covid-19-analysis-and-modelling-sir-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "This notebook consists of two main parts:\n",
    "1. Data Analysis (Development, Fatality Rate, ...) (finished)\n",
    "2. Modelling and Prediction (SIR, ML approaches, ...) (not fully finished)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the number of confirmed cases is strongly dependent on the number of Covid-19-tests that are performed over time. Many countries are still ramping up their testing efforts and they do so at different speeds. Thus, I will mostly exclude the number of confirmed cases from these analyses; The number of fatalities is much less error-prone, so I will focus on that instead. (I will still predict the number of confirmed cases as that's necessary for the submission file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mpld3 in /opt/conda/lib/python3.6/site-packages (0.3)\r\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "!pip install mpld3\n",
    "import mpld3\n",
    "mpld3.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('../input/covid19-global-forecasting-week-2/train.csv')\n",
    "test = pd.read_csv('../input/covid19-global-forecasting-week-2/test.csv')\n",
    "submission = pd.read_csv('../input/covid19-global-forecasting-week-2/submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Id</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19399</th>\n",
       "      <td>29362</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19400</th>\n",
       "      <td>29363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19401</th>\n",
       "      <td>29364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19402</th>\n",
       "      <td>29365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19403</th>\n",
       "      <td>29366</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19404 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id Province_State Country_Region        Date  ConfirmedCases  \\\n",
       "0          1            NaN    Afghanistan  2020-01-22             0.0   \n",
       "1          2            NaN    Afghanistan  2020-01-23             0.0   \n",
       "2          3            NaN    Afghanistan  2020-01-24             0.0   \n",
       "3          4            NaN    Afghanistan  2020-01-25             0.0   \n",
       "4          5            NaN    Afghanistan  2020-01-26             0.0   \n",
       "...      ...            ...            ...         ...             ...   \n",
       "19399  29362            NaN       Zimbabwe  2020-03-23             3.0   \n",
       "19400  29363            NaN       Zimbabwe  2020-03-24             3.0   \n",
       "19401  29364            NaN       Zimbabwe  2020-03-25             3.0   \n",
       "19402  29365            NaN       Zimbabwe  2020-03-26             3.0   \n",
       "19403  29366            NaN       Zimbabwe  2020-03-27             5.0   \n",
       "\n",
       "       Fatalities  \n",
       "0             0.0  \n",
       "1             0.0  \n",
       "2             0.0  \n",
       "3             0.0  \n",
       "4             0.0  \n",
       "...           ...  \n",
       "19399         1.0  \n",
       "19400         1.0  \n",
       "19401         1.0  \n",
       "19402         1.0  \n",
       "19403         1.0  \n",
       "\n",
       "[19404 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Country_Region\"] = [country_name.replace(\"'\",\"\") for country_name in train[\"Country_Region\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Analysis (Development, Fatality Rate, ...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "LAST_DATE = train.iloc[-1][\"Date\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Development"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exemplary development in one country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el131398187534179609452393489\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el131398187534179609452393489\", {\"width\": 576.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [0.0, 65.0], \"ylim\": [-4324.900000000001, 90822.9], \"xdomain\": [0.0, 65.0], \"ydomain\": [-4324.900000000001, 90822.9], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0], \"tickformat\": [\"2020-01-22\", \"2020-02-01\", \"2020-02-11\", \"2020-02-21\", \"2020-03-02\", \"2020-03-12\", \"2020-03-22\", \"\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 7, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818548904904\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818548696960\", \"color\": \"#1F77B4\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818548698080\", \"color\": \"#FF7F0E\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818548286296\", \"color\": \"#1F77B4\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818548287360\", \"color\": \"#FF7F0E\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818548698864\", \"dasharray\": \"none\", \"alpha\": 0.8, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Date\", \"position\": [0.5, -0.09657836644591611], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818548907144\"}, {\"text\": \"Covid-19 cases and fatalities in Italy\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818548576208\"}, {\"text\": \"ConfirmedCases\", \"position\": [0.08288530465949823, 0.9218175128771156], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818548285792\"}, {\"text\": \"Fatalities\", \"position\": [0.08288530465949823, 0.8528329654157469], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818548286912\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [4.0, 0.0, 0.0], [5.0, 0.0, 0.0], [6.0, 0.0, 0.0], [7.0, 0.0, 0.0], [8.0, 0.0, 0.0], [9.0, 2.0, 0.0], [10.0, 2.0, 0.0], [11.0, 2.0, 0.0], [12.0, 2.0, 0.0], [13.0, 2.0, 0.0], [14.0, 2.0, 0.0], [15.0, 2.0, 0.0], [16.0, 3.0, 0.0], [17.0, 3.0, 0.0], [18.0, 3.0, 0.0], [19.0, 3.0, 0.0], [20.0, 3.0, 0.0], [21.0, 3.0, 0.0], [22.0, 3.0, 0.0], [23.0, 3.0, 0.0], [24.0, 3.0, 0.0], [25.0, 3.0, 0.0], [26.0, 3.0, 0.0], [27.0, 3.0, 0.0], [28.0, 3.0, 0.0], [29.0, 3.0, 0.0], [30.0, 20.0, 1.0], [31.0, 62.0, 2.0], [32.0, 155.0, 3.0], [33.0, 229.0, 7.0], [34.0, 322.0, 10.0], [35.0, 453.0, 12.0], [36.0, 655.0, 17.0], [37.0, 888.0, 21.0], [38.0, 1128.0, 29.0], [39.0, 1694.0, 34.0], [40.0, 2036.0, 52.0], [41.0, 2502.0, 79.0], [42.0, 3089.0, 107.0], [43.0, 3858.0, 148.0], [44.0, 4636.0, 197.0], [45.0, 5883.0, 233.0], [46.0, 7375.0, 366.0], [47.0, 9172.0, 463.0], [48.0, 10149.0, 631.0], [49.0, 12462.0, 827.0], [50.0, 12462.0, 827.0], [51.0, 17660.0, 1266.0], [52.0, 21157.0, 1441.0], [53.0, 24747.0, 1809.0], [54.0, 27980.0, 2158.0], [55.0, 31506.0, 2503.0], [56.0, 35713.0, 2978.0], [57.0, 41035.0, 3405.0], [58.0, 47021.0, 4032.0], [59.0, 53578.0, 4825.0], [60.0, 59138.0, 5476.0], [61.0, 63927.0, 6077.0], [62.0, 69176.0, 6820.0], [63.0, 74386.0, 7503.0], [64.0, 80589.0, 8215.0], [65.0, 86498.0, 9134.0]], \"data02\": [[0.020161290322580655, 0.9379139072847682, 0.8689293598233996], [0.0649641577060932, 0.9379139072847682, 0.8689293598233996]], \"data03\": [[0.015681003584229386, 0.8252391464311994], [0.2702172939068101, 0.8252391464311994], [0.2746975806451613, 0.8252391464311994], [0.2746975806451613, 0.8344370860927152], [0.2746975806451613, 0.9678072111846947], [0.2746975806451613, 0.9770051508462105], [0.2702172939068101, 0.9770051508462105], [0.015681003584229386, 0.9770051508462105], [0.011200716845878145, 0.9770051508462105], [0.011200716845878145, 0.9678072111846947], [0.011200716845878145, 0.8344370860927152], [0.011200716845878145, 0.8252391464311994], [0.015681003584229386, 0.8252391464311994]]}, \"id\": \"el13139818753417960\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el131398187534179609452393489\", {\"width\": 576.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [0.0, 65.0], \"ylim\": [-4324.900000000001, 90822.9], \"xdomain\": [0.0, 65.0], \"ydomain\": [-4324.900000000001, 90822.9], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0], \"tickformat\": [\"2020-01-22\", \"2020-02-01\", \"2020-02-11\", \"2020-02-21\", \"2020-03-02\", \"2020-03-12\", \"2020-03-22\", \"\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 7, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818548904904\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818548696960\", \"color\": \"#1F77B4\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818548698080\", \"color\": \"#FF7F0E\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818548286296\", \"color\": \"#1F77B4\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818548287360\", \"color\": \"#FF7F0E\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818548698864\", \"dasharray\": \"none\", \"alpha\": 0.8, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Date\", \"position\": [0.5, -0.09657836644591611], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818548907144\"}, {\"text\": \"Covid-19 cases and fatalities in Italy\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818548576208\"}, {\"text\": \"ConfirmedCases\", \"position\": [0.08288530465949823, 0.9218175128771156], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818548285792\"}, {\"text\": \"Fatalities\", \"position\": [0.08288530465949823, 0.8528329654157469], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818548286912\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [4.0, 0.0, 0.0], [5.0, 0.0, 0.0], [6.0, 0.0, 0.0], [7.0, 0.0, 0.0], [8.0, 0.0, 0.0], [9.0, 2.0, 0.0], [10.0, 2.0, 0.0], [11.0, 2.0, 0.0], [12.0, 2.0, 0.0], [13.0, 2.0, 0.0], [14.0, 2.0, 0.0], [15.0, 2.0, 0.0], [16.0, 3.0, 0.0], [17.0, 3.0, 0.0], [18.0, 3.0, 0.0], [19.0, 3.0, 0.0], [20.0, 3.0, 0.0], [21.0, 3.0, 0.0], [22.0, 3.0, 0.0], [23.0, 3.0, 0.0], [24.0, 3.0, 0.0], [25.0, 3.0, 0.0], [26.0, 3.0, 0.0], [27.0, 3.0, 0.0], [28.0, 3.0, 0.0], [29.0, 3.0, 0.0], [30.0, 20.0, 1.0], [31.0, 62.0, 2.0], [32.0, 155.0, 3.0], [33.0, 229.0, 7.0], [34.0, 322.0, 10.0], [35.0, 453.0, 12.0], [36.0, 655.0, 17.0], [37.0, 888.0, 21.0], [38.0, 1128.0, 29.0], [39.0, 1694.0, 34.0], [40.0, 2036.0, 52.0], [41.0, 2502.0, 79.0], [42.0, 3089.0, 107.0], [43.0, 3858.0, 148.0], [44.0, 4636.0, 197.0], [45.0, 5883.0, 233.0], [46.0, 7375.0, 366.0], [47.0, 9172.0, 463.0], [48.0, 10149.0, 631.0], [49.0, 12462.0, 827.0], [50.0, 12462.0, 827.0], [51.0, 17660.0, 1266.0], [52.0, 21157.0, 1441.0], [53.0, 24747.0, 1809.0], [54.0, 27980.0, 2158.0], [55.0, 31506.0, 2503.0], [56.0, 35713.0, 2978.0], [57.0, 41035.0, 3405.0], [58.0, 47021.0, 4032.0], [59.0, 53578.0, 4825.0], [60.0, 59138.0, 5476.0], [61.0, 63927.0, 6077.0], [62.0, 69176.0, 6820.0], [63.0, 74386.0, 7503.0], [64.0, 80589.0, 8215.0], [65.0, 86498.0, 9134.0]], \"data02\": [[0.020161290322580655, 0.9379139072847682, 0.8689293598233996], [0.0649641577060932, 0.9379139072847682, 0.8689293598233996]], \"data03\": [[0.015681003584229386, 0.8252391464311994], [0.2702172939068101, 0.8252391464311994], [0.2746975806451613, 0.8252391464311994], [0.2746975806451613, 0.8344370860927152], [0.2746975806451613, 0.9678072111846947], [0.2746975806451613, 0.9770051508462105], [0.2702172939068101, 0.9770051508462105], [0.015681003584229386, 0.9770051508462105], [0.011200716845878145, 0.9770051508462105], [0.011200716845878145, 0.9678072111846947], [0.011200716845878145, 0.8344370860927152], [0.011200716845878145, 0.8252391464311994], [0.015681003584229386, 0.8252391464311994]]}, \"id\": \"el13139818753417960\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el131398187534179609452393489\", {\"width\": 576.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [0.0, 65.0], \"ylim\": [-4324.900000000001, 90822.9], \"xdomain\": [0.0, 65.0], \"ydomain\": [-4324.900000000001, 90822.9], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0], \"tickformat\": [\"2020-01-22\", \"2020-02-01\", \"2020-02-11\", \"2020-02-21\", \"2020-03-02\", \"2020-03-12\", \"2020-03-22\", \"\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 7, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818548904904\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818548696960\", \"color\": \"#1F77B4\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818548698080\", \"color\": \"#FF7F0E\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818548286296\", \"color\": \"#1F77B4\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818548287360\", \"color\": \"#FF7F0E\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818548698864\", \"dasharray\": \"none\", \"alpha\": 0.8, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Date\", \"position\": [0.5, -0.09657836644591611], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818548907144\"}, {\"text\": \"Covid-19 cases and fatalities in Italy\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818548576208\"}, {\"text\": \"ConfirmedCases\", \"position\": [0.08288530465949823, 0.9218175128771156], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818548285792\"}, {\"text\": \"Fatalities\", \"position\": [0.08288530465949823, 0.8528329654157469], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818548286912\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [4.0, 0.0, 0.0], [5.0, 0.0, 0.0], [6.0, 0.0, 0.0], [7.0, 0.0, 0.0], [8.0, 0.0, 0.0], [9.0, 2.0, 0.0], [10.0, 2.0, 0.0], [11.0, 2.0, 0.0], [12.0, 2.0, 0.0], [13.0, 2.0, 0.0], [14.0, 2.0, 0.0], [15.0, 2.0, 0.0], [16.0, 3.0, 0.0], [17.0, 3.0, 0.0], [18.0, 3.0, 0.0], [19.0, 3.0, 0.0], 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train[train[\"Country_Region\"]==\"Italy\"][[\"ConfirmedCases\", \"Fatalities\", \"Date\"]].plot(x=\"Date\", figsize=(8, 4), title=\"Covid-19 cases and fatalities in Italy\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Progression for the whole world (i.e. all countries summed