],\n",
    "                y1=self.hospital_beds,\n",
    "                line=dict(\n",
    "                    color=\"Red\",\n",
    "                    width=3,\n",
    "                    dash=\"dashdot\",\n",
    "                ),\n",
    "            )\n",
    "        if self.t_lockdown != None:\n",
    "            fig.add_shape(\n",
    "                type=\"line\",\n",
    "                x0=plot_data.index[self.t_lockdown],\n",
    "                y0=0,\n",
    "                x1=plot_data.index[self.t_lockdown],\n",
    "                y1=plot_data.max().max(),\n",
    "                line=dict(\n",
    "                    color=\"Dark Blue\",\n",
    "                    width=3,\n",
    "                    dash=\"dot\",\n",
    "                ),\n",
    "            )\n",
    "        if self.t_lift != None:\n",
    "            fig.add_shape(\n",
    "                type=\"line\",\n",
    "                x0=plot_data.index[self.t_lift],\n",
    "                y0=0,\n",
    "                x1=plot_data.index[self.t_lift],\n",
    "                y1=plot_data.max().max(),\n",
    "                line=dict(\n",
    "                    color=\"Red\",\n",
    "                    width=3,\n",
    "                    dash=\"dashdot\",\n",
    "                ),\n",
    "            )\n",
    "        fig.show()\n",
    "        return (param_est, param_stdev, plot_data)\n",
    "def run_pandemic(epidemic_list):\n",
    "    plot_data = [];\n",
    "    #Collect output from each locales SIRD regression\n",
    "    sird_param_est = []\n",
    "    sird_param_stdev = []\n",
    "    sird_rmse = []\n",
    "    #Iterate over list of epidemics\n",
    "    for epi in epidemic_list:\n",
    "        #Run regression and receive parameter estimates/stdevs\n",
    "        res = epi.sird_reg()\n",
    "        #Store parameter estimates and stdev for display later\n",
    "        sird_param_est.append(res[0])\n",
    "        sird_param_stdev.append(res[1])\n",
    "        #Calculate root mean square error (RMSE) for each outcome variable prediction (SIRD)\n",
    "        plot_data = res[2].dropna()\n",
    "        if epi.locale_name == \"NYC\":\n",
    "            #save NYC plot data for use outside loop\n",
    "            nyc_plot_data = plot_data\n",
    "        infected_rmse = math.sqrt(np.sum((plot_data[\"Infected\"]-plot_data[\"Projected Infected\"]).pow(2))/len(plot_data.index))\n",
    "        recovered_rmse = math.sqrt(np.sum((plot_data[\"Recovered\"]-plot_data[\"Projected Recovered\"]).pow(2))/len(plot_data.index))\n",
    "        death_rmse = math.sqrt(np.sum((plot_data[\"Deaths\"]-plot_data[\"Projected Deaths\"]).pow(2))/len(plot_data.index))\n",
    "        sird_rmse.append([infected_rmse, recovered_rmse, death_rmse])\n",
    "    locale_name_list = [x.locale_name for x in epidemic_list]\n",
    "    print(\"SIRD Regression Parameter Estimates\")\n",
    "    display(pd.DataFrame(sird_param_est, index=locale_name_list, columns=[\"Beta_0\", \"T_beta\", \"Gamma\", \"Delta\"]))\n",
    "    sird_param_95ci = [zip(np.maximum([0, 0, 0, 0], x - 2*sird_param_stdev[i]).round(5), np.maximum([0, 0, 0, 0], x + 2*sird_param_stdev[i]).round(5)) for i,x in enumerate(sird_param_est)]\n",
    "    print(\"\\n\\nSIRD Regression Parameter 95% Confidence Intervals\")\n",
    "    display(pd.DataFrame(sird_param_95ci, index=locale_name_list, columns=[\"Beta_0\", \"T_beta\", \"Gamma\", \"Delta\"]))\n",
    "    print(\"\\n\\nSIRD Outcome RMSEs\")\n",
    "    display(pd.DataFrame(sird_rmse, index=locale_name_list, columns=[\"Infected RMSE\", \"Recovered RMSE\", \"Deaths RMSE\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
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      "SIRD Regression Parameter Estimates\n"
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       "      <th></th>\n",
       "      <th>Beta_0</th>\n",
       "      <th>T_beta</th>\n",
       "      <th>Gamma</th>\n",
       "      <th>Delta</th>\n",
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       "      <th>NYC</th>\n",
       "      <td>0.476914</td>\n",
       "      <td>7.377784</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.002000</td>\n",
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       "      <th>Hubei</th>\n",
       "      <td>0.519814</td>\n",