up)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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22mlcfPHFTJgwgSuuuAJV5U9/+hPPP/8899xzD1dccQW7du1iypQpADzxxBNccsklXHDBBSxYsIA1a9Ywf/58Lr30UsaNG8fixYtZtmwZs2bNYurUqezcuROA3Nxc/uM//oMTTzyRE088kX//+98A5Ofns2DBAo4//ni++93v4l1P2fy+zJ07l5KSEs4880xmzpzJ1KlTefll7/dZaWkp5513HtOnT2fKlCkN+7lhwwbmz5/PCSecwNlnn012djYADz74IJMmTWLatGlcdtllnfmnMcaYsPXOZ3mUVAZ/gBfUbXmquhHvdrqmzgyQV4EbmtnO48DjAdIz8e7xb5qeH6iMY/Gzv21h6/6ijtwkk4YmcvcFk5tdvnnzZk444YSj0l966SU2btzIpk2byMvL48QTT2TevHkAfPjhh2zZsoWhQ4dy6qmn8u9//5trrrmGd955h/PPP5+LL76YXbt2Ndre2rVr+eijjxgwYABr1qxh06ZNbNu2jQEDBjB69GiuueYa3n//fR544AF+97vfcf/993PzzTdz6623MmfOHPbs2cPZZ5/Ntm3b+NnPfsacOXP4yU9+wsqVK3n00Udb3Bfw7o9fvnw5iYmJ5OXlMXv2bL785S/z2muvMXToUFauXAlAYWEh1dXVfP/73+fll18mNTWV5557jrvuuovHH3+cJUuW8MUXXxATE8Phw4fb8ycxxpge79XNOSTEBv9KnB738pxw8s4773D55ZcTERHBoEGDmD9/PuvXrycxMZFZs2aRluZd2jBjxgx27drFnDlzWtzeWWedxYABAxrmTzzxRIYMGQLAcccdx4IFCwCYOnUqb7zxBgD//Oc/2bp1a8M6RUVFFBcX89Zbb/HSSy8BcN5555GUlNTq/qgqP/rRj3jrrbfw+Xzs27ePAwcOMHXqVG677TbuuOMOzj//fObOncvmzZvZvHkzZ511FgC1tbUNdZ02bRpXXHEFF154IRde2K47MY0xpkerrq1j9dYDfGniIDYHuU6vC/gtHYl3lsmTJwc8117fTR5ITExMw3RERERQ5+X79u3b7DZ8Pl/DvM/na9heXV0da9euJS4u7qjtBbr9rbl9Ae/6gtzcXDZs2EBUVBTp6elUVFQwbtw4NmzYwCuvvMKdd97JggULuOiii5g8eTJr1649ajsrV67krbfeYsWKFfz85z9ny5YtREb2uo+qMcY0a93nBRSWV7NwymDuD3Ide5Z+FzjjjDOorKzkj3/8Y0Pa+vXrSUpK4rnnnqO2tpbc3FzeeustZs2a1aV1W7BgAQ899FDD/MaNGwGYN28ey5YtA+DVV1/l0CHvicbN7cubb75JYWEhAwcOJCoqijfeeIPdu703Nu7fv58+ffrw9a9/ndtuu40PPviA8ePHk5ub2xDwq6ur2bJlC3V1dezdu5fTTz+de++9l8OHD1NSUtIlbWGMMeHi1c3Z9ImOYP644G9Ft8OmLiAiLF++nFtuuYUlS5YQGxtLeno6999/PyUlJUyfPh0R4d5772Xw4MF88sknXVa3Bx98kBtuuIFp06ZRU1PDvHnzeOSRR7j77ru5/PLLmTlzJvPnz2fEiBGt7svkyZO54IILyMjIYMaMGUyYMAGAjz/+mNtvvx2fz0dUVBQPP/ww0dHRvPjii9x0000UFhZSU1PDLbfcwrhx4/j6179OYWEhqsqtt95K//79u6w9jDGmu6utU1ZtOcDp4wcSGxUR9HrSUrdyOMrIyNDMzMxGadu2bWPixIkhqpHpDPY3Ncb0Vu9/UcCl/7uW311+PBdMH4qIbFDVQBfWN2Jd+sYYY0wYeXVzNtGRPk6fEPBp882ygG+MMcaECVVl1eYc5o1NIT6mbWflLeAbY4wxYWJTViH7CytYOGVIm9e1gG+MMcaEidc25xDpE86aOKjN61rAN8YYY8KAqvLa5mxOPi6Zfn2i2ry+BXxjjDEmDHySU8yu/DIWThncrvUt4HeRiIgIZsyY0TA0fQ6+v127dvH000+3uk3/l+dkZmZy0003Ad7Ld959992GfI888ghLly49th0wxhgTUq9uzkEEFkxqX8C3B+90kbi4uIan2LWmPuB/7WtfC3r7GRkZZGR4t2GuWbOG+Ph4TjnlFIBGb7YzxhgTnl7bnM2J6QNITYhpPXMAdoQfQrt27WLu3LnMnDmTmTNnNhyVL168mLfffpsZM2Zw3333NZvP35o1azj//PPZtWsXjzzyCPfddx8zZszg7bff5qc//Sm/+c1vANi5cycLFy7khBNOYO7cuQ1P9XvhhReYMmUK06dPb3hjnzHGmO5hZ24J2w+UcE47u/OhNx7hv7oYcj7u2G0OngrnLGkxS3l5OTNmzABg1KhRLF++nIEDB7J69WpiY2P57LPPuPzyy8nMzGTJkiX85je/4e9//zsAZWVlAfMFkp6eznXXXUd8fDy33XYbAP/6178all977bU88sgjjB07lnXr1nH99dfz+uuvc88997Bq1SqGDRtmr6Q1xphu5rXNOQDtPn8PvTHgh0igLv3q6mpuvPFGNm7cSEREBNu3bw+4brD5WlNSUsK7777LJZdc0pBWWVkJwKmnnso3v/lNLr30Ur7yla+0a/vGGGM6x2ubc5gxvD9D+h39ZtNg9b6A38qReFe67777GDRoEJs2baKuro7Y2Nhjyteauro6+vfvH/BagkceeYR169axcuVKZsyYwcaNG0lOTm5XOcYYYzrO3oIyPt5XyOJzJhzTduwcfggVFhYyZMgQfD4ff/7zn6mtrQUgISGB4uLiVvM1p+n69RITExk1ahQvvPAC4N3TuWnTJsA7t3/SSSdxzz33kJKSwt69eztqN40xxhyD+u78Yzl/DxbwQ+r666/nySefZPbs2Wzfvp2+ffsCMG3aNCIjI5k+fTr33Xdfs/mac8EFF7B8+fKGi/b8LVu2jMcee4zp06czefJkXn75ZQBuv/12pk6dypQpU5g3bx7Tp0/vnJ02xhjTJis/zmby0ERGJrf83d8aez2uCUv2NzXG9Ab7Dpdz6pLXuf3s8dxw+piAeez1uMYYY0yYe/XjbADOm9r2l+U0ZQHfGGOM6ab+/pHXnZ+ecmzd+dCLAn5PO3XRm9nf0hjTG+w7XM7GvYc5twOO7qGXBPzY2Fjy8/MtUPQAqkp+fn67b000xphw0ZHd+RDkffgisgsoBmqBGlXNEJEBwHNAOrALuFRVD4mIAA8A5wJlwDdV9QO3nauAH7vN/kJVn3TpJwBPAHHAK8DNqqrNldHWnUxLSyMrK4vc3Ny2rmq6odjYWNLS0kJdDWOM6VQd2Z0PbXvwzumqmuc3vxj4l6ouEZHFbv4O4BxgrBtOAh4GTnLB+24gA1Bgg4iscAH8YeBa4D28gL8QeLWFMtokKiqKUaNGtXU1Y4wxJiTqu/NvP3t8h23zWLr0FwFPuukngQv90peq5z2gv4gMAc4GVqtqgQvyq4GFblmiqq5Vr899aZNtBSrDGGOM6bE6ujsfgg/4CvxDRDaIyLUubZCqZgO48UCXPgzwf0xblktrKT0rQHpLZTQiIteKSKaIZFq3vTHGmHDX0d35EHzAP1VVZ+J1198gIi29P1UCpGk70oOmqo+qaoaqZqSmprZlVWOMMaZb6eir8+sFFfBVdb8bHwSWA7OAA647Hjc+6LJnAcP9Vk8D9reSnhYgnRbKMMYYY3qkzujOhyACvoj0FZGE+mlgAbAZWAFc5bJdBbzsplcAV4pnNlDouuNXAQtEJElEktx2VrllxSIy213hf2WTbQUqwxhjjOmR6p+d35Hd+RDcVfqDgOVeLCYSeFpVXxOR9cDzInI1sAeof8n6K3i35O3Auy3vWwCqWiAiPwfWu3z3qGqBm/4eR27Le9UNAEuaKcMYY4zpcfYdLufDPR17dX69VgO+qn4OHPXqNFXNB84MkK7ADc1s63Hg8QDpmcCUYMswxhhjeqLO6s6HXvKkPWOMMSYcdFZ3PljAN8YYY7qF+u78jr46v54FfGOMMaYb6MzufLCAb4wxxnQLndmdDxbwjTHGmJDr7O58sIBvjDHGhNzz6/ciAhdMG9ppZVjAN8YYY0KouraOZ97fw/xxqYxI7tNp5VjAN8YYY0Jo9dYDHCyu5MqTR3ZqORbwjTHGmBBaunYXaUlxzB8X8IWwHcYCvjHGGBMinx0o5r3PC7jipJFE+AK9PLbjWMA3xhhjQuSp93YTHeHj0oy01jMfIwv4xhhjTAiUVNbwlw/2cf60ISTHx3R6eRbwjTHGmBD464f7KKms4eudfLFePQv4xhhjTBdTVZ56bzeThyZy/PD+XVKmBXxjjDGmi2XuPsQnOcV8Y/ZIRDr3Yr16FvCNMcaYLrZ07W4SYiNZNGNYl5VpAd8YY4zpQgeLK3htczaXnDCcuOiILivXAr4xxhjThZ5fv5fqWuWK2SO6tFwL+MYYY0wXqamtY9m6PcwZk8JxqfFdWrYFfGOMMaaL/OuTg2QXVvCNLroVz58FfGOMMaaLPPXebob0i+XMCZ373PxALOAbY4wxXeDTnGLe/iyPr80aQWRE14dfC/jGGGNMF7hv9XYSYiJD0p0PFvCNMcaYTvdxViGvbcnh6rmj6N8nOiR1CDrgi0iEiHwoIn9386NEZJ2IfCYiz4lItEuPcfM73PJ0v23c6dI/FZGz/dIXurQdIrLYLz1gGcYYY0w4+Z/Vn9K/TxTfnjMqZHVoyxH+zcA2v/lfA/ep6ljgEHC1S78aOKSqY4D7XD5EZBJwGTAZWAj8wf2IiAB+D5wDTAIud3lbKsMYY4wJCxt2F/DGp7l8d95xJMZGhaweQQV8EUkDzgP+5OYFOAN40WV5ErjQTS9y87jlZ7r8i4BnVbVSVb8AdgCz3LBDVT9X1SrgWWBRK2UYY4wxYeG3/9hOSnw0V50SmnP39YI9wr8f+H9AnZtPBg6rao2bzwLqHwg8DNgL4JYXuvwN6U3WaS69pTKMMcaYbu/dHXm8uzOf608bQ5/oyJDWpdWALyLnAwdVdYN/coCs2sqyjkoPVMdrRSRTRDJzc3MDZTHGGGO6lKry29XbGZwYy9dO6trH6AYSzBH+qcCXRWQXXnf7GXhH/P1FpP7nShqw301nAcMB3PJ+QIF/epN1mkvPa6GMRlT1UVXNUNWM1NTUIHbJGGOM6VxrtueyYfchbjxjDLFRXfeSnOa0GvBV9U5VTVPVdLyL7l5X1SuAN4CLXbargJfd9Ao3j1v+uqqqS7/MXcU/ChgLvA+sB8a6K/KjXRkr3DrNlWGMMcZ0W6rK//xjO2lJcVyaMbz1FbrAsdyHfwfwAxHZgXe+/TGX/hiQ7NJ/ACwGUNUtwPPAVuA14AZVrXXn6G8EVuHdBfC8y9tSGcYYY0y39Y+tB/h4XyE3nzmW6Mju8cgb8Q6ke46MjAzNzMwMdTWMMcb0UnV1yjkPvE11bR3/uHVepz9GV0Q2qGpGa/m6x88OY4wxpof4+8fZfHqgmFvOGheSZ+Y3p/vUxBhjjAlzNbV13L96O+MHJXD+1CGhrk4jFvCNMcaYDvLyxv18nlfKrWeNxecLdHd56FjAN8YYYzpAdW0dD77+GZOHJnL25MGhrs5RLOAbY4wxHWD5B/vYnV/GrV8ah/d0+O7FAr4xxhhzjKpqvKP7aWn9OHPiwFBXJyAL+MYYY8wx+ssHWWQdKufWs7rn0T1YwDfGGGOOSWVNLQ+9voPjR/TntHHd9/HuFvCNMcaYY/B8Zhb7Dpd323P39SzgG2OMMe1UUV3L71/fQcbIJOaOTQl1dVpkAd8YY4xpp+fW7yWnqIIfdONz9/Us4BtjjDHtUFFdy+/f2MFJowZw8nHJoa5OqyzgG2OMMe2wbN0eDhZXdusr8/1ZwDfGGGPaqKyqhofX7ODUMcnMHt39j+4BIkNdAWOMMSbcPPXebvJKqnjkS+NCXZWg2RG+McYY0wYFpVX8Yc1O5o5NISN9QKirEzQL+MYYY0wb/HLlNkora/jJ+ZNCXZU2sYBvjDHGBOndnXn85YMsvjvvOMYOSgh1ddrEAr4xxhgThIrqWn68fDMjk/tw4xljQl2dNrOL9owxxpggPLxmJ5/nlbL027OIjYoIdXXazI7wjTHGmFbsOFjCw2tm1Og0AAAgAElEQVR2smjGUOZ14xfktMQCvjHGGNMCVeWu5R8TG+Xjx+eF14V6/izgG2OMMS34ywf7WPdFAXeeO5HUhJhQV6fdLOAbY4wxzSgoreKXK7eSMTKJr2YMD3V1jokFfGOMMaYZv3plG8UVNfzyoqn4fN3/efktaTXgi0isiLwvIptEZIuI/MyljxKRdSLymYg8JyLRLj3Gze9wy9P9tnWnS/9URM72S1/o0naIyGK/9IBlGGOMMZ1t7c58XtyQxbXzRjN+cHjdcx9IMEf4lcAZqjodmAEsFJHZwK+B+1R1LHAIuNrlvxo4pKpjgPtcPkRkEnAZMBlYCPxBRCJEJAL4PXAOMAm43OWlhTKMMcaYTlNRXctdyz9m+IA4vn/G2FBXp0O0GvDVU+Jmo9ygwBnAiy79SeBCN73IzeOWnyneewMXAc+qaqWqfgHsAGa5YYeqfq6qVcCzwCK3TnNlGGOMMZ3modd38HleKb+6aCpx0eF3z30gQZ3Dd0fiG4GDwGpgJ3BYVWtclixgmJseBuwFcMsLgWT/9CbrNJee3EIZTet3rYhkikhmbm5uMLtkjDHGBPRpTjGPvLmTrxw/jLljw/Oe+0CCCviqWquqM4A0vCPyiYGyuXGgqxq0A9MD1e9RVc1Q1YzU1J7zxzHGGNO1auuUxS99RGJcFD8Os5fjtKZNV+mr6mFgDTAb6C8i9Y/mTQP2u+ksYDiAW94PKPBPb7JOc+l5LZRhjDHGdLin3tvNh3sO85/nT2RA3551nXgwV+mnikh/Nx0HfAnYBrwBXOyyXQW87KZXuHnc8tdVVV36Ze4q/lHAWOB9YD0w1l2RH413Yd8Kt05zZRhjjDEdav/hcu597RPmjUvlwhkBzyCHtWBenjMEeNJdTe8DnlfVv4vIVuBZEfkF8CHwmMv/GPBnEdmBd2R/GYCqbhGR54GtQA1wg6rWAojIjcAqIAJ4XFW3uG3d0UwZxhhjTIdRVX7y8mbqFH554RS868Z7FvEOpHuOjIwMzczMDHU1jDHGhJGVH2Vzw9MfcNe5E/nOvNGhrk6biMgGVc1oLZ89ac8YY0yvVlhWzd0rtjB1WD++dWp6qKvTaYLp0jfGGGN6rP96dRuHyqp44lsnEhnRc4+De+6eGWOMMa147/N8nl2/l2vmjGLKsH6hrk6nsoBvjDGmVyquqOb2FzcxYkAfbvnSuFBXp9NZl74xxphe6ed/38q+Q+W8cN3JPebxuS2xI3xjjDG9zqotOTyfmcX1p43hhJEDQl2dLmEB3xhjTK9ysLiCO1/6mCnDErnpzJ7xJrxgWMA3xhjTa6gqd7z4EaWVNdz/1RlER/aeMNh79tQYY0yvt2zdHt74NJc7z5nAmIEJoa5Ol7KAb4wxplf4PLeEX67cxtyxKVx5cnqoq9PlLOAbY4zp8apr67j1+U1ER/r4zSXT8fl63rPyW2O35RljjOnxHnp9B5v2Hub3X5vJoMTYUFcnJOwI3xhjTI/24Z5DPPTGDr5y/DDOmzYk1NUJGQv4xhhjeqzC8mq+/8yHDE6M5aeLJoe6OiFlXfrGGGN6pPpb8HIKK3jhupNJjI0KdZVCyo7wjTHG9EhL1+7mtS053LFwAsePSAp1dULOAr4xxpgeZ/O+Qn65chtnTBjI1XNGhbo63YIFfGOMMT1KcUU1Nzz9Acnx0fy2l96CF4idwzfGGNNjqCp3vvQxWYfKefba2ST1jQ51lboNO8I3xhjTYzz9/h7+/lE2PzhrHCem94634AXLAr4xxpgeYVt2ET/721bmjk3he/OPC3V1uh0L+MYYY8Je/Xn7/nFR3PfVGXbePgA7h2+MMSas5RRW8O0n1rM7v4w/Xz2LlPiYUFepW7KAb4wxJmxt2V/I1U9kUlxRzZ+uyuCU41JCXaVuq9UufREZLiJviMg2EdkiIje79AEislpEPnPjJJcuIvKgiOwQkY9EZKbftq5y+T8Tkav80k8QkY/dOg+KiLRUhjHGGPPGJwe55JG1iMAL153C6eMHhrpK3Vow5/BrgB+q6kRgNnCDiEwCFgP/UtWxwL/cPMA5wFg3XAs8DF7wBu4GTgJmAXf7BfCHXd769Ra69ObKMMYY04v9ee0urn5yPaNS+vLXG05l0tDEUFep22s14Ktqtqp+4KaLgW3AMGAR8KTL9iRwoZteBCxVz3tAfxEZApwNrFbVAlU9BKwGFrpliaq6VlUVWNpkW4HKMMYY0wvV1ik///tW/vPlLZw+fiDPf/fkXvu627Zq0zl8EUkHjgfWAYNUNRu8HwUiUt+XMgzY67dalktrKT0rQDotlNG0Xtfi9RAwYsSItuySMcaYMFFSWcOtz21k9dYDfPOUdP7z/ElE2NX4QQs64ItIPPAX4BZVLXKn2QNmDZCm7UgPmqo+CjwKkJGR0aZ1jTHGdH/bsou4YdkH7Mov5e4LJvGtU+35+G0V1H34IhKFF+yXqepLLvmA647HjQ+69CxguN/qacD+VtLTAqS3VIYxxpheQFV59v09XPj7f1NSWcPT35ltwb6dgrlKX4DHgG2q+j9+i1YA9VfaXwW87Jd+pbtafzZQ6LrlVwELRCTJXay3AFjllhWLyGxX1pVNthWoDGOMMT1caWUNP3h+E4tf+phZowbwys1zmT06OdTVClvBdOmfCnwD+FhENrq0HwFLgOdF5GpgD3CJW/YKcC6wAygDvgWgqgUi8nNgvct3j6oWuOnvAU8AccCrbqCFMowxxvRgn+YUc/2yDXyRV8oPzhrHDaePsfP1x0i8C+N7joyMDM3MzAx1NYwxxrSDqvLChix+8vJm4mOiePDyGfYwnVaIyAZVzWgtnz1pzxhjTLeQX1LJXcs389qWHE4encwDl89gYILdctdRLOAbY4wJudVbD3DnSx9RVF7D4nMm8J25o60Lv4NZwDfGGBMyxRXV3PO3rbywIYuJQxJ56prpTBhsT83rDBbwjTHGhMS7O/O4/YWPyC4s54bTj+PmM8cRHWlvbe8sFvCNMcZ0qcNlVdz/z8944t1djErpywvXncIJI+3daJ3NAr4xxpguUVlTy9J3d/PQGzsoqqjmqpNHcsc5E+gTbaGoK1grG2OM6VR1dcrfPtrPf6/6lKxD5cwfl8qd506wc/VdzAK+McaYTvPe5/n81yvb2JRVyKQhiTx19TTmjLX76kPBAr4xxpgO90lOEb9Z9Sn/3HaQIf1i+e0l07no+GH47Fa7kLGAb4wxpsPsyS/jvn9u568b9xEfE8ntZ4/n6jmjiI2KCHXVej0L+MYYY47ZweIKHnp9B8+8vwefCN+ddxzXzR9N/z7Roa6acSzgG2OMabfC8mr+982d/N+/d1FdW8dXTxzOTWeOZVCiPRK3u7GAb4wxpl22ZRfxnaWZZB0qZ9GModz6pXGkp/QNdbVMMyzgG2OMabPXNmfzg+c3kRAbyUvXn8LMEfbgnO7OAr4xxpig1dUpD77+Gff/8zNmDO/Po984gYHWfR8WLOAbY4wJSmllDT98fhOvbcnhP2am8cuLptjV92HEAr4xxphW7S0o4ztLM9l+oJgfnzeRq+eMQsTuqQ8nFvCNMcY0UlFdS3ZhBfsOlbPvcBn7DpXz1Lo91NTW8X/fmsX8camhrqJpBwv4xhjTy+04WMKKjft467M89h0uJ7e4stFyEZgytB8PXDaD0anxIaqlOVYW8I0xphfKLiznb5v28/LG/WzZX4RP4ISRSZwxfiBD+8cxLCmOYf3jSEuKY1BirL2nvgewgG+MMb1ETmEFr39ykBWb9rHuiwJUYXpaP/7z/ElcMG2IXW3fw1nAN8aYHqqksob3dubzzo483tmRx46DJQCMTu3LLWeO48szhjLKHpTTa1jAN8aYHqCuTtl3uJxt2UVs3l/Euzvy2Lj3MDV1SmyUj1mjkvlqxnDmjE1hwuAEu8K+F7KAb4wxYaaksoZPsovYllPMJ9lFfJJTzKc5xZRU1gBHLrL7zrzRzB2TwsyRSXa/vGk94IvI48D5wEFVneLSBgDPAenALuBSVT0k3k/GB4BzgTLgm6r6gVvnKuDHbrO/UNUnXfoJwBNAHPAKcLOqanNlHPMeG2NMmFBVDhRVsjW7kK37i9iaXcTW/UXsyi9ryJMQG8nEwYl8ZeYwJgxOZMKQBMYPSqBvjB3PmcZEVVvOIDIPKAGW+gX8e4ECVV0iIouBJFW9Q0TOBb6PF/BPAh5Q1ZNc8M4EMgAFNgAnuB8J7wM3A+/hBfwHVfXV5spobYcyMjI0MzOzPW1hjDEhU1unfJ5b0hDUt7gAX1Ba1ZBnZHIfJg1JZNKQRCYOSWTi0ESG9ou17vleTkQ2qGpGa/la/Qmoqm+JSHqT5EXAaW76SWANcIdLX6rer4j3RKS/iAxxeVeraoGr3GpgoYisARJVda1LXwpcCLzaQhnGGBPWyqpq+CSnuOGofcv+Ij7NKaKiug6A6Agf4wbHc9bEQUwcksDkYf2YMDiBhNioENfchLP29vkMUtVsAFXNFpGBLn0YsNcvX5ZLayk9K0B6S2UYY0xYKKmsYW9BGXsLytiRW9IQ4L/IK6W+c7VfXBQThyRwxUkjmTQkkcnDEjkuNZ6oCLvv3XSsjj7JE6hfSduR3rZCRa4FrgUYMWJEW1c3xpg2U1UKSqvILqwgp7CC7KIK9h8ubwjwew+VN+qOBxg+II5JQxJZNH0Yk4YmMsm65E0Xam/APyAiQ9yR9xDgoEvPAob75UsD9rv005qkr3HpaQHyt1TGUVT1UeBR8M7ht3OfjDGmQUV1LfsPl7PvcLl7pnw5WW6cU1hBTlEFVTV1jdaJ9AlpSXEMH9CHhcP6MTypDyMG9GH4gDhGJvelX5x1yZvQaW/AXwFcBSxx45f90m8UkWfxLtordAF7FfArEUly+RYAd6pqgYgUi8hsYB1wJfC7Vsowxph2q6ypJaewgv2HK8gtqSS3+MiQ5+YPuml/PoEh/bzHzR4/oj+D+8UyJDGWwf3iGNIvliH9YkmOjyHCZ0frpnsK5ra8Z/COzlNEJAu4Gy8IPy8iVwN7gEtc9lfwrtDfgXdb3rcAXGD/ObDe5bun/gI+4HscuS3vVTfQQhnGGHOU2jqvi70+aNePswsryC4sJ7vQ63LPK6k6at2oCCE1PoaUhBiG9ItlWlo/73ny7pnyaUlxDE6MJdLOq5sw1upteeHGbsszJrxVVNdSVF5NYXk1RRXeuLC8msKyagrLa47Ml1c35MsvraKgtJK6AF9nfaMjGNo/jiH94xjaL9abduOBCTGkJsTQLy7KzqObsNVht+UZY3qvujqlqraO6to6auuU6lqlpq6OmlqluraO6lqlqqaOqtpaKmvqvOmaOipr6iivrqWiupayqlrKq2opr/bGZVW1lFXVUFpVS1mlNy6trKGsqobiihoqm5wXb6pvdAT94qJIjIuiX1wUI5P7cPyI/qQmxJASH9NkHE18TKQFc2OwgG9Mj6DqBeb6gNtSIK4fV9bUUlVTR0V1LYXlNRSUVpJXWkV+SSX5JVXkl1ZxqKyKjuoEjI3yERcVQZ/oSPpER9A3JpK+MREk9Y2mb3QEfWIiiY+JbBTM/YfE2EgS46LsdjVj2skCvjHdhKpSVF7DweKKhovGcosryS2pJK/YOzedV1LJ4bLqhqNuL7h7Af5YJcZGkhIfQ3J8NMelxjNrVDTJfaOJjY4g0idE+nxERQiRET5vPkKIjoggOtLnDRHeOMbNx0VFEBcdQZ/oCGIjI/DZxWzGhJQFfGO6QFlVTcOtXAeKKsgprOSAmz5YXOkF+aLKgN3Z/heUDU6MZfzgBGIiI4iOEKJckD0yFhd4GwfiGJcnNsrnresCc0yUtzwhNoroSDtyNqYns4BvzDEqLKtmd0EpOYUVHCiu5IBfYPeCewVFFTVHrZcQE8nAxBgGJsQyc0QSgxJjGy4iG5gQS2pCDKnxMSTG2TloY8yxs4BvTDvsLShj1ZYc/rHlAJm7CxpdHR7h847IB/WLJT25L7NHJzMo0btPe3BiLIPc2N5mZozpSvaNY0wQVJVPDxSzavMBVm3JYWt2EQATBidw4xljmTI0kcEukNvDV4wx3ZEFfGNa8UVeKbc8t5FNew8jAjNHJPGjcyewYNJg0lP6hrp6xhgTFAv4xrRg+YdZ/Hj5ZqIifdyzaDILpwxmYEJsqKtljDFtZgHfmABKK2v4yctb+MsHWZyYnsQDlx3P0P5xoa6WMca0mwV8Y5rYur+IG5/5gC/ySrnpjDHcdOZYe4a6MSbsWcA3xlFV/vzebn6xchv946JYds1JnHJcSqirZYwxHcICvulxauuUvBLvwTYHiyo54B5qc7C4koNFFRSWVzc8na66to6aOm9cUV1HXkklp41P5TeXTCclPibUu2KMMR3GAr4JO6rea1B35ZeSdaicvQVlZB0q96YPlbH/cHnAR82mxEeTmhBLUp8o+sZEeo+J9fmIivQR5R4VO314fy4/cYQ9BtYY0+NYwDfdVmFZNXsKyvgiv5Qvckv5Iq+EL/JK+SKv9Kgn16XERzMsqQ9Th/XjnClDGJYUx6CEGO/pdYne29PspSvGmN7MAr4JGVUlt6SS7Tkl7C4oZU9BGXsLythTUMae/LJGQV0EhvaLY1RKX748YyijUuJJT+7DiAF9SEvqQ1x0RAj3xBhjuj8L+KZLlFTWsP1AMZ/m+A0HiikorWrIEx3hI21AHCMG9OH44UmMGNCH4QP6MCqlLyOT+xAbZUHdGGPaywK+6TC1dcr+w+XsyC3h89xSPq8f55VwoKiyIV+f6AjGDUrgrImDGD84gfGDExid2pdBCbF27twYYzqJBXzTLoXl1XySXcQnOcVsyy5iW04x23OKKa+ubciTGBvJcQPjmTMmldGpfRk7MJ4JgxNJS4qzwG6MMW1VUwUFn0Pedjd85o2DZAHftEhVOVBUyeZ9hWzeX8iW/UVs3V/EvsPlDXn694li4uBELps1nPGDEhidGs/o1L4k942217oaY0xbVJXBoV1u+MIbF3zhBfpDu0CPHFSROAxSxga9aQv4pkFxRXXDVfDbDxSzeV8RW/YXklfinWcXgVEpfZk5MokrZo9g4uBEJg5JZFBijAV2Y4wJRnU5FGbB4d1weI8b9h6ZLslpnD8mEZLSYfAUmPIVSBnnBfnkMRCT4OW5KrjvXwv4vVB5VS2bsg7zUdZhvsgrZWeuF+Rzi4+cZ4/wCWMHxnPa+IFMGZrIlGH9mDgk0d7hbowxgVSVQslBKM31xiU5UJQNxTlQnH1kKD/UeD1fJPRLg/4jYMyXYEA6JI3yhgGjIC7JO9rqAPbt3QvkFleyYXcB63cdInP3IbbsK6SmznswzYC+0YxO6ctp41IZnRrPqJS+jE71roqPibSr4o0xvVR1OZTlQ2kelOVBab4b+82X5kLpQSjJherSo7chERA/CBIGewF85CnedL8RXoDvPxwShoCva75rLeCHMVWlrKqW/JIqDhZXcKDIe5xs/aNkcworyDpcxt4C73x7dKSP6Wn9uGbuaE5MT+L4EUkM6Bsd4r0wxphOVFMFlcVQWegdXZcfgvLDTcYFRwJ5Wb4XzAMFcPCOyPukQF83JJ0IfQdCfKobD4S+qV5g75vaZcE8GBbwu6HSyhrvOfDFfs+DL6ogv7SK/NIqCkorKSjxpitr6o5aPzrSx6DEGAYlxDI9rT/fmD2SE0YOYMqwRDtqN8Z0H6pQU+EN1RV+0+VHxvVDTf102ZFxld90/XxVMVQUuSBfDLWVLdchOt7rNu+T7AXwlPFuOvlIYG8YJ0Nsvw7rYu9q3T7gi8hC4AEgAviTqi4JcZWCVlunHCiqYG9BGXvdM9/3HiqjsKyaypo6qmrqqKyto7K6lqpab/5wWTUllTVHbSs2ykdKfAzJfaNJjY9h/KBEkuOjGdDXGwYmxDC4XyyDEmLp3yfKLqIzJtypuqEW6mqbjOu8sdZ5Q53ftH9aXY031K9bP19XA7V+03XVR5areuujjafragMH55oK7yi6rhpqq922qxpP11QeyVdT4c3XurT2iuoDUXFu7DcdPxiSx3oXtMUmeuMYN45LajzE9ofI3tPL2a0DvohEAL8HzgKygPUiskJVtza3jgJVNXUoeuT/BaVOvQBcV6fU1Cl16sZuvqa2jsqaukZvUasPwpU1XlCucGP/+fKqWsqrvaHCb/pQaRX7mrzERQQGJ8aSHB9NdISPmMgI+kdHEZ0QQ0ykj+hIH/3johmYGNNwhD4wMYaBibEkxER2vyBe38D4jevTCbCsxXGT9fy/cJqOAy1rbnmg6Rb3qf6LsukXrP8Xqh795VqfTtNl/nWoo9k6B1xe12R72rjso9bxr1uAtOZ3ukng8A8yLbTZUfsbYN9b+rs3bMNv7P95aJqnxc9Zk+UNY4JYN4hxQ3WC+Cwe9XchcP6j1q1rXF63Jl6AjYx1QzT4oiAiGiIi3XSU1/0d3dflifHGEdFH1omM89Kj3Lh+PjLWL5jH+Q19jizrbt+HYaBbB3xgFrBDVT8HEJFngUVAswG/cv8Wdt0z5aj0lj4agiJALBDXhn+2CFFEBBHw4X3+fG4+QiAyXoj0uSFCiBT3Ga11Q1WTDWqTiYBfgs0sbzZ/0y8UCPjlEtSXZ5PyTIgJiM/7UInPzYt3odBRaUKL/wXi8wZfhFvfBz7fkfTW1msYmpTb6pjGaYhXbsP2/fI0zDe3btPlgZYReN321FWaydfS/te3ZX1bNVoeKM39PX0+N47wG/uOjMV35O/m/7f0RR49logjwdgX4QVnX6SX5v93bFonX8SRYOuLtIAbhrp7wB8G7PWbzwJOappJRK4FrgWYMDQe38AJbgGI+2cXL6P3/4M0/K/WT3uBWvCJ4PO5sYCIEOHz4fMJEYIbe3n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       "  s.onreadystatechange = s.onload = callback;\n",
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       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el131398185482328805784658126\", {\"width\": 576.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [0.0, 65.0], \"ylim\": [-29632.75, 622661.75], \"xdomain\": [0.0, 65.0], \"ydomain\": [-29632.75, 622661.75], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0], \"tickformat\": [\"2020-01-22\", \"2020-02-01\", \"2020-02-11\", \"2020-02-21\", \"2020-03-02\", \"2020-03-12\", \"2020-03-22\", \"\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818548099840\", \"lines\": [{\"data\": \"data01\", 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214703.0, 8733.0], [57.0, 242378.0, 9867.0], [58.0, 271903.0, 11299.0], [59.0, 304263.0, 12973.0], [60.0, 337275.0, 14659.0], [61.0, 377902.0, 16498.0], [62.0, 417892.0, 18623.0], [63.0, 467504.0, 21179.0], [64.0, 529342.0, 23966.0], [65.0, 593012.0, 27192.0]], \"data02\": [[0.020161290322580655, 0.9379139072847682, 0.8689293598233996], [0.0649641577060932, 0.9379139072847682, 0.8689293598233996]], \"data03\": [[0.015681003584229386, 0.8252391464311994], [0.2702172939068101, 0.8252391464311994], [0.2746975806451613, 0.8252391464311994], [0.2746975806451613, 0.8344370860927152], [0.2746975806451613, 0.9678072111846947], [0.2746975806451613, 0.9770051508462105], [0.2702172939068101, 0.9770051508462105], [0.015681003584229386, 0.9770051508462105], [0.011200716845878145, 0.9770051508462105], [0.011200716845878145, 0.9678072111846947], [0.011200716845878145, 0.8344370860927152], [0.011200716845878145, 0.8252391464311994], [0.015681003584229386, 0.8252391464311994]]}, \"id\": 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el131398185482328805784658126\", {\"width\": 576.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [0.0, 65.0], \"ylim\": [-29632.75, 622661.75], \"xdomain\": [0.0, 65.0], \"ydomain\": [-29632.75, 622661.75], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0], \"tickformat\": [\"2020-01-22\", \"2020-02-01\", \"2020-02-11\", \"2020-02-21\", \"2020-03-02\", \"2020-03-12\", \"2020-03-22\", \"\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818548099840\", \"lines\": [{\"data\": \"data01\", 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0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818514369336\", \"dasharray\": \"none\", \"alpha\": 0.8, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Date\", \"position\": [0.5, -0.09657836644591611], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818548456192\"}, {\"text\": \"Covid-19 total cases and fatalities (world)\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818514283880\"}, {\"text\": \"ConfirmedCases\", \"position\": [0.08288530465949823, 0.9218175128771156], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818514370344\"}, {\"text\": \"Fatalities\", \"position\": [0.08288530465949823, 0.8528329654157469], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818514408392\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 554.0, 17.0], [1.0, 653.0, 18.0], [2.0, 939.0, 26.0], [3.0, 1432.0, 42.0], [4.0, 2113.0, 56.0], [5.0, 2922.0, 82.0], [6.0, 5573.0, 131.0], [7.0, 6161.0, 133.0], [8.0, 8229.0, 171.0], [9.0, 9920.0, 213.0], [10.0, 12030.0, 259.0], [11.0, 16779.0, 362.0], [12.0, 19870.0, 426.0], [13.0, 23881.0, 492.0], [14.0, 27624.0, 564.0], [15.0, 30783.0, 634.0], [16.0, 34380.0, 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214703.0, 8733.0], [57.0, 242378.0, 9867.0], [58.0, 271903.0, 11299.0], [59.0, 304263.0, 12973.0], [60.0, 337275.0, 14659.0], [61.0, 377902.0, 16498.0], [62.0, 417892.0, 18623.0], [63.0, 467504.0, 21179.0], [64.0, 529342.0, 23966.0], [65.0, 593012.0, 27192.0]], \"data02\": [[0.020161290322580655, 0.9379139072847682, 0.8689293598233996], [0.0649641577060932, 0.9379139072847682, 0.8689293598233996]], \"data03\": [[0.015681003584229386, 0.8252391464311994], [0.2702172939068101, 0.8252391464311994], [0.2746975806451613, 0.8252391464311994], [0.2746975806451613, 0.8344370860927152], [0.2746975806451613, 0.9678072111846947], [0.2746975806451613, 0.9770051508462105], [0.2702172939068101, 0.9770051508462105], [0.015681003584229386, 0.9770051508462105], [0.011200716845878145, 0.9770051508462105], [0.011200716845878145, 0.9678072111846947], [0.011200716845878145, 0.8344370860927152], [0.011200716845878145, 0.8252391464311994], [0.015681003584229386, 0.8252391464311994]]}, \"id\": 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       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el131398185482328805784658126\", {\"width\": 576.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [0.0, 65.0], \"ylim\": [-29632.75, 622661.75], \"xdomain\": [0.0, 65.0], \"ydomain\": [-29632.75, 622661.75], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0], \"tickformat\": [\"2020-01-22\", \"2020-02-01\", \"2020-02-11\", \"2020-02-21\", \"2020-03-02\", \"2020-03-12\", \"2020-03-22\", \"\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818548099840\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818514367936\", \"color\": \"#1F77B4\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818514368944\", \"color\": \"#FF7F0E\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818514407776\", \"color\": \"#1F77B4\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818514408840\", \"color\": \"#FF7F0E\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818514369336\", \"dasharray\": \"none\", \"alpha\": 0.8, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Date\", \"position\": [0.5, -0.09657836644591611], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818548456192\"}, {\"text\": \"Covid-19 total cases and fatalities (world)\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818514283880\"}, {\"text\": \"ConfirmedCases\", \"position\": [0.08288530465949823, 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214703.0, 8733.0], [57.0, 242378.0, 9867.0], [58.0, 271903.0, 11299.0], [59.0, 304263.0, 12973.0], [60.0, 337275.0, 14659.0], [61.0, 377902.0, 16498.0], [62.0, 417892.0, 18623.0], [63.0, 467504.0, 21179.0], [64.0, 529342.0, 23966.0], [65.0, 593012.0, 27192.0]], \"data02\": [[0.020161290322580655, 0.9379139072847682, 0.8689293598233996], [0.0649641577060932, 0.9379139072847682, 0.8689293598233996]], \"data03\": [[0.015681003584229386, 0.8252391464311994], [0.2702172939068101, 0.8252391464311994], [0.2746975806451613, 0.8252391464311994], [0.2746975806451613, 0.8344370860927152], [0.2746975806451613, 0.9678072111846947], [0.2746975806451613, 0.9770051508462105], [0.2702172939068101, 0.9770051508462105], [0.015681003584229386, 0.9770051508462105], [0.011200716845878145, 0.9770051508462105], [0.011200716845878145, 0.9678072111846947], [0.011200716845878145, 0.8344370860927152], [0.011200716845878145, 0.8252391464311994], [0.015681003584229386, 0.8252391464311994]]}, \"id\": \"el13139818548232880\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby(\"Date\").sum()[[\"ConfirmedCases\", \"Fatalities\"]].plot(figsize=(8, 4), title=\"Covid-19 total cases and fatalities (world)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Fatalities and Case Fatality Rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Countries with no fatalities yet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Countries with no fatalities as of 2020-03-27\n",
      "Angola, Antigua and Barbuda, Bahamas, Barbados, Belarus, Belize, Benin, Bhutan, Bolivia, Brunei, Cambodia, Central African Republic, Chad, Congo (Brazzaville), Cote dIvoire, Djibouti, Dominica, El Salvador, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Fiji, Georgia, Grenada, Guinea, Guinea-Bissau, Haiti, Holy See, Kuwait, Kyrgyzstan, Laos, Latvia, Liberia, Libya, Liechtenstein, Madagascar, Maldives, Mali, Malta, Mauritania, Monaco, Mongolia, Mozambique, Namibia, Nepal, New Zealand, Oman, Papua New Guinea, Qatar, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Senegal, Seychelles, Somalia, Sri Lanka, Suriname, Syria, Tanzania, Timor-Leste, Uganda, Uruguay, Vietnam, Zambia\n"
     ]
    }
   ],
   "source": [
    "print(\"Countries with no fatalities as of \" + LAST_DATE)\n",
    "print(*train.groupby(\"Country_Region\").sum()[train.groupby(\"Country_Region\").sum()[\"Fatalities\"] == 0].index.tolist(), sep=\", \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Countries with the most fatalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Country_Region</th>\n",
       "      <th>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9305</th>\n",
       "      <td>Italy</td>\n",
       "      <td>86498.0</td>\n",
       "      <td>9134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13859</th>\n",
       "      <td>Spain</td>\n",
       "      <td>65719.0</td>\n",
       "      <td>5138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4157</th>\n",
       "      <td>China</td>\n",
       "      <td>67801.0</td>\n",
       "      <td>3174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9041</th>\n",
       "      <td>Iran</td>\n",
       "      <td>32332.0</td>\n",
       "      <td>2378.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7787</th>\n",
       "      <td>France</td>\n",
       "      <td>32964.0</td>\n",
       "      <td>1995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19007</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>14543.0</td>\n",
       "      <td>759.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11681</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>8603.0</td>\n",
       "      <td>546.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17027</th>\n",
       "      <td>US</td>\n",
       "      <td>44876.0</td>\n",
       "      <td>527.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8051</th>\n",
       "      <td>Germany</td>\n",
       "      <td>50871.0</td>\n",
       "      <td>342.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1583</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>7284.0</td>\n",
       "      <td>289.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country_Region  ConfirmedCases  Fatalities\n",
       "9305            Italy         86498.0      9134.0\n",
       "13859           Spain         65719.0      5138.0\n",
       "4157            China         67801.0      3174.0\n",
       "9041             Iran         32332.0      2378.0\n",
       "7787           France         32964.0      1995.0\n",
       "19007  United Kingdom         14543.0       759.0\n",
       "11681     Netherlands          8603.0       546.0\n",
       "17027              US         44876.0       527.0\n",
       "8051          Germany         50871.0       342.0\n",
       "1583          Belgium          7284.0       289.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train[\"Date\"] == LAST_DATE].sort_values(\"Fatalities\", ascending=False)[[\"Country_Region\", \"ConfirmedCases\", \"Fatalities\"]].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing countries' [case fatality rate](https://en.wikipedia.org/wiki/Case_fatality_rate) (\"death rate\"). **Careful here, Fatalities/ConfirmedCases is not necessarily the real CFR; this can only be calculated ex post**. Thus, a higher CFR as calculated *here* does not necessarily mean that one country's CFR really is higher, it could very well be because of less/more prevalent testing (example: Country A only tests patients that are already in critical condition, Country B tests the whole population -> Country A's Fatalities/ConfirmedCases - ratio will be much higher).\n",
    "\n",