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       "      <td>0.039863</td>\n",
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       "      <th>Lombardia</th>\n",
       "      <td>0.274525</td>\n",
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       "             Beta_0     T_beta     Gamma     Delta\n",
       "NYC        0.476914   7.377784  0.020000  0.002000\n",
       "Hubei      0.519814  10.810201  0.039863  0.002323\n",
       "Lombardia  0.274525  14.855136  0.034644  0.021555"
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      "\n",
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      "SIRD Regression Parameter 95% Confidence Intervals\n"
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       "      <th>NYC</th>\n",
       "      <td>(0.47078, 0.48305)</td>\n",
       "      <td>(6.888, 7.86757)</td>\n",
       "      <td>(0.01466, 0.02534)</td>\n",
       "      <td>(0.0, 0.00723)</td>\n",
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       "      <th>Hubei</th>\n",
       "      <td>(0.50038, 0.53925)</td>\n",
       "      <td>(10.19023, 11.43017)</td>\n",
       "      <td>(0.03748, 0.04225)</td>\n",
       "      <td>(0.00097, 0.00368)</td>\n",
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       "      <th>Lombardia</th>\n",
       "      <td>(0.27309, 0.27596)</td>\n",
       "      <td>(14.42108, 15.28919)</td>\n",
       "      <td>(0.03333, 0.03595)</td>\n",
       "      <td>(0.02032, 0.02279)</td>\n",
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       "                       Beta_0                T_beta               Gamma  \\\n",
       "NYC        (0.47078, 0.48305)      (6.888, 7.86757)  (0.01466, 0.02534)   \n",
       "Hubei      (0.50038, 0.53925)  (10.19023, 11.43017)  (0.03748, 0.04225)   \n",
       "Lombardia  (0.27309, 0.27596)  (14.42108, 15.28919)  (0.03333, 0.03595)   \n",
       "\n",
       "                        Delta  \n",
       "NYC            (0.0, 0.00723)  \n",
       "Hubei      (0.00097, 0.00368)  \n",
       "Lombardia  (0.02032, 0.02279)  "
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      "\n",
      "\n",
      "SIRD Outcome RMSEs\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Infected RMSE</th>\n",
       "      <th>Recovered RMSE</th>\n",
       "      <th>Deaths RMSE</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>NYC</th>\n",
       "      <td>502.312357</td>\n",
       "      <td>643.145395</td>\n",
       "      <td>74.955041</td>\n",
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       "      <th>Hubei</th>\n",
       "      <td>7578.264220</td>\n",
       "      <td>5995.223620</td>\n",
       "      <td>477.967541</td>\n",
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       "      <th>Lombardia</th>\n",
       "      <td>521.091092</td>\n",
       "      <td>234.019384</td>\n",
       "      <td>70.424880</td>\n",
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       "           Infected RMSE  Recovered RMSE  Deaths RMSE\n",
       "NYC           502.312357      643.145395    74.955041\n",
       "Hubei        7578.264220     5995.223620   477.967541\n",
       "Lombardia     521.091092      234.019384    70.424880"
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   "source": [
    "#Collect clean locale data into dictionary for batch processing\n",
    "epidemic_list = [\n",
    "    Epidemic(\"NYC\", nytimes_nyc_data, locale_pop=8623000, hospital_beds=23000, \n",
    "             t_lockdown=\"03-19-2020\", t_lift=\"05-19-2020\", forecast_days=180),\n",
    "    Epidemic(\"Hubei\", jhu_hubei_data, locale_pop=58500000, t_lockdown=\"01-23-2020\", t_lift=\"03-27-2020\"),\n",
    "    Epidemic(\"Lombardia\", lombardia_data, locale_pop=10040000, t_lockdown=\"03-09-2020\", t_lift=\"05-09-2020\")\n",
    "]\n",
    "run_pandemic(epidemic_list)"
   ]
  },
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   "execution_count": 7,
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      "SIRD Regression Parameter Estimates\n"
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Beta_0</th>\n",
       "      <th>T_beta</th>\n",
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       "      <th>NYC #1</th>\n",
       "      <td>0.476914</td>\n",
       "      <td>7.377784</td>\n",
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       "      <td>0.002</td>\n",