    "Only countries with at least 100 fatalities are considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean CFR (%): 2.0673159919528663\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el13139818514164032863076760\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el13139818514164032863076760\", {\"width\": 792.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-1.025, 16.025], \"ylim\": [0.0, 11.087770815510185], \"xdomain\": [-1.025, 16.025], \"ydomain\": [0.0, 11.087770815510185], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 16, \"tickvalues\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], \"tickformat\": [\"Italy\", \"Spain\", \"Iran\", \"Netherlands\", \"France\", \"United Kingdom\", \"China\", \"US\", \"US\", \"Belgium\", \"Sweden\", \"Switzerland\", \"Korea, South\", \"US\", \"US\", \"Germany\"], \"scale\": \"linear\", \"fontsize\": 8.33, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 11, \"tickvalues\": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0], \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818513787312\", \"lines\": [], \"paths\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365415664\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365378232\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365417232\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data04\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365418072\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data05\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365418912\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data06\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365460776\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data07\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", 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\"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818364980136\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data15\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818364980976\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data16\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365018744\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}], \"markers\": [], \"texts\": [{\"text\": \"Preliminary Case Fatality Rate in Percent by Country\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818365310904\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[-0.25, 0.0], [0.25, 0.0], [0.25, 10.559781729057319], [-0.25, 10.559781729057319]], \"data02\": [[0.75, 0.0], [1.25, 0.0], [1.25, 7.818134785982744], [0.75, 7.818134785982744]], \"data03\": [[1.75, 0.0], [2.25, 0.0], [2.25, 7.35494247185451], [1.75, 7.35494247185451]], \"data04\": [[2.75, 0.0], [3.25, 0.0], [3.25, 6.346623270951994], [2.75, 6.346623270951994]], \"data05\": [[3.75, 0.0], [4.25, 0.0], [4.25, 6.052056789224609], [3.75, 6.052056789224609]], \"data06\": [[4.75, 0.0], [5.25, 0.0], [5.25, 5.219005707213093], [4.75, 5.219005707213093]], \"data07\": [[5.75, 0.0], [6.25, 0.0], [6.25, 4.68134688278934], [5.75, 4.68134688278934]], \"data08\": [[6.75, 0.0], [7.25, 0.0], [7.25, 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true, \"enabled\": false}]});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el13139818514164032863076760\", {\"width\": 792.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-1.025, 16.025], \"ylim\": [0.0, 11.087770815510185], \"xdomain\": [-1.025, 16.025], \"ydomain\": [0.0, 11.087770815510185], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 16, \"tickvalues\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], \"tickformat\": [\"Italy\", \"Spain\", \"Iran\", \"Netherlands\", \"France\", \"United Kingdom\", \"China\", \"US\", \"US\", \"Belgium\", \"Sweden\", \"Switzerland\", \"Korea, South\", \"US\", \"US\", \"Germany\"], \"scale\": \"linear\", \"fontsize\": 8.33, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 11, \"tickvalues\": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0], \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, 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\"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data04\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365418072\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data05\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365418912\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data06\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365460776\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data07\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", 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1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data11\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818364977616\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data12\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818364978456\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data13\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818364979296\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data14\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818364980136\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data15\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818364980976\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data16\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365018744\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}], \"markers\": [], \"texts\": [{\"text\": \"Preliminary Case Fatality Rate in Percent by Country\", \"position\": [0.5, 1.0275938189845473], 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true, \"enabled\": false}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el13139818514164032863076760\", {\"width\": 792.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-1.025, 16.025], \"ylim\": [0.0, 11.087770815510185], \"xdomain\": [-1.025, 16.025], \"ydomain\": [0.0, 11.087770815510185], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 16, \"tickvalues\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], \"tickformat\": [\"Italy\", \"Spain\", \"Iran\", \"Netherlands\", \"France\", \"United Kingdom\", \"China\", \"US\", \"US\", \"Belgium\", \"Sweden\", \"Switzerland\", \"Korea, South\", \"US\", \"US\", \"Germany\"], \"scale\": \"linear\", \"fontsize\": 8.33, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 11, \"tickvalues\": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0], \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, 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\"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data04\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365418072\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data05\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365418912\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data06\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el13139818365460776\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data07\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", 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true, \"enabled\": false}]});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = train[train[\"Date\"] == LAST_DATE].copy()\n",
    "tmp[\"CaseFatalityRate\"] = tmp[\"Fatalities\"] / tmp[\"ConfirmedCases\"] * 100  # CFR here is Fatalities/ConfirmedCases * 100 (so that it's in percent)\n",
    "print(\"Mean CFR (%):\", tmp[\"CaseFatalityRate\"].mean())\n",
    "\n",
    "heights = tmp[tmp[\"Fatalities\"] >= 100].sort_values(\"CaseFatalityRate\", ascending=False)[\"CaseFatalityRate\"].values\n",
    "bars = tmp[tmp[\"Fatalities\"] >= 100].sort_values(\"CaseFatalityRate\", ascending=False)[\"Country_Region\"].values\n",
    "y_pos = np.arange(len(bars))\n",
    "\n",
    "plt.figure(figsize=(11,4))\n",
    "plt.bar(y_pos, heights, width=0.5)\n",
    " \n",
    "plt.xticks(y_pos, bars, size=\"small\")\n",
    "plt.yticks(np.arange(0.0, 11.0, 1.0))\n",
    "plt.title(\"Preliminary Case Fatality Rate in Percent by Country\")\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Modelling and Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 SIR Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [SIR](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model) Model tries to model infectious disease developments, with the population split up into three groups (\"compartments\"):\n",
    "- **S**usceptible: They can still be infected (healthy)\n",
    "- **I**nfected: Currently infected\n",
    "- **R**ecovered: They are presumed immune (cannot contract the virus again)\n",
    "\n",
    "\"$\\cdot$\" is \"multiplied with\".\n",
    "\n",
    "Let $\\beta$ (beta) be the **probability of transmission from infected to healthy $\\cdot$ the number of people a person is in contact with per day**. Thus, it can be thought of as the **expected amount of people an infected person infects per day** (or any other timestep, I'll use days here). Example: Let the probability of an infected person to infect a healthy/susceptible person be $5 \\%$ and the average number of people a person is in contact with per day be $6$. Then, $\\beta = 0.05 \\cdot 6 = 0.3$, that is, an infected person infects $0.3$ people per day on average.\n",
    "\n",
    "\n",
    "Now one can see that the **number of days than an infected person can spread the disease** is extremely important. Let $D$ be that number. Then, the number of people an infected person infects on average is $=$ **expected amount of people an infected person infects per day $\\cdot $ number of days the person can spread the disease**, and that's exactly $\\beta \\cdot D$. This is the [basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number) $R_{0}$. Continuing the example from above: let $D = 10$, then $R_{0} = \\beta \\cdot D = 0.3 \\cdot 10 = 3$. That means that on average, each infected person infects $3$ others.\n",
    "\n",
    "Now the model below does not use $D$ but $\\gamma$, and that's just $= \\frac{1}{D}$ (Thus, $R_{0} = \\beta \\cdot D = \\frac{\\beta}{\\gamma}$ and $\\beta = R_{0} \\cdot \\gamma$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.integrate import odeint # a lot of the code for SIR from https://scipython.com/book/chapter-8-scipy/additional-examples/the-sir-epidemic-model/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The SIR model differential equations.\n",
    "def deriv(y, t, N, beta, gamma):\n",
    "    S, I, R = y\n",
    "    dSdt = -beta * S * I / N\n",
    "    dIdt = beta * S * I / N - gamma * I\n",
    "    dRdt = gamma * I\n",
    "    return dSdt, dIdt, dRdt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now want to add *cumulative* Deaths $X$ to the model: $X(t) = \\textit{number of deaths from day 0 to day t}$ for $t\\geq 13$, else $0$. \n",
    "\n",
    "Recursively, the number of cumulative deaths on day $t$ is equal to the number of cumulative deaths on day $t-1$ (that's $=X(t-1)$) plus the number of newly infected 13 days prior multiplied with the case fatality rate $\\alpha$ (alpha) (I chose 13 days as that is reported as the average time from infection until death in [this study](https://wwwnc.cdc.gov/eid/article/26/6/20-0320_article)).\n",
    "\n",
    "Now, the number of newly infected 13 days prior (that's the people who can die on day $t$) is equal to the number of infected 13 days prior multiplied with the expected amount of people an infected person infects per day (that's $\\beta$). So the number of newly infected 13 days prior is $\\beta \\cdot I(t-13)$.\n",
    "\n",
    "Putting it all together: $X(t) = X(t-1) + \\alpha \\cdot \\beta \\cdot I(t-13)$.\n",
    "\n",
    "This is equal to the closed form formula $X(t) = \\alpha \\cdot \\beta \\cdot \\displaystyle \\sum_{i=0}^{t-13} I(i)$\n",
    "\n",
    "Proof: induction over $t$ for $t\\geq 13$ (both are $0$ for $t<13$).\n",
    "\n",
    "Base Case: $X(13) = X(12) + \\alpha \\cdot \\beta \\cdot I(13-13) = 0 + \\alpha \\cdot \\beta \\cdot I(0) = \\alpha \\cdot \\beta \\cdot \\displaystyle \\sum_{i=0}^{0} I(i)$.\n",
    "\n",
    "Assume $X(t) = X(t-1) + \\alpha \\cdot \\beta \\cdot I(t-13) = \\alpha \\cdot \\beta \\cdot \\displaystyle \\sum_{i=0}^{t-13} I(i)$ holds for some $t\\geq 13$. Then:\n",
    "\n",
    "$X(t+1) = X(t+1-1) + \\alpha \\cdot \\beta \\cdot I(t+1-13) = X(t) + \\alpha \\cdot \\beta \\cdot I(t-12) \\stackrel{inductive\\, assumption}{=} \\alpha \\cdot \\beta \\cdot \\displaystyle \\sum_{i=0}^{t-13} I(i) + \\alpha \\cdot \\beta \\cdot I(t-12) = \\alpha \\cdot \\beta \\cdot \\displaystyle \\sum_{i=0}^{t-12} I(i) = \\alpha \\cdot \\beta \\cdot \\displaystyle \\sum_{i=0}^{(t+1)-13} I(i)$\n",
    "\n",
    "which was to be shown.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SIR_model(N, D, R_0, CaseFatalityRate, max_days):\n",
    "    '''\n",
    "    N: total population\n",
    "    D, R_0, CaseFatalityRate: see texts above\n",
    "    '''\n",
    "    I0, R0 = 1, 0  # Initial number of infected and recovered individuals (1 infected, 0 recovered) [this R0 has nothing to do with the basic reproduction number R0]\n",
    "    S0 = N - I0 - R0 # Initial number of susceptible (everyone else)\n",
    "\n",
    "    gamma = 1.0 / D  # see texts above\n",
    "    beta = R_0 * gamma  # see texts above\n",
    "    alpha = CaseFatalityRate\n",
    "\n",
    "    t = np.linspace(0, max_days, max_days) # Grid of time points (in days)\n",
    "    \n",
    "    # Initial conditions vector\n",
    "    y0 = S0, I0, R0\n",
    "    # Integrate the SIR equations over the time grid, t.\n",
    "    ret = odeint(deriv, y0, t, args=(N, beta, gamma))\n",
    "    S, I, R = ret.T\n",
    "    \n",
    "    # Adding deaths (see text above)\n",
    "    X = np.zeros(max_days)\n",
    "    for day in range(12, max_days):\n",
    "        X[day] = sum(I[:day-12])\n",
    "    # X[13:] = I[:-13]\n",
    "    X = alpha * X * gamma\n",
    "    \n",
    "\n",
    "    # Plot the data on three separate curves for S(t), I(t) and R(t)\n",
    "    f, ax = plt.subplots(1,1,figsize=(10,4))\n",
    "    ax.plot(t, S, 'b', alpha=0.7, linewidth=2, label='Susceptible')\n",
    "    ax.plot(t, I, 'y', alpha=0.7, linewidth=2, label='Infected')\n",
    "    ax.plot(t, X, 'r', alpha=0.7, linewidth=2, label='Dead')\n",
    "    ax.plot(t, R, 'g', alpha=0.7, linewidth=2, label='Recovered')\n",
    "\n",
    "    ax.set_xlabel('Time (days)')\n",
    "    ax.title.set_text('SIR-Model. Total Population: ' + str(N) + \", Days Infectious: \" + str(D) + \", R_0: \" + str(R_0) + \", CFR: \" + str(CaseFatalityRate*100))\n",
    "    # ax.set_ylabel('Number (1000s)')\n",
    "    # ax.set_ylim(0,1.2)\n",
    "    ax.yaxis.set_tick_params(length=0)\n",
    "    ax.xaxis.set_tick_params(length=0)\n",
    "    ax.grid(b=True, which='major', c='w', lw=2, ls='-')\n",
    "    legend = ax.legend()\n",
    "    legend.get_frame().set_alpha(0.5)\n",
    "    for spine in ('top', 'right', 'bottom', 'left'):\n",
    "        ax.spines[spine].set_visible(False)\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of a SIR Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el13139818548609544214419993\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-18.0, 378.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-18.0, 378.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 10, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818233948704\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818233548360\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818233548752\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el13139818233586464\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el13139818233587248\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818233622992\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818233624056\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el13139818233625120\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el13139818233626184\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818233913128\", \"dasharray\": \"none\", \"alpha\": 0.5, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Time (days)\", \"position\": [0.5, -0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818233857528\"}, {\"text\": \"SIR-Model. 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[0.8266129032258065, 0.35513245033112584]]}, \"id\": \"el13139818548609544\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el13139818548609544214419993\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-18.0, 378.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-18.0, 378.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 10, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818233948704\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818233548360\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818233548752\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el13139818233586464\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el13139818233587248\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818233622992\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818233624056\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el13139818233625120\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el13139818233626184\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818233913128\", \"dasharray\": \"none\", \"alpha\": 0.5, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Time (days)\", \"position\": [0.5, -0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818233857528\"}, {\"text\": \"SIR-Model. Total Population: 1000000, Days Infectious: 14.0, R_0: 2.0, CFR: 5.0\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818233444224\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.5896799116997793], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818233589656\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.5206953642384106], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818233623608\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 0.45171081677704195], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818233624672\"}, {\"text\": \"Recovered\", \"position\": [0.8803763440860216, 0.38272626931567333], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818233625736\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 999999.0, 1.0, 0.0, 0.0], [1.0027855153203342, 999998.8514898848, 1.0742549779037456, 0.0, 0.0742551374416697], [2.0055710306406684, 999998.6919521728, 1.1540237359298107, 0.0, 0.15402409142475162], [3.0083565459610027, 999998.5205680118, 1.2397156969704095, 0.0, 0.23971629133090672], [4.011142061281337, 999998.3364577668, 1.3317706748350877, 0.0, 0.3317715585232498], 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365.63272736260393, 39677.69987587878, 796196.2767195737], [358.99721448467966, 203427.65817177636, 350.42426403910497, 39679.87376210957, 796221.9175641844], [360.0, 203417.6602337514, 335.84790395630785, 39681.95727447389, 796246.4918622922]], \"data02\": [[0.8301971326164875, 0.6057763061074319, 0.5367917586460632, 0.46780721118469465, 0.39882266372332603], [0.8660394265232976, 0.6057763061074319, 0.5367917586460632, 0.46780721118469465, 0.39882266372332603]], \"data03\": [[0.8266129032258065, 0.35513245033112584], [0.9874551971326165, 0.35513245033112584], [0.9910394265232975, 0.35513245033112584], [0.9910394265232975, 0.36433038999264167], [0.9910394265232975, 0.6356696100073583], [0.9910394265232975, 0.6448675496688742], [0.9874551971326165, 0.6448675496688742], [0.8266129032258065, 0.6448675496688742], [0.8230286738351255, 0.6448675496688742], [0.8230286738351255, 0.6356696100073583], [0.8230286738351255, 0.36433038999264167], [0.8230286738351255, 0.35513245033112584], [0.8266129032258065, 0.35513245033112584]]}, \"id\": \"el13139818548609544\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el13139818548609544214419993\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-18.0, 378.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-18.0, 378.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 10, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818233948704\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818233548360\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818233548752\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el13139818233586464\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el13139818233587248\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818233622992\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818233624056\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el13139818233625120\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el13139818233626184\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818233913128\", \"dasharray\": \"none\", \"alpha\": 0.5, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Time (days)\", \"position\": [0.5, -0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818233857528\"}, {\"text\": \"SIR-Model. Total Population: 1000000, Days Infectious: 14.0, R_0: 2.0, CFR: 5.0\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818233444224\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.5896799116997793], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818233589656\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.5206953642384106], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818233623608\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 0.45171081677704195], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818233624672\"}, {\"text\": \"Recovered\", \"position\": [0.8803763440860216, 0.38272626931567333], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818233625736\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 999999.0, 1.0, 0.0, 0.0], [1.0027855153203342, 999998.8514898848, 1.0742549779037456, 0.0, 0.0742551374416697], [2.0055710306406684, 999998.6919521728, 1.1540237359298107, 0.0, 0.15402409142475162], [3.0083565459610027, 999998.5205680118, 1.2397156969704095, 0.0, 0.23971629133090672], [4.011142061281337, 999998.3364577668, 1.3317706748350877, 0.0, 0.3317715585232498], 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SIR_model(N=1_000_000, D=14.0, R_0=2.0, CaseFatalityRate=0.05, max_days=360)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 SIR-Model with Lockdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We now want to find suitable parameters (Days infectious, R_0, CFR) for the SIR model\n",
    "\n",
    "As I said before, the number of confirmed cases is likely far off from the real number (as not the whole population is getting tested) and thus is not very useful to fit our data to a SIR-Model.\n",
    "\n",
    "So, we'll mainly use the number of deceased from the dataset to find parameters for the SIR model. What's important to note is that many countries implemented a *lockdown* that greatly reduces the basic reproduction number R_0; thus, we first tweak the model to allow for a second R_0_2 to come into effect on day L (for lockdown)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SIR_model_with_lockdown(N, D, R_0, CaseFatalityRate, max_days, L, R_0_2):\n",
    "    '''\n",
    "    N: total population\n",
    "    D, R_0, CaseFatalityRate, ...: see texts above\n",
    "    '''\n",
    "    # BEFORE LOCKDOWN (same code as first model)\n",
    "    I0, R0 = 1, 0  # Initial number of infected and recovered individuals (1 infected, 0 recovered) [this R0 has nothing to do with the basic reproduction number R0]\n",
    "    S0 = N - I0 - R0 # Initial number of susceptible (everyone else)\n",
    "\n",
    "    gamma = 1.0 / D  # see texts above\n",
    "    beta = R_0 * gamma  # see texts above\n",
    "    alpha = CaseFatalityRate\n",
    "\n",
    "    t = np.linspace(0, L, L)  # Grid of time points (in days)\n",
    "    \n",
    "    # Initial conditions vector\n",
    "    y0 = S0, I0, R0\n",
    "    # Integrate the SIR equations over the time grid, t.\n",
    "    ret = odeint(deriv, y0, t, args=(N, beta, gamma))\n",
    "    S, I, R = ret.T\n",
    "    \n",
    "    \n",
    "    # AFTER LOCKDOWN\n",
    "    I0_2, R0_2, S0_2 = I[-1], R[-1], S[-1]  # beginning of lockdown -> starting Infected/Susceptible/Recovered numbers are the numbers at the end of no-lockdown period\n",
    "\n",
    "    gamma = 1.0 / D  # constant\n",
    "    beta_2 = R_0_2 * gamma\n",
    "    alpha = CaseFatalityRate  # constant\n",
    "\n",
    "    t_2 = np.linspace(0, max_days - L + 1, max_days - L + 1)\n",
    "    \n",
    "    # Initial conditions vector\n",
    "    y0_2 = S0_2, I0_2, R0_2\n",
    "    # Integrate the SIR equations over the time grid, t.\n",
    "    ret_2 = odeint(deriv, y0_2, t_2, args=(N, beta_2, gamma))\n",
    "    S_2, I_2, R_2 = ret_2.T\n",
    "\n",
    "    \n",
    "    # COMBINING PERIODS\n",
    "    S_full = np.concatenate((S, S_2[1:]))\n",
    "    I_full = np.concatenate((I, I_2[1:]))\n",
    "    R_full = np.concatenate((R, R_2[1:]))\n",
    "    t_full = np.linspace(0, max_days, max_days)\n",
    "    \n",
    "    # Adding deaths (see text above)\n",
    "    X = np.zeros(max_days)\n",
    "    for day in range(12, max_days):\n",
    "        X[day] = sum(I_full[:day-12])\n",
    "    # X[13:] = I[:-13]\n",
    "    X = alpha * X * gamma\n",
    "\n",
    "    # Plot the data on three separate curves for S(t), I(t) and R(t)\n",
    "    f, ax = plt.subplots(1,1,figsize=(10,4))\n",
    "    ax.plot(t_full, S_full, 'b', alpha=0.7, linewidth=2, label='Susceptible')\n",
    "    ax.plot(t_full, I_full, 'y', alpha=0.7, linewidth=2, label='Infected')\n",
    "    ax.plot(t_full, X, 'r', alpha=0.7, linewidth=2, label='Dead')\n",
    "    ax.plot(t_full, R_full, 'g', alpha=0.7, linewidth=2, label='Recovered')\n",
    "\n",
    "    ax.set_xlabel('Time (days)')\n",
    "    ax.title.set_text('SIR-Model with Lockdown. Total Population: ' + str(N) + \n",
    "                      \", Days Infectious: \" + str(D) + \", R_0: \" + str(R_0) + \n",
    "                      \", CFR: \" + str(CaseFatalityRate*100) + \" R_0_2: \" + str(R_0_2) + \n",
    "                      \", L: \" + str(L) + \" days\")\n",
    "    # ax.set_ylabel('Number (1000s)')\n",
    "    # ax.set_ylim(0,1.2)\n",
    "    plt.text(L,N/20,'Lockdown')\n",
    "    plt.plot(L, 0, marker='o', markersize=6, color=\"red\")\n",
    "    ax.yaxis.set_tick_params(length=0)\n",
    "    ax.xaxis.set_tick_params(length=0)\n",
    "    ax.grid(b=True, which='major', c='w', lw=2, ls='-')\n",
    "    legend = ax.legend()\n",
    "    legend.get_frame().set_alpha(0.5)\n",
    "    for spine in ('top', 'right', 'bottom', 'left'):\n",
    "        ax.spines[spine].set_visible(False)\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (fictional) Case Study: No Lockdown vs Lockdown\n",
    "We model a highly infectious and deadly virus with an R_0 of 3.0 and 4 days infectious spreading in a population of 1 million. The CFR is set to 20%.\n",
    "We look at the development without a lockdown and with a lockdown after 10 and 30 days that reduces R_0 to 0.9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No Lockdown:\n",
    "\n",
    "About 180k Fatalities at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el131398182330303047630738295\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el131398182330303047630738295\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.0, 63.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-3.0, 63.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818233031928\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818233115480\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818233115816\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el13139818233116600\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el13139818233117384\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818207475136\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818207476200\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el13139818207477264\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el13139818207515256\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818232988616\", \"dasharray\": \"none\", \"alpha\": 0.5, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Time (days)\", \"position\": [0.5, -0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818233307264\"}, {\"text\": \"SIR-Model. Total Population: 1000000, Days Infectious: 4, R_0: 3.0, CFR: 20.0\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818232990688\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.5896799116997793], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818207474632\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.5206953642384106], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818207475752\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 0.45171081677704195], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818207476816\"}, {\"text\": \"Recovered\", \"position\": [0.8803763440860216, 0.38272626931567333], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818207514808\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 999999.0, 1.0, 0.0, 0.0], [1.0169491525423728, 999998.005872736, 1.6627510131126346, 0.0, 0.33137625069391313], [2.0338983050847457, 999996.3528895084, 2.7647382772291076, 0.0, 0.8823722139815948], [3.0508474576271185, 999993.6044010698, 4.597059302521973, 0.0, 1.7985396272375958], [4.067796610169491, 999989.0343841417, 7.643724030927844, 0.0, 3.321891826892342], [5.084745762711864, 999981.4356881012, 12.709483992370757, 0.0, 5.8548279057374195], [6.101694915254237, 999968.8012490654, 21.13233855874362, 0.0, 10.06641237532188], [7.11864406779661, 999947.7941746667, 35.13676279708726, 0.0, 17.069062535616148], [8.135593220338983, 999912.8670303448, 58.42071450126895, 0.0, 28.712255153362747], [9.152542372881356, 999854.7985500441, 97.1307858794626, 0.0, 48.07066407576624], [10.169491525423728, 999758.2636419958, 161.48116450237063, 0.0, 80.25519350107717], [11.1864406779661, 999597.8014577114, 268.43872713064826, 0.0, 133.75981515733653], [12.203389830508474, 999331.1348875698, 446.16881161785176, 0.0, 222.6963008115144], [13.220338983050848, 998888.1276207847, 741.3753896743636, 0.05, 370.4969895402791], [14.23728813559322, 998152.6016668299, 1231.362706174451, 0.13313755065563174, 616.0356269949033], [15.254237288135592, 996932.5963965816, 2043.6976914142008, 0.27137446451708713, 1023.7059120036243], [16.271186440677965, 994912.2612238373, 3387.830309711233, 0.5012274296431858, 1699.908466451022], [17.28813559322034, 991575.511103175, 5604.763729103717, 0.8834136311895779, 2819.725167720807], [18.305084745762713, 986088.8455548785, 9241.880626089822, 1.518887830808116, 4669.27381903122], [19.322033898305083, 977132.0799330941, 15157.104434306351, 2.575504758745297, 7710.815632599055], [20.338983050847457, 962681.5253993236, 24641.263632079317, 4.33234289859966, 12677.210968596628], [21.35593220338983, 939803.1222731811, 39502.2543725778, 7.2533786236631075, 20694.62335424067], [22.3728813559322, 904637.0896694366, 61956.10325259748, 12.109917917636238, 33406.80707796548], [23.389830508474574, 852951.2926696612, 94031.42954147013, 20.18397614275477, 53017.27778886819], [24.406779661016948, 781724.1908906432, 136191.7098130425, 33.605912499287186, 82084.09929631391], [25.423728813559322, 691669.4723004968, 185448.49941413576, 55.91435308017978, 122882.02828536696], [26.440677966101696, 589128.5811475172, 234501.47837492664, 92.98312256389795, 176369.94047755562], [27.457627118644066, 484871.94634376944, 273838.23960299545, 154.5512578726205, 241289.81405323453], [28.47457627118644, 389668.39072466275, 296178.8859134464, 256.73614244333055, 314152.72336189036], [29.491525423728813, 310112.97589978814, 299614.48594332504, 426.12765792889223, 390272.5381568863], [30.508474576271183, 247725.9135942493, 287130.30596466776, 706.3658443840782, 465143.78044108243], [31.525423728813557, 200668.55408735998, 263964.87216608535, 1168.4598756885691, 535366.5737465543], [32.54237288135593, 165846.50065051057, 235256.29505225143, 1926.3150974038865, 598897.2042972377], [33.559322033898304, 140216.64819325125, 204928.16424603635, 3158.3782790078526, 654855.1875607121], [34.57627118644068, 121293.7480447025, 175526.58392075333, 5133.490997636743, 703179.6680345439], [35.59322033898305, 107214.50820531473, 148477.92435606834, 8231.296160266616, 744307.5674386164], [36.610169491525426, 96636.48501556941, 124430.87469739157, 12932.867637340125, 778932.6402870385], [37.62711864406779, 88607.4068585496, 103546.31554453264, 19742.45312799225, 807846.2775969171], [38.644067796610166, 82453.08090942065, 85705.29520513373, 29014.87809869903, 831841.623885445], [39.66101694915254, 77693.45475703289, 70645.45493459456, 40739.952017445365, 851661.0903083719], [40.67796610169491, 73983.32926111206, 58045.170751672915, 54431.86399759513, 867971.4999872145], [41.69491525423729, 71071.5231617481, 47572.627297370724, 69240.80829326744, 881355.8495408805], [42.71186440677966, 68772.97314273247, 38912.543414497006, 84221.53259043371, 892314.4834427697], [43.728813559322035, 66949.64475464827, 31779.1823022603, 98578.04788866709, 901271.1729430907], [44.7457627118644, 65497.38068434646, 25921.242049116343, 111776.29149697137, 908581.3772665365], [45.762711864406775, 64336.747343230396, 21122.14694668358, 123539.10624958394, 914541.1057100854], [46.77966101694915, 63406.587253255624, 17197.90369102308, 133785.51446188576, 919395.5090557206], [47.79661016949152, 62659.418229278584, 13993.821128068867, 142561.8436579234, 923346.7606426518], [48.813559322033896, 62058.10678190813, 11380.849854441029, 149985.73987572684, 926561.0433636501], [49.83050847457627, 61573.43128447086, 9251.963720917296, 156207.2836105964, 929174.604994611], [50.847457627118644, 61182.27426223962, 7518.802184238834, 161384.59938782302, 931298.9235535208], [51.86440677966102, 60866.26568313388, 6108.67087245965, 165669.8641480797, 933025.0634444056], [52.88135593220339, 60610.75349208784, 4961.92846473701, 169202.13689480943, 934427.3180431742], [53.89830508474576, 60404.01483010403, 4029.7490490451814, 172104.39543239307, 935566.2361208498], [54.91525423728813, 60236.646299651795, 3272.229655811175, 174483.0267972616, 936491.1240445361], [55.932203389830505, 60101.089034097786, 2656.804308393218, 176428.65396798647, 937242.1066575083], [56.94915254237288, 59991.256258631154, 2156.924125613235, 178017.61308309948, 937851.8196157549], [57.96610169491525, 59902.23961971343, 1750.9645747429945, 179313.67518555527, 938346.7958055428], [58.983050847457626, 59830.076372759424, 1421.3245310754914, 180369.78253288945, 938748.5990961643], [60.0, 59771.56410510148, 1153.685892921196, 181229.67771744062, 939074.7500019766]], \"data02\": [[0.8301971326164875, 0.6057763061074319, 0.5367917586460632, 0.46780721118469465, 0.39882266372332603], [0.8660394265232976, 0.6057763061074319, 0.5367917586460632, 0.46780721118469465, 0.39882266372332603]], \"data03\": [[0.8266129032258065, 0.35513245033112584], [0.9874551971326165, 0.35513245033112584], [0.9910394265232975, 0.35513245033112584], [0.9910394265232975, 0.36433038999264167], [0.9910394265232975, 0.6356696100073583], [0.9910394265232975, 0.6448675496688742], [0.9874551971326165, 0.6448675496688742], [0.8266129032258065, 0.6448675496688742], [0.8230286738351255, 0.6448675496688742], [0.8230286738351255, 0.6356696100073583], [0.8230286738351255, 0.36433038999264167], [0.8230286738351255, 0.35513245033112584], [0.8266129032258065, 0.35513245033112584]]}, \"id\": \"el13139818233030304\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el131398182330303047630738295\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.0, 63.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-3.0, 63.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818233031928\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818233115480\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818233115816\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el13139818233116600\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el13139818233117384\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818207475136\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818207476200\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el13139818207477264\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el13139818207515256\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818232988616\", \"dasharray\": \"none\", \"alpha\": 0.5, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Time (days)\", \"position\": [0.5, -0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818233307264\"}, {\"text\": \"SIR-Model. Total Population: 1000000, Days Infectious: 4, R_0: 3.0, CFR: 20.0\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818232990688\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.5896799116997793], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818207474632\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.5206953642384106], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818207475752\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 0.45171081677704195], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818207476816\"}, {\"text\": \"Recovered\", \"position\": [0.8803763440860216, 0.38272626931567333], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818207514808\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 999999.0, 1.0, 0.0, 0.0], [1.0169491525423728, 999998.005872736, 1.6627510131126346, 0.0, 0.33137625069391313], [2.0338983050847457, 999996.3528895084, 2.7647382772291076, 0.0, 0.8823722139815948], [3.0508474576271185, 999993.6044010698, 4.597059302521973, 0.0, 1.7985396272375958], [4.067796610169491, 999989.0343841417, 7.643724030927844, 0.0, 3.321891826892342], [5.084745762711864, 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0.36433038999264167], [0.8230286738351255, 0.35513245033112584], [0.8266129032258065, 0.35513245033112584]]}, \"id\": \"el13139818233030304\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el131398182330303047630738295\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.0, 63.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-3.0, 63.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818233031928\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818233115480\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818233115816\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el13139818233116600\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el13139818233117384\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818207475136\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818207476200\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el13139818207477264\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el13139818207515256\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818232988616\", \"dasharray\": \"none\", \"alpha\": 0.5, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Time (days)\", \"position\": [0.5, -0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818233307264\"}, {\"text\": \"SIR-Model. Total Population: 1000000, Days Infectious: 4, R_0: 3.0, CFR: 20.0\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818232990688\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.5896799116997793], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818207474632\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.5206953642384106], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818207475752\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 0.45171081677704195], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818207476816\"}, {\"text\": \"Recovered\", \"position\": [0.8803763440860216, 0.38272626931567333], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818207514808\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 999999.0, 1.0, 0.0, 0.0], [1.0169491525423728, 999998.005872736, 1.6627510131126346, 0.0, 0.33137625069391313], [2.0338983050847457, 999996.3528895084, 2.7647382772291076, 0.0, 0.8823722139815948], [3.0508474576271185, 999993.6044010698, 4.597059302521973, 0.0, 1.7985396272375958], [4.067796610169491, 999989.0343841417, 7.643724030927844, 0.0, 3.321891826892342], [5.084745762711864, 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0.36433038999264167], [0.8230286738351255, 0.35513245033112584], [0.8266129032258065, 0.35513245033112584]]}, \"id\": \"el13139818233030304\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SIR_model(N=1_000_000, D=4, R_0=3.0, CaseFatalityRate=0.2, max_days=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lockdown after 30 Days:\n",
    "\n",
    "Fatalities around 150k. The lockdown is too late to have a significant impact. However, at the time of the lockdown, there aren't even any fatalities yet; as the R_0 is so high, the virus spreads incredibly fast and a lockdown would have to come into effect "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "\n",
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       "\n",
       "<div id=\"fig_el131398182075866244705082308\"></div>\n",
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       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
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       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el131398182075866244705082308\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.0, 63.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-3.0, 63.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818207229600\", \"lines\": [{\"data\": \"data01\", \"xindex\": 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\"coordinates\": \"data\", \"id\": \"el13139818207428504\", \"color\": \"#FF0000\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818206951744\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818206952808\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el13139818206990800\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 4, 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-0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818513963048\"}, {\"text\": \"SIR-Model with Lockdown. Total Population: 1000000, Days Infectious: 4, R_0: 3.0, CFR: 20.0 R_0_2: 0.9, L: 30 days\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818207332896\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.5896799116997793], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206951240\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.5206953642384106], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206952360\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el131398182075866244705082308\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.0, 63.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-3.0, 63.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818207229600\", \"lines\": [{\"data\": \"data01\", 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-0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818513963048\"}, {\"text\": \"SIR-Model with Lockdown. Total Population: 1000000, Days Infectious: 4, R_0: 3.0, CFR: 20.0 R_0_2: 0.9, L: 30 days\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818207332896\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.5896799116997793], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206951240\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.5206953642384106], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206952360\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 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[0.8230286738351255, 0.6356696100073583], [0.8230286738351255, 0.36433038999264167], [0.8230286738351255, 0.35513245033112584], [0.8266129032258065, 0.35513245033112584]]}, \"id\": \"el13139818207586624\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el131398182075866244705082308\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.0, 63.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-3.0, 63.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818207229600\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818207424864\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818207425200\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el13139818207425984\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el13139818207426768\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818207428504\", \"color\": \"#FF0000\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818206951744\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818206952808\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el13139818206990800\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el13139818206991864\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data04\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818207330824\", \"dasharray\": \"none\", \"alpha\": 0.5, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [{\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818207428504pts\", \"facecolor\": \"#FF0000\", \"edgecolor\": \"#FF0000\", \"edgewidth\": 1.0, \"alpha\": 1, \"zorder\": 2, \"markerpath\": [[[0.0, 3.0], [0.7956093000000001, 3.0], [1.5587396123545605, 2.683901074764725], [2.121320343559643, 2.121320343559643], [2.683901074764725, 1.5587396123545605], [3.0, 0.7956093000000001], [3.0, 0.0], [3.0, -0.7956093000000001], [2.683901074764725, -1.5587396123545605], [2.121320343559643, -2.121320343559643], [1.5587396123545605, -2.683901074764725], [0.7956093000000001, -3.0], [0.0, -3.0], [-0.7956093000000001, -3.0], [-1.5587396123545605, -2.683901074764725], [-2.121320343559643, -2.121320343559643], [-2.683901074764725, -1.5587396123545605], [-3.0, -0.7956093000000001], [-3.0, 0.0], [-3.0, 0.7956093000000001], [-2.683901074764725, 1.5587396123545605], [-2.121320343559643, 2.121320343559643], [-1.5587396123545605, 2.683901074764725], [-0.7956093000000001, 3.0], [0.0, 3.0]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]}], \"texts\": [{\"text\": \"Lockdown\", \"position\": [30.0, 50000.0], \"coordinates\": \"data\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818207427552\"}, {\"text\": \"Time (days)\", \"position\": [0.5, -0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818513963048\"}, {\"text\": \"SIR-Model with Lockdown. Total Population: 1000000, Days Infectious: 4, R_0: 3.0, CFR: 20.0 R_0_2: 0.9, L: 30 days\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818207332896\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.5896799116997793], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206951240\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.5206953642384106], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206952360\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 0.45171081677704195], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206953424\"}, {\"text\": \"Recovered\", \"position\": [0.8803763440860216, 0.38272626931567333], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206991416\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 999999.0, 1.0, 0.0, 0.0], [1.0169491525423728, 999997.9839111381, 1.6773920638345392, 0.0, 0.3386967980726029], [2.0338983050847457, 999996.2795348238, 2.8136413104367235, 0.0, 0.9068238656975072], [3.0508474576271185, 999993.4206395466, 4.719566587467252, 0.0, 1.859793865789187], [4.067796610169491, 999988.6251898883, 7.916518676559665, 0.0, 3.458291435129187], 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[0.8230286738351255, 0.6356696100073583], [0.8230286738351255, 0.36433038999264167], [0.8230286738351255, 0.35513245033112584], [0.8266129032258065, 0.35513245033112584]]}, \"id\": \"el13139818207586624\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SIR_model_with_lockdown(N=1_000_000, D=4, R_0=3.0, CaseFatalityRate=0.2, max_days=60, L=30, R_0_2=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lockdown after 25 days:\n",
    "\n",
    "The lockdown is able to break the chain of infection early on! Fatalities are around 60k."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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unHsnzHt/GF/rohrdx1jkaeByfK3trhrTNuJPqmgH4tu8bQK6WvWnag+MGl5HVfASSR2cc/fWtw/49rWD8T9lvuOc+yKs+9t8M8iLqO9b5p6oeRwOxN+0tuD386A6lv0z8H/Aa1HHKnLT61tjnbVtLzJ9QxiuGQS/Q+1BcHOoVj4z64y/cGwg9vtwI/4Gf4TzP7dO5ps/K+1xOfDHqIgQDAU1j3GkmUwh1b/Bx6zFDv6MfwDtoFD+O6kqf33n3Uaqv7eRcmyIMe/etpTqv3yMBL52/teMpfiav5Qa05e28rKNFpq4ZOJ/EQFf8/sM0DfU9j1J9fMt0iTiZGBL+JUA5z3gnBuNDwhHAtc0tVx1iDw3EH1tTHXOLaDu60lDzrXaPg/VzvfwBaAyoAqVLufgm1A8BLxgZkkN2JdJwM/M92SwGf8F/6XGNuFxzpXhfyFKourn19p8CXSKqs2HPTuHEqj9mFc7l80sDn8fbsi23sDXHI5oYrkqOf9L6jP4NuYNZmb34iufTgmfv4jG7FeTj3eojb8U+LWZ1VdxULNMnfHnc73bqWM/69OobTrf88/hzrkM/Hk6GP9cSLMys4PwlYE3O+eerWf2us7nTUTdE8P+do6aXte9DnwQfGaoke6Lb7YJgHPuKefc0fjmhylE/WoRS329Qwwx361KnzDeF/8z7kd1LRfld8C3QvuPOoUq6wlUf3o74jX8Des8M0sw3x/locCrzrcPmo9vZ5kUbkSnRy37V3yziSlmFm++K6mJ1oAuT0IwvgD/8FUkqPsA/2GqLcjbAmSGN3VPJIWyRlI88Hfgp2Y2INQM3QM8Ey7cM4GTzOzscIwyYxz3K/EPwbxqZqnhp9cXgNvNLM18dznTo+av9bhHHYvBwDj8AzJL8R/cI/HfTJvb34H/MrPDQjDzG/zDBZvDTyx5+A9CREd8zVhuuBD+Yi+W4/rwa0dH/IfubzVqXW8zs1TzbZYuoOop5UXAaWbWxXz79avq2E5HIM85V2Bmw4D/ikyoZX+jvQvEmdm14b38Fv5LwF7p4SJ89lLwF6rEcM5GLlozgEvN7BDz7clvxgeEhNr0FcCtZpZsZmfjf75/qTWXNbOTzay4gfve2Xx3Un/FP4izIux7OrDdOVdsZkcDZ9VYdE6Y526imoCZ2XgzGxtqIgvxD8g0R48OjwK/MN8MDTPramaRJj4v4gOny8J728mqugTcAgyIen9rquvzsAzf9G1SCIDvIOo+ZL7bsMxwbcrDB9wV39zENxyLf/BzVEjb8W0D/xzWe6+Z/V9DDkoo573ATRbVpV6M+Xbgr4V3huvnRPwXmpkN2U7Y18i9dQD+Rl9be+Z/A6lm9hPz7Vt/ij833gvL/8TMvqylnJ/jH0581nw3V0nhWnS+mTWl+cj/A6aZrxGsl5ndAUzD9zCR25j9qrEfe3S8w2d+LvU3mfkHcISZnR6uaXcAHzjn1tS1UD37WZ9GbdPMRofreBd8beuyUPEY3eVfXRUqkWt0JNX7q7SZ9cM/KPsb59wTMab/l/ku7MzMhuObJ9Z2Pj8LfNfMjgzv+11U/5zXeq8DcM6txveg8QQ+BtodynCo+e7jkvFfvIuo79rp6n76r3co7Ab8ibkB/xNdpIeEC6mjd4iQ9wdqeZKU0DtELdMuoXrvEMfiA9K88Br9tOpA/E2+gNi9QxyJD1pz8LWf/wscGKbNIUbvEFHL/jocyOQwfiX+wtwjap4nieq5AH/B2Y5/Cj/SO0R0efqHdSTUsk0XI12Cv1n8El9Lk42/6XaNWu44/BOVO8M802uWL6xjBr4pQAq+xuRVau8dotbjHqZ/CLwdNf4P/Acyep5q50X08cDXEhVE3o863odqxzgq/2pgdXhvX6R6bw9X42/YufgHEw/EX1wL8LUKlxP1VDiN6B2ixrR4/Id4Pb4d/JNUfUaie4fYjP8GfG3Ush3wX0R24h9Mup7ae4eYhH9YtQB/3t6Dbwde2/7W7B1iZNj/PHzznm9HTauvl4q3gOvqeH8+4pvn7Pio6TeGY5OH71YnMWrawfjPbxH+wjahxrpbfFn8RffNOvZ3M/4LVUE43u/jvxzHRc3zffznMB9/bj5a8xwK50050KfGsf88rDs7nE9pdRz3ar1DxChn5Pz5xjmMf0J7KVU9WDwaNW0U/rqZiz9vfxryD8B/7nfgb9Q1y1Hr5yFMvzSUawv+nI0u47P4hwvz8efoqVHLrQK+V9d1ItZ+h/GZ+OdGYs0bq3eIOPzDX/9Vz3Yi189d+OvcWVHTDgnvYfdalr0fX2teGI7VH4AutX3m8M+hLArn6yfAYVHT7gb+Ukc54/DB37Ko7f0NGBLr81/XsQx5TwAzo86hBbUsm4y/FpRQ1QypoBH7dQfwz4Yc7xjbjnW+T8Cf6xn1vK+n4L8k78LXpPepZ/6G7Ged529d28R/hn4XNR65Z+SFczszatq38PeJ+DquGTWv1Y/VV0Z8LORq7N+2qOl/w1+vCvH35F8DSXXs739RFcvU7B2izntdmOeSUJ6jovLG4CtF86mKCWJ+/iLJwoIi0gzMbAjwuXOuse2/pRWZ/4fHP7tQu9KM2/kxcLZz7qTm3E57Z2afA8c43/Zwv2P+j6Uudv4hImnHzP877Arn3FP1zrwPM7PJwP845/boj8UUBIs0IwXBUhvzbfPfw/fmUF/7OhERwTe/w9eGz3XO/XZP1tWoB+NERGTPmdlUfFOBlfgmRNIGhfbkBbWkWvv/lbbNzO6o5T2t2TNV9DLJdZwLR9S2nOxd5p912oFvN/zIHq9PNcEiIiIi0t6oJlhERERE2h0FwSIiIiLS7uhhnYZTuxERERHZV+yNP4Par6kmWERERETaHQXBIiIiItLuKAgWERERkXZHQbCIiIiItDt6ME5ERET2ivLycnJycigtLW3torQbiYmJZGRkEB8f39pF2ee0+J9lmNnjwGnAVufcYSEvA3gG6A+sAc52zu0wMwMeBE4FdgEXOuc+DctMB34RVntX5H+yzWwM8CSQCrwGXOOcc03ZRg3qHUJERKQO2dnZpKSkkJ6ejr+9SnNyzlFQUEBxcTHdunWrOVlvQD1aoznEk8DJNfJuBN50zg0C3gzjAKcAg0L6MfAHqAyabwOOBMYBt5lZ17DMH8K8keVObso2REREpHFKS0sVALcgMyM9PV01703U4s0hnHNzzax/jexpwMQw/BQwB7gh5M9wvrr6IzPrYmY9w7z/ds7lAJjZv4GTzWwO0Mk592HInwF8B3i9sdtwzm3am/tdn5UrYdeultxi82vINbAx89R8rW16bctEUs3xSIoLXwnj4qrGo6fVTPHx1cd1zRcRQQFwC9Pxbrq20ia4RyTodM5tMrPuIb83sC5qvvUhr6789THym7KNFg2C//QnWLasJbcoe1tcHCQk+OA4IaFqODGxKi8pyafkZJ+ix5OSICUF0tKgQ4eq1+jhtDS/HhERqd3dd9/N3/72N+Lj44mLi+OPf/wjRx55ZIuXY9GiRWzcuJFTTz0VgJdffpkvvviCG2+8kQsvvJDTTjuNM888s9oyc+bM4f777+fVV19t8fK2N239dhrr641rQn5TttGiDj7YB0t7qoWbeNeqKeWItUwkr+a0uvJry4tOteVXVPhpFRVV45HX6FReXn04Ms/u3Y3f78bq0AEyMqqnrl0hM7PqtXv3qpptEZH25MMPP+TVV1/l008/JTk5mW3btrG7JS7OMSxatIj58+dXBsFTp05l6tSprVIW+aa2EgRviTRBCM0dtob89UDfqPn6ABtD/sQa+XNCfp8Y8zdlGy3qxz9u6S3K3uScD4bLy6GszKeaw6WlPpWU+LR7t0/Rw8XFvllMYWFViozv2gUFBVX569bVXp6kJOjTB/r2hQMP9KlvXzjgAF8rLSKyv9q0aRNZWVkkJycDkJWVBUD//v2ZP38+WVlZzJ8/n+uvv545c+bwzjvvcM011wC+acHcuXPp2LEjv/3tb3n66aeJi4vjlFNO4d5772XVqlVcccUVZGdnk5aWxp///GeGDBnChRdeSEpKCkuXLmXLli088MADTJ48mV/+8pcUFRXx3nvvcdNNN1FUVMT8+fP5/e9/D8Abb7zBgw8+WLnMaaedVm1fCgsLueqqq/jss88oKyvj9ttvZ9q0aS14NPdvbSUIfhmYDtwbXl+Kyr/SzGbhH4LLC0Hsv4B7oh6Gmwzc5JzLMbN8MxsPzAN+CDzclG00477KfsisqglEuO42C+cgPx9ycnzasQO2b/evkbytW/3r6tU+RUtI8MHxwQfD6NEwciR07tx85RWR9uv005tnva+8Uvf0yZMnc+edd3LIIYdw0kkncc455zBhwoRa57///vt55JFHOOaYYygoKCAlJYXXX3+dF198kXnz5pGWlkZOTg4AP/7xj3n00UcZNGgQ8+bN4/LLL+ett94CYM2aNbzzzjusWrWKE044gZUrV3LnnXdWC3qffPLJatuOtUy0u+++mxNPPJHHH3+c3Nxcxo0bx0knnUSHDh0aedQklhYPgs3s7/ha3CwzW4/v5eFe4FkzuxhYC5wVZn8N33XZSnz3ZRcBhGD3V8AnYb47Iw/JAZdR1UXa6yHR2G2ItEVm0KmTT/371z5fYSGsXw9r1/q0bp1/zc6GNWt8euMNP+/AgT4gHj0ahg71tcgiIvuq9PR0FixYwLvvvsvbb7/NOeecw7333lvr/McccwzXXXcd559/Pt/97nfp06cPb7zxBhdddBFpaWkAZGRkUFBQwAcffMBZZ51VuWxJSUnl8Nlnn01cXByDBg1i4MCBfPnll/WWtb5lZs+ezcsvv8z9998PQHFxMWvXrmXo0KGNOiYSW2v0DvH9WiZNijGvA66oZT2PA4/HyJ8PHBYjf3tjtyGyr+rQAQYP9ilaUZEPhr/4AhYuhKVLq2qMn3/eB8CHHeYD4uOP9+2NRUSaor4a2+YUHx/PxIkTmThxIsOHD+epp54iISGBivDgR3FxceW8N954I9/+9rd57bXXGD9+PG+88QbOuW/0ulBRUUGXLl1YtGhRzG3WnL8hvTbUt4xzjueff57BNS/mslfo0RmRdiQ11QfGZ5wBd94Jf/87/OpX8N3vwoABvl3yp5/CX/4CF18MDzzgu+8TEdlX/Oc//2HFihWV44sWLaJfv37079+fBQsWAPD8889XTl+1ahXDhw/nhhtuYOzYsXz55ZdMnjyZxx9/nF2h79KcnBw6derEgAEDeO655wAfoC5evLhyPc899xwVFRWsWrWK1atXM3jwYDp27Eh+fn6tZY21TLQpU6bw8MMPE/ljs4ULF+7h0ZFobaVNsIi0gqQkGDXKp4sugtxcWLQIPvzQp7ff9mnYMJg2DY48Ur1OiEjbVlBQwFVXXUVubi4JCQkcfPDB/OlPf2LZsmVcfPHF3HPPPdW6S/vd737H22+/TXx8PIceeiinnHIKycnJLFq0iLFjx5KUlMSpp57KPffcw8yZM7nsssu46667KC0t5dxzz2XkyJEADB48mAkTJrBlyxYeffRRUlJSOOGEE7j33nsZNWoUN9100zfKGmuZaLfeeivXXnstI0aMwDlH//791XXaXtTif5u8D9OBknZl61b/c+bs2VV/5NKjh3/Y5Vvf8n0Wi4hE27hxI7169WrtYrS42vr8bSm1HHf9i0Y9VKcjIjF17+6bRDz5pO/C74ADYMsWeOwxuPBCeOIJ36WbiIjIvkg1wQ2nAyXtWkUFfPwxvPQSfP65z+vZE667DoYMad2yiUjb0F5rglubaoKbRkFww+lAiQQrVsBDD/mu1szg7LPh3HP1l84i7Z2C4NahILhp1BxCRBpt0CDfc8T3vufHn3kGfvYz3zexiIjIvkBBsIg0SWKibxt8zz2+/fDKlXDNNfDqq/6f7URERNoyBcEiskcOOwwefhgmTfL9DP/xj3Dbbf7vnEVERNoqBcEissfS0uDaa+Hmm/1fOi9cCFde6R+kExFpSenp6fXO8+677zJs2DBGjRpFUVFRo9b/4osv8sUXXzRLuaRlKQgWkb3mqKPg97+HI46AggLfVOKTT1q7VCIi1c2cOZPrr7+eRYsWkZqa2qhlmxoES9ujIFhE9qquXeHWW/3ZMda5AAAgAElEQVRDc+Xl8Otfw5IlrV0qEWlv5syZw8SJEznzzDMZMmQI559/Ps45HnvsMZ599lnuvPNOzj//fADuu+8+jjjiCEaMGMFtt91WuY4ZM2YwYsQIRo4cyQUXXMAHH3zAyy+/zM9+9jNGjRrFqlWrWLVqFSeffDJjxozhuOOO48svvwTgq6++4qijjuKII47g1ltvbZVjIHVTh0YisteZwfTpUFQEr70Gv/oV3HUXDB7c2iUTkZby2WenN8t6hw9/pcHzLly4kKVLl9KrVy+OOeYY3n//fS655BLee++9yn94mz17NitWrODjjz/GOcfUqVOZO3cumZmZ3H333bz//vtkZWWRk5NDRkYGU6dOrfbvcJMmTeLRRx9l0KBBzJs3j8svv5y33nqLa665hssuu4wf/vCHPPLII81yLGTPKAgWkWZhBj/5iQ+E337bPyx3zz0wcGBrl0xE2otx48bRp08fAEaNGsWaNWs49thjq80ze/ZsZs+ezejRowEoKChgxYoVLF68mDPPPJOsrCwAMjIyvrH+goICPvjgA84666zKvJKSEgDef/99nn/+eQAuuOACbrjhhr2/g7JHFASLSLMx892mlZTABx/AL38Jv/kN9O7d2iUTkebWmBrb5pKcnFw5HB8fT1lZ2Tfmcc5x0003cemll1bLf+ihhzCr+/8mKioq6NKlC4sWLYo5vb7lpXWpTbCINKv4eLj+ejj8cMjLg1/8ArZube1SiYh4U6ZM4fHHH6egoACADRs2sHXrViZNmsSzzz7L9tDfY05ODgAdO3YkPz8fgE6dOjFgwACee+45wAfUixcvBuCYY45h1qxZgH8QT9oeBcEi0uwSE333acOGwbZtcMstEO4nIiKtavLkyZx33nkcddRRDB8+nDPPPJP8/HyGDRvGLbfcwoQJExg5ciTXXXcdAOeeey733Xcfo0ePZtWqVcycOZO//OUvjBw5kmHDhvHSSy8B8OCDD/LII49wxBFHkJeX15q7KLUwp792aigdKJE9tGuXrwlesQL69oV77/X9CovI/mHjxo306tWrtYvR7tRy3NUWox6qCRaRFpOWBnfcAQceCOvW+TbCK1bob5ZFRKTlqSa44XSgRPaSnBy48UbYtMmP9+kDJ5wAEydC9+6tWjQR2QOqCW4dqgluGgXBDacDJbIX5eTAP/4Bc+f6B+Yihg3zwfCxx4L+ZVRk36IguHUoCG4aBcENpwMl0gzKymDxYnjrLfjoI9i92+cnJMC4cTBhAowZA1E9HYlIG6UguHUoCG4aBcENpwMl0syKiuDDD/2fayxeXNVWOCXFB8THHANjx0JSUuuWU0RiUxDcOhQEN42C4IbTgRJpQdu3+6YS777rH56LiATExx7ra4gVEIu0HQqCW4eC4KZRENxwOlAirWTLFnj/fXjvvdgB8dFH+z/jSE1tvTKKSNsIguPj4xk+fDilpaUkJCQwffp0rr32WuLi9rxDrNtvv5309HSuv/76vVDSvUdBcNPob5NFpM3r0QO++12fNm+uCohXrvS1xXPn+jbEI0fC+PE+MM7IaO1Si0hrSE1Nrfwb461bt3LeeeeRl5fHHXfc0colk7ZGNcENpwMl0sZEAuJ58+DLL6v3N3zIIT4gHj/ed8FmqhMRaXZtoSY4PT298i+QAVavXs0RRxzBtm3bqKio4MYbb