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       "          Beta_0    T_beta  Gamma  Delta\n",
       "NYC #1  0.476914  7.377784   0.02  0.002\n",
       "NYC #2  0.476914  7.377784   0.02  0.002\n",
       "NYC #3  0.476914  7.377784   0.02  0.002"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "SIRD Regression Parameter 95% Confidence Intervals\n"
     ]
    },
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       "      <th>Beta_0</th>\n",
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       "      <th>NYC #1</th>\n",
       "      <td>(0.47078, 0.48305)</td>\n",
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       "      <td>(0.01466, 0.02534)</td>\n",
       "      <td>(0.0, 0.00723)</td>\n",
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       "      <td>(0.47078, 0.48305)</td>\n",
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       "      <td>(0.0, 0.00723)</td>\n",
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       "                    Beta_0            T_beta               Gamma  \\\n",
       "NYC #1  (0.47078, 0.48305)  (6.888, 7.86757)  (0.01466, 0.02534)   \n",
       "NYC #2  (0.47078, 0.48305)  (6.888, 7.86757)  (0.01466, 0.02534)   \n",
       "NYC #3  (0.47078, 0.48305)  (6.888, 7.86757)  (0.01466, 0.02534)   \n",
       "\n",
       "                 Delta  \n",
       "NYC #1  (0.0, 0.00723)  \n",
       "NYC #2  (0.0, 0.00723)  \n",
       "NYC #3  (0.0, 0.00723)  "
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     "text": [
      "\n",
      "\n",
      "SIRD Outcome RMSEs\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>Infected RMSE</th>\n",
       "      <th>Recovered RMSE</th>\n",
       "      <th>Deaths RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>NYC #1</th>\n",
       "      <td>502.312357</td>\n",
       "      <td>643.145395</td>\n",
       "      <td>74.955041</td>\n",
       "    </tr>\n",
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       "      <th>NYC #2</th>\n",
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       "      <td>74.955041</td>\n",
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       "      <th>NYC #3</th>\n",
       "      <td>502.312357</td>\n",
       "      <td>643.145395</td>\n",
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       "        Infected RMSE  Recovered RMSE  Deaths RMSE\n",
       "NYC #1     502.312357      643.145395    74.955041\n",
       "NYC #2     502.312357      643.145395    74.955041\n",
       "NYC #3     502.312357      643.145395    74.955041"
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   ],
   "source": [
    "#Collect clean locale data into dictionary for batch processing\n",
    "epidemic_list = [\n",
    "    Epidemic(\"NYC #1\", nytimes_nyc_data, locale_pop=8623000, hospital_beds=23000, \n",
    "             t_lockdown=\"03-19-2020\", t_lift=\"04-19-2020\", forecast_days=180),\n",
    "    Epidemic(\"NYC #2\", nytimes_nyc_data, locale_pop=8623000, hospital_beds=23000, \n",
    "             t_lockdown=\"03-19-2020\", t_lift=\"05-19-2020\", forecast_days=180),\n",
    "    Epidemic(\"NYC #3\", nytimes_nyc_data, locale_pop=8623000, hospital_beds=23000, \n",
    "             t_lockdown=\"03-19-2020\", t_lift=\"06-19-2020\", forecast_days=180),\n",
    "]\n",
    "run_pandemic(epidemic_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References:\n",
    "- [Chinese and Italian COVID-19 outbreaks can be correctly described by a modified SIRD model](https://www.medrxiv.org/content/10.1101/2020.03.19.20039388v1)\n",
    "\n",
    "Future reading:\n",
    "- [Analysis and forecast of COVID-19 spreading in China, Italy and France](https://www.sciencedirect.com/science/article/pii/S0960077920301636)\n",
    "- [Preliminary analysis of COVID-19 spread in Italy with an adaptive SEIRD model](https://arxiv.org/pdf/2003.09909.pdf)\n",
    "- [Tracing DAY-ZERO and Forecasting the Fade out of the COVID-19 Outbreak in Lombardy, Italy: A Compartmental Modelling and Numerical Optimization Approach.](https://www.medrxiv.org/content/10.1101/2020.03.17.20037689v2)\n",
    "- [Estimation of the final size of the COVID-19 epidemic](https://www.medrxiv.org/content/10.1101/2020.02.16.20023606v5.full.pdf+html)"
   ]
  }
 ],
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