2TOnDmUlJRwxRVXcOmll1JQUMC0adPYsWMHpaWl3HXXXUybNg2Au+++mxkzZtC3b1+6devGmDFjVBO8n1BNsIjssw44AL73PZ9yc+GTT3wPE4sWwfLlPs2YAT17+gfqxoyB4cPVjlikRZx+evOs95VXGjX7wIEDqaioYOvWrbz00kt07tyZTz75hJKSEo455hgmT55M3759+ec//0mnTp3Ytm0b48ePZ+rUqXz66afMmjWLhQsXUlZWxuGHH86YMWOaZ7+kxSkIFpH9Qpcu8K1v+VRc7APhjz6Cjz/2f8rxyis+JSX5QHjMGJ/0DI/I/i/yq/fs2bNZsmQJ//jHPwDIy8tjxYoV9OnTh5tvvpm5c+cSFxfHhg0b2LJlC++++y5nnHEGaWlpAEydOrXV9kH2PgXBIrLfSUmpagpRXu5rhBcs8Gnlyqph8LXEY8b4B+sOO0wP14nsNY2ssW0uq1evJj4+nu7du+Oc4+GHH2bKlCnV5nnyySfJzs5mwYIFJCYm0r9/f4qLiwEwtaXab+35o5IiIm1YfDwMHQo/+AH893/D00/DtdfCccf5f6TbtAlefRXuvBO+/334+c9h5kz4/HP/Rx4isu/Kzs7mJz/5CVdeeSVmxpQpU/jDH/5AaWkpAMuXL6ewsJC8vDy6d+9OYmIib7/9Nl9//TUAxx9/PP/85z8pKioiPz+fV9pIYC97R5upCTaznwKX4B9A+wy4COgJzAIygE+BC5xzu80sGZgBjAG2A+c459aE9dwEXAyUA1c75/4V8k8GHgTigcecc/eG/AGxttES+ywiLa9LF5g0yadILfGnn1a1I162zKdZs/y/1A0bBqNG+Z4n+veHvdDLkog0o6KiIkaNGlXZRdoFF1zAddddB8All1zCmjVrOPzww3HO0a1bN1588UXOP/98Tj/9dMaOHcuoUaMYMmQIAIcffjjnnHMOo0aNol+/fhx33HGtuWuyl7WJ3iHMrDfwHnCoc67IzJ4FXgNOBV5wzs0ys0eBxc65P5jZ5cAI59xPzOxc4Azn3Dlmdijwd2Ac0At4AzgkbGY58C1gPfAJ8H3n3BdhW9/YRoxitv6BEpFmtWuXrwFevNinUBlUqUMHOPRQHxgfdhgcdJDvmk1EvLbQO0R7pN4hmqYtXb4TgFQzKwXSgE3AicB5YfpTwO3AH4BpYRjgH8DvzTfamQbMcs6VAF+Z2Up8QAyw0jm3GsDMZgHTzGxZHdsQkXYmLc33MTwuXDV27IAlS6qC4q1bfQ8Un3zipyclwZAhPiAeNgwGD/a1xyIi0va1iSDYObfBzO4H1gJFwGxgAZDrnIu0ylsP9A7DvYF1YdkyM8sDMkP+R1Grjl5mXY38I8MytW1DRNq5rl1hwgSfALZtg6VLfW3x0qWwbp0Pkpcs8dPj42HAAB8YR1L37uqjWESkLWoTQbCZdcXX4g4AcoHngFNizBppkhDrluLqyI/Viq+u+UVEviErq3pQnJfng+FIYPzVV773iZUr/cN24NsgDx5cFRQffLDvvUJERFpXmwiCgZOAr5xz2QBm9gJwNNDFzBJCTW0fYGOYfz3QF1hvZglAZyAnKj8ieplY+dvq2IaISJ06d4ajj/YJoKgIVqzw/14XSbm5/h/t5s3z85hB374waJAPiAcN8rXH+gMPEZGW1VaC4LXAeDNLwzeHmATMB94GzsT33jAdeCnM/3IY/zBMf8s558zsZeBvZvYA/sG4QcDH+BrfQaEniA3AucB5YZnatiEi0iipqTBihE/g/8Z58+bqQfHXX8PatT69+aafLy4O+vWrCowHDvTjqjEWEWk+baJ3CAAzuwM4BygDFuK7S+tNVfdlC4EfOOdKzCwFeBoYja8BPjfqobdbgB+F9VzrnHs95J8K/A7fRdrjzrm7Q/7AWNuIUcS2caBEZJ+2ezesWeObTCxf7l/XrvUBczQz/292Awb4oHjgQD/ctavaGEvbpd4hWod6h2iaNhME7wN0oESkWRQX+/bEy5fD6tU+rVvn+zGuqXNn31/xgQf62uJ+/fxw+FdXkVbVFoLg+Ph4hg8fTllZGQMGDODpp5+mS5curVqmppg4cSL3338/Y8eOrXdeBcFN01aaQ4iItFspKf5f7YYOrcorLfWB8FdfVQXGX33lH8aLdNkWLSurKiDu1w/69PGpQ4eW3ReR1paamsqiRYsAmD59Oo888gi33HJLK5fKKysrI0Gdi7cZeidERNqgxMSqZhCTJvk85yA727crjrQt/vprHyxv2+bTggXV19O1a1VAHJ26dVOzCtn/HXXUUSyJ9GEI3HfffTz77LOUlJRwxhlncMcddwAwY8YM7r//fsyMESNG8PTTT/P111/zox/9iOzsbLp168YTTzxB586dGTlyJKtXryYuLo5du3YxePBgVq9ezdq1a7niiivIzs4mLS2NP//5zwwZMoQLL7yQjIwMFi5cyOGHH86dd97JVVddxWeffUZZWRm3334706ZNo6ioiIsuuogvvviCoUOHUlRU1FqHrd1QECwiso8w8/0Od+8ORxxRlV9RAZs2VQXFa9fChg2wfr3/w48dO+Czz6qvKykJDjgAevb0bY8jqWdPX6usAFn21Ol/P71Z1vvK919p0Hzl5eW8+eabXHzxxQDMnj2bFStW8PHHH+OcY+rUqcydO5fMzEzuvvtu3n//fbKyssjJyQHgyiuv5Ic//CHTp0/n8ccf5+qrr+bFF19k5MiRvPPOO5xwwgm88sorTJkyhcTERH784x/z6KOPMmjQIObNm8fll1/OW2+9BcDy5ct54403iI+P5+abb+bEE0/k8ccfJzc3l3HjxnHSSSfxxz/+kbS0NJYsWcKSJUs4/PDDm+X4SRUFwSIi+7i4OOjd26ejjqrKj9Qcr19fPa1b57tui/RSUVNiog+QI6lHj6rXHj18LxgibVVRURGjRo1izZo1jBkzhm9961uAD4Jnz57N6NGjASgoKGDFihUsXryYM888k6ysLAAyMjIA+PDDD3nhhRcAuOCCC/j5z38OwDnnnMMzzzzDCSecwKxZs7j88sspKCjggw8+4KyzzqosR0lJ1TP2Z511FvHx8ZXlePnll7n//vsBKC4uZu3atcydO5err74agBEjRjAi0s2MNBsFwSIi+6nomuOalUq7dvna402bYOPGqrRpkw+Q163zKZZOnaoC4m7d/Pq7dasa7tBBNcnS8BrbvS3SJjgvL4/TTjuNRx55hKuvvhrnHDfddBOXXnpptfkfeughrAEnbGSeqVOnctNNN5GTk8OCBQs48cQTKSwspEuXLpVtkWvqENU43znH888/z+DBg2vdhrQMBcEiIu1QWhocdJBPNUUC5C1bfNq8uep161bYudOnFStirzslpXpgnJnpU1aWTxkZfvu630tz6ty5Mw899BDTpk3jsssuY8qUKdx6662cf/75pKens2HDBhITE5k0aRJnnHEGP/3pT8nMzCQnJ4eMjAyOPvpoZs2axQUXXMDMmTM59thjAUhPT2fcuHFcc801nHbaacTHx9OpUycGDBjAc889x1lnnYVzjiVLljBy5MhvlGvKlCk8/PDDPPzww5gZCxcuZPTo0Rx//PHMnDmTE044gc8//7xaW2ZpHgqCRUSkmroCZOd8G+NIgJyd7QPj6Nfi4tqbWkSkpPiAODPTB8Vdu/rXmsP6wxDZE6NHj2bkyJGVweyyZcs4KrQZSk9P569//SvDhg3jlltuYcKECcTHxzN69GiefPJJHnroIX70ox9x3333VT4YF3HOOedw1llnMWfOnMq8mTNnctlll3HXXXdRWlrKueeeGzMIvvXWW7n22msZMWIEzjn69+/Pq6++ymWXXcZFF13EiBEjGDVqFOPGjWv249PeqZ/ghtOBEhGph3NQUOCD4exs2L69queK7durxkti/SVRDCkpPiju2hW6dPEpejiSOnf286p2uXW1hX6C2yP1E9w0qgkWEZG9xgw6dvRp4MDY8zjnm1xEAuMdO6pec3KqXnNyfK1ypO1yfRITfTDcqZNPnTtXpU6dqsoVmd6xo19GRNonBcEiItKizPzDcx06+D/2qI1zUFjog+Lc3OqpZl5env9L6kitc0OlpFQFxx07Qnq6Tx07+vJFxqPzOnTwTUbCw/4iso9SECwiIm2SWVUA2rdv/fMXF/sH9vLyql6jh/Pzfdq5s2q4uNin7OzGly8lxZctEhhHB8hpab4ruchwhw5V46mpVcPJyWrCIdJaFASLiMh+ISWlqmeKhnAOioqqAuPCQt+eOT/fv0aGo/MLC33atasqgG5MzXNNZr7M0YFxamrVvkQPxxpPTvYpMhzJT0xsveDaOaeuvlqQnu1qOj0Y13A6UCIiAlQF0JGguKCgKjiuLxUVVb3u3t085TPz/woYCZKTk785HsmLTtF5yck+mE5KqnqNNRydtm/PJiUlhfT0dAXCLcA5R0FBAcXFxXTr1q3mZL0B9VAQ3HA6UCIisleVl/va5KKi6sFxSYl/jdQ2R+aJvJaU+BSZFhmO5DdXcF2flJRyjj46hwMOKCU+3gfjcXFVqa7xuoYj4zWH65te2zSoytvXJSYmkpGRUfmPdFH2g71rXgqCG04HSkRE9gkVFT4QjgTFkcA41vju3VXDpaXV8yKptLT6a828SNrXQgoz/4BjXSkurva8mq+R4ej8WMOHHw5jxjT/7jX7FvZxahMsIiKyn4mLq2of3FKc8zXbZWXVg+SyMj8cea05XFpatVxkWnSqmReZt2aK5JeXVx+Ofq2oqD6Pc1XLt6S0tBYJgqUeCoJFRERkj5lBQoJP+8o//UWC4EhQHJ0qKurPjzVcUVH7cOT1kENae88F1ByiMXSgREREZF+h5hD1iGvtAoiIiIiItDQFwSIiIiLS7igIFhEREZF2R0GwiIiIiLQ7CoJFREREpN1RECwiIiIi7Y6CYBERERFpdxQEi4iIiEi7oyBYRERERNodBcEiIiIi0u4oCBYRERGRdkdBsIiIiIi0O20mCDazLmb2DzP70syWmdlRZpZhZv82sxXhtWuY18zsITNbaWZLzOzwqPVMD/OvMLPpUfljzOyzsMxDZmYhP+Y2RERERGT/1WaCYOBB4P+cc0OAkcAy4EbgTefcIODNMA5wCjAopB8DfwAf0AK3AUcC44DbooLaP4R5I8udHPJr24aIiIiI7KfaRBBsZp2A44G/ADjndjvncoFpwFNhtqeA74ThacAM530EdDGznsAU4N/OuRzn3A7g38DJYVon59yHzjkHzKixrljbEBEREZH9VJsIgoGBQDbwhJktNLPHzKwD0MM5twkgvHYP8/cG1kUtvz7k1ZW/PkY+dWxDRERERPZTbSUITgAOB/7gnBsNFFJ3swSLkeeakC8iIiIi7VBbCYLXA+udc/PC+D/wQfGW0JSB8Lo1av6+Ucv3ATbWk98nRj51bENERERE9lNtIgh2zm0G1pnZ4JA1CfgCeBmI9PAwHXgpDL8M/DD0EjEeyAtNGf4FTDazruGBuMnAv8K0fDMbH3qF+GGNdcXahoiIiIjsp8w/J9b6zGwU8BiQBKwGLsIH6c8CBwJrgbOcczkhkP09voeHXcBFzrn5YT0/Am4Oq73bOfdEyB8LPAmkAq8DVznnnJllxtpGjCK2jQMlIiIiUr9YTUElSpsJgvcBOlAiIiKyr1AQXI820RxCRERERKQlKQgWERERkXZHQbCIiIiItDsKgkVERESk3VEQLCIiIiLtjoJgEREREWl3FASLiIiISLujIFhERERE2h0FwSIiIiLS7igIFhEREZF2R0GwiIiIiLQ7CoJFREREpN1RECwiIiIi7Y6CYBERERFpdxQEi4iIiEi7oyBYRERERNodBcEiIiIi0u4oCBYRERGRdkdBsIiIiIi0OwqCRURERKTdURAsIiIiIu2OgmARERERaXcUBIuIiIhIu6MgWERERETaHQXBIiIiItLuKAgWERERkXZHQbCIiIiItDsKgkVERESk3VEQLCIiIiLtjoJgEREREWl3FASLiIiISLvTpoJgM4s3s4Vm9moYH2Bm88xshZk9Y2ZJIT85jK8M0/tHreOmkP8fM5sSlX9yyFtpZjdG5cfchoiIiIjsv9pUEAxcAyyLGv8N8N/OuUHADuDikH8xsMM5dzDw32E+zOxQ4FxgGHAy8D8hsI4HHgFOAQ4Fvh/mrWsbIiIiIrKfajNBsJn1Ab4NPBbGDTgR+EeY5SngO2F4WhgnTJ8U5p8GzHLOlTjnvgJWAuNCWumcW+2c2w3MAqbVsw0RERER2U+1mSAY+B3wc6AijGcCuc65sjC+HugdhnsD6wDC9Lwwf2V+jWVqy69rGyIiIiKyn2oTQbCZnQZsdc4tiM6OMaurZ9reyhcRERGR/VhCaxcgOAaYamanAilAJ3zNcBczSwg1tX2AjWH+9UBfYL2ZJQCdgZyo/IjoZWLlb6tjGyIiIiKyn2oTNcHOuZucc32cc/3xD7a95Zw7H3gbODPMNh14KQy/HMYJ099yzrmQf27oPWIAMAj4GPgEGBR6gkgK23g5LFPbNkRERERkP9UmguA63ABcZ2Yr8e13/xLy/wJkhvzrgBsBnHNLgWeBL4D/A65wzpWHWt4rgX/he594Nsxb1zZEREREZD9lvjJUGkAHSkRERPYVsZ57kihtvSZYRERERGSvUxAsIiIiIu2OgmARERERaXcUBIuIiIhIu6MgWERERETaHQXBIiIiItLuKAgWERERkXZHQbCIiIiItDsKgkVERESk3VEQLCIiIiLtjoJgEREREWl3FASLiIiISLuT0NoFEBEREWltFa6CsooySstL/WtFaczh6FRaUUp5RXnlcM3pkWllFWWUu6rhcb3HMb7P+Nbe5XZPQbCIiIi0CeUV5ewu383u8t2UlJdQUlZCSXlJVV5ZCaUVpZSUVeVFp0iwGj2+u3x3ZV709DLnA95IXrkrb7H9zEjNUBDcBigIFhERkQYrqyijqLSIorIiikqLKC4rrpaKyoooKSuhqMxPKykr8a/lJZXjkQA3kh8Jessqylp13xLjEkmMTyQhLoHEuBqvNfKjU2RavMV/Yzje4ivni4+LJzEukYFdB7bqfoqnIFhERGQ/55yjuKyYwtJCCncXsqt0V4NSJJCtDHrLipo1UDWM5IRkkuN9SopPIik+ieSEqOH4ZBLjE6tNT4xPrBxOik8iMa5qPCEuodo8kSA2khcJfOMtHjNrtn2TtkdBsIiIyD6grKKMwt2F5O/OJ78kn4LdBZWpsLSw+vjuwqqAt2wXhbsLcbi9Uo54iyc1MZWUhBRSE1JJTfDD9aXK4DYh2Y/HJ38jX4GotCQFwSIiIi2stLyUnSU7K1NeSV618fyS/MpgN3+3T7tKd+3RNlMSUkhLTKNDYgfSEtNIT0onNSGVtMQ00hLTSE2sGk5LTKucVi3gTUwlMS5RgarsFxQEi4iI7CHnHDtLdpJbnEteSR65xbl+uDivWl5ecR55JXkUlRU1ehuGkZ6UTsekjnRM7kh6UjodEjuQnpReLXVI6lA5rTLYTUwlIU63fJFo+kSIiIjUoryinB3FO9i+azs5RTnsKN7BjqId1YZ3FO8gtzi3Ub0LxFs8nZI70Sm5E52TO1cOR1LH5I6VwW7ktUNiB9XAiuxFCoJFRKRd2l2+m+zCbLbt2kb2rmy279rO9iIf7EaGc4tzG9yWNj0pnS7JXeiS4lPnlM5Vw8mdK/M6J3cmLTFNAa1IK1MQLCIi+51I84QthVvYWriVLQVbKoPd7MJsthVtY2fJznrXYxhdU7qSmZpJRmoGGakZdE3tSteUrt94TYxPbIE9E5G9RUGwiIjskwp2F7ApfxNbCrewpSAEu4VVr7vLd9e5fEJcApmpmXRL60ZWWhZZaVlkpGaQmZZJZmommWmZdEnpora0IvspfbJFRKRNcs6xo3gHG/M3srlgM5vyN/nXAv+avzu/zuU7JnWke4fu9OjQg24dutEtrRvdOlQFvF1TuqpJgkg7piBYRERaVeHuQjbmb2RD/gY27NxQNZy/geKy4lqXS0lI4YAOB3BAuk/dO3SnR3oPunfoTvcO3UlLTGvBvRCRfY2CYBERaXbOOXKLc1m3cx1r89ayLm8d63b6lFucW+tynZI70TO9Jz3Te3JA+gH07OiHe3bsSefkzqrJFZEmUxAsIiJ7VV5xHmty17Amd40PeEOwW7C7IOb8SfFJ9ErvRe9OvendsXfla6+OveiY3LGFSy8i7YWCYBERaZLd5btZl7euMuBdk7uGNXlraq3Z7ZDYgQM7H0jfTn3p27lv5Wu3tG6q0RWRFqcgWERE6lW4u5BVO1axesdqVuWsYtWOVazfuT5mH7qpCan069yP/l36c2DnA33g27mvHkQTkTZFQbCIiFSTV5zHypyVrNqxqjLg3VK45RvzGUbfTn3p17kfA7oOqHxVza6I7AvaRBBsZn2BGcABQAXwJ+fcg2aWATwD9AfWAGc753aYv7o+CJwK7AIudM59GtY1HfhFWPVdzrmnQv4Y4EkgFXgNuMY552rbRjPvsohIm1BcVsyqnFUs376cFTkrWL59ecyANyk+if6d+zOw60AOyjiIg7oeRL8u/UiKT2qFUouI7DlzrmF/B9mshTDrCfR0zn1qZh2BBcB3gAuBHOfcvWZ2I9DVOXeDmZ0KXIUPgo8EHnTOHRkC2vnAWMCF9YwJgfPHwDXAR/gg+CHn3Otm9ttY24hRzNY/UCIie8A5x/qd61m2bRn/2fYflm9fztd5X3+jSUNyfDIHZxzMwRkH+6C360H06dSH+Lj4Viq5iDSBfo6pR5uoCXbObQI2heF8M1sG9AamARPDbE8Bc4AbQv4M5yP4j8ysSwikJwL/ds7lAJjZv4GTzWwO0Mk592HIn4EPsl+vYxsiIvu0krISlm9fzrJty1iWvYwvt3/5jR4a4iyOAZ0HcEjmIRySeQiDMgfRt1NfBbwist9rE0FwNDPrD4wG5gE9QoCMc26TmXUPs/UG1kUttj7k1ZW/PkY+dWxDRGSfsrNkJ59v/ZylW5eybNsyVu9YTbkrrzZPZmomQ7OGMiRrCIdkHsLArgNJTkhupRKLiLSeNhUEm1k68DxwrXNuZx0PVsSa4JqQLyKyz8otzuXzrZ/z2ZbP+Hzr56zdubbadMMY2GUgh3Y7lKHdhjI0ayhZaVl6aE1EhDYUBJtZIj4AnumceyFkbzGznqGGtiewNeSvB/pGLd4H2BjyJ9bInxPy+7KyBgUAABvKSURBVMSYv65tiIi0KXnFeSzZsoTPtvqgd93OddWmJ8UnMSRzCId1P4xDux3K4KzBpCSktFJpRUTatjYRBIfeHv4CLHPOPRA16WVgOnBveH0pKv9KM5uFfzAuLwSx/wLuMbOuYb7JwE3OuRwzyzez8fhmFj8EHq5nGyIirWp3+W6Wbl3Kos2LWLR5EatzV1ebnhyfzNCsoQzvMZzDuh/GoIxBJMYntlJpRUT2LW2ld4hjgXeBz/BdpAHcjA9YnwUOBNYCZ4WA1oDfAyfju0i7yDk3P6zrR2FZgLudc0+E/LFUdZH2OnBV6CItM9Y2YhSz9Q+UiOzXKlwFq3JWsXjLYhZuWsiybcsorSitnJ4Un8ShWYcyoscIhvcYzsEZB5MQ1ybqMkSk7VG7p3q0iSB4H6EDJSJ7XX5JPgs3L2T+xvks2LSAnSU7K6cZxkFdD2LUAaMYdcAohnYbqn55RaShFATXQ1UIIiItyDnHV7lfMX/jfOZvnM+X276s1k9v97TujO45mlEHjGJEjxF0Su7UiqUVEdl/KQgWEWlmu8t3s3DTQuZtmMeCTQvIKapqcZUQl8CwbsMY22ssY3uNpXfH3uq9QUSkBSgIFhFpBvkl+Xy84WM+Wv8RCzcvpKS8pHJaZmpmZdA7ssdIUhNTW7GkIiLtk4JgEZG9ZEvBFj5a/xEfrf+IpdlLqzVzGJQxiPF9xjOu9zj6de6n2l4RkVamIFhEZA9syt/Eu2vf5f2171frwize4hnRYwTj+4znyN5HkpmW2YqlFBGRmhQEi4g00qb8Tby/7n3e/frdaoFvakIqY3uNZXyf8YzpOYYOSR1asZQiIlIXBcEiIg2wuWAz7619j/fWvseqHasq81MTUhnfZzzHHngsow8YrT+rEBHZRygIFhGpRV5xHu+ufZc5a+bwn+3/qcxPSUhhfO8Q+PYcrb57RUT2QQqCRUSilJSVMG/DPOasmcOCTQuocP5PLFMSUjiy95Ecd+BxCnxFRPYD+se4htOBEtlPVbgKlmxZwpw1c3h/3fsUlxUDEGdxjOk5hon9J3Jk7yNJTkhu5ZKKiDTY/9/evUdZVd4HH//+9j5n7lcGROTiIE5FAwSUWAUxRAV16tJEk3qrK9U0vqWNJrV9Y1u7Xt92xcYLlZC8jettUWNXiNamBilVikoimC4RMeQV5X4RBpTrwNzOmTlnn9/7x97nzD6HGZgZhrn+Pms963me3372Pvs8zBl+s8++2C1oTsOOBBtjhq0DjQd4c9ebvLX7rawHWFxUdRFzq+cyZ8IcygvK+3EPjTHGnC2WBBtjhpV4Ms6v9/6aN3a9wUeHP8rEx5SMYW71XOZWz+W80vP6cQ+NMcb0BUuCjTFDnqqy7eg23tj1Bms+WUMsGQMg383nqglXMX/SfC4eebE9wMIYY4YRS4KNMUNWY2sjq3ev5r92/hf7GvZl4pOrJjNv0jzmTJhjjyw2xphhypJgY8yQoqpsP7ad17a/xtq9a2nz2gAozy/nmonXMO+CeYwvH9/Pe2mMMaa/WRJsjBkS4sk4az5Zw2vbX8t6mMWMc2dw44U38oWxXyDi2K88Y4wxPvsfwRgzqO07sY/Xd7zO6t2raU40A1CaV8p1F1zHjRfeyJjSMf28h8YYYwYiS4KNMf2itfUADQ3rcN1iIpERRKMjiEarcN0yRISSkhKampo6XFdVef/A+yzfupyNBzdm4pOrJlNbU8vsCbM5sO8A866cx6ZNm065H3PnzmXhwoXMnDmzV9+fMcaYgc2SYGNMn1JNceTIqxw8+FNU2zoY4RKNVqLaxiefPBYkyFVEIiNIUsTa/ZtZufvXfNZ0GMS/w8OXqr9EbU0tEysn9vn7McYYMzg5/b0DxpjhIx6vY+fO7/LZZ8+h2kZZ2SwqK6+jpORSCgqqcd1SwCOROIJqioaGdzl27DU+/mQJP/r1n/O1f/4yf/S1Bbz4J//Cu997g5laz/+aNo4r3E/5k7tu5XOfq2bKlEm8+eYLtLYeAFKoKrt27WLGjBmsX7+eWCzGHXfcwbRp07j99tuJxWKZ/XvxxReZOnUqU6ZM4eGHHwbg5Zdf5qGHHgJg8eLFXHDBBQDs3LmTq666CoDq6moeffRRLr30UqZOncqWLVv6dF6NMcZ0nx0JNsacdf7R32XB0d8EkcgIxo79FmVlXzhpbCqVIJmsx3GqaS7+ff5j2yre/2wTXirJRz9rZtq1Y/jm79ew41eHeXvJDm5dXMkjj2xg2rRKnnxyKp6ntLS8yO7dCeLxOlasuJ6HH/6AJ564idGjV/P4408hcoS3317Mli2HmTPnLpLJFvbv38/DDz/Mhg0bqKysZP78+Sxbtoyrr76ap556CoC1a9dSVVXF/v37eeedd5gzZ05mv0eOHMkHH3zAj3/8YxYuXMiSJUv6bH6NMcZ0nyXBxpizKh7fR13dD4jFtgFQWTmPMWO+gesWdzheJMKGgztp85I8tu5lAIoKq5kzYQ4f1H2PN3+wg2g0Svx3G/k/PzqfiRO/z/vvX8WSJYtx3UYSiWOUlR0jFvuE+vo2Hnzwv3n66ZlUVx/lxIl3WLt2PXfdNZG6uqcpKYGammL27HmE995LMGNGHidOPEFLywhqa89j5cp/5uqri2hoOMLBgxvZu3c3d955J2vWrGHt2rXceuutmf1Oty+77DJeeeWVszyrxhhjzpQlwcaYs0LV4/DhX3Do0FJUk0SjIxk79luUll7W4fiEl+CXe37JK5tfYX/jflKaojSvlNqaWmprahlROIK/l7/PjHfdAkRcSkqmIBJl1Kibyc/Pzyx3nD1UVa1nwoTxHDgwn3nzakkm68nP/59UVMyhvPyc4Ijzu4gUoBonlYoTi20lFoPm5r20tDSyf/+PuOQSj0WL/oDRo49z3nmvsmzZQTZsOMIDD1Swb99mPK+JxsY3qa+fQDx+gLa2JhKJY0Qi5Yi4Z32ujTHdpAqpFHjeqUtnY3Lj6f6p6nB78mSYMqW/Z2HYsyTYGNOr/HN513Ho0L8Sj/v3662snM+YMfd1ePS3ua2Z13e8zvKty6mP1wMwqmgUUTfKc7c8R0GkIDN21qxZvPTSS9xzzz0sXbo0c07utddeyzPPPMN3vvMdPM+judm/VVpeXh6vvrqc66+/nsrK8dx1113Mm3c7K1d+zFe/+iSbNm1i69YnmDTp+8yeXcU//MMcysr+itLSJKtXP8i9995MZeVEZs1yWbToLRYsmMLkyRW8995G8vMdHGcrx49vJZVq4dChn5FI5PHpp8eJxbazZcvXAcF1S4lEKnJKeaZ23fJM33EK7NHNZnBS9RO8ZBISiY5LeFluO91PJv3thOPh5enXCI/LjZ2uTpf+dMcdlgQPAJYEG2N6hefFqa9/k6NHl9PW9ilAcPT3QUpLZ5w0/lDzIVZsW8HKHSuJJf2L0yZWTOS2i29j9oTZ/CT2Ey6svjAz/qGHHuKHP/wh9913H0899RSjRo3i+eefB/wL1u6//36effZZXNflmWeeYcwY//7AxcXFrFixgnnz5lFcXMyCBQu49957mTZtGtOnT+fyyy8HYNy4iTz++JPcdNP/QFWpra3lD//wSQBuvfUmvvvdC7n99heoqbmQSZOuo6ZmAtXV3yaZrMd11zFixA2UlXkUFHyM4+zCdcvxvIZMaW3de9o5FMkjEikLJcbluG4ZkUhZTsJchuuW4bolljSbbKrQ1gatraeu0+1Eor1Ox8MlHQ8vTyev4XYi4b/2YCICkQi4bnuJRMBxsmOu2x5znPZ1wrHccbnLc8deckl/v3sDiA62H9r+YxNlTAcSiWMcPfofHDu2Es/z7+sbjY5m5MhbqKych+u2H8lNppK8W/cuq3auYuNnG9HgYzXtnGncdsltzDh3xpBK6lQ9kskGksnjJJMnSCbrSSZP4HknQrH2uuNbxp2K4LolmUS5vS4NjkCXZtrhvuPknZX3a7oolYJ43C+xWHadW1pbO293VvqL40A0Cnl5fh2J+HW6hPvhtuu2x9LxdLuzkl4nnbimY6erw+0h9LumE0P+DZ4pS4K7zibKmJBYbDdHjizjxIk1qCYBKCqazMiRX6Gs7ApE2u/AWNdQx6qdq3hr91s0tDYAEHWizBo/i1suuoWaqpp+eQ8DiaqSSrWSTB7H8xqCZLkhSKLTiXN73PMaM390dJdIXigpLsFxijNtv6TbxZnlfrt4+CbQySS0tGSXWKzjdjqpTcfCiW4s5h9BPZvy8tpLfr5fOuufqkSj/th0spqOpevcuGN3XR1gLAk+DUuCu84mygxrqilise00Nm6gsfF9YrHtwRKhrGwWI0d+meLiyZnxrclW3tn7Dqt2ruLjIx9n4tXl1cyfNJ+51XMpzS/t43cxtKh6eF4TyWRDkDA3ZhJnP0n2ix9vL+k/WnpCJJpJkP3kuKiDdlEoaS7CdYtCdWHfJ9Kq/hHSpia/NDe3l9x+c7OfuObWvZm4ikBBARQW+nVuu6DATz4766fb6YQ2XPLyLBk1aZYEn4YlwV1nE2WGnWSykaamD4LEdwOe15BZ5jgFVFbOo6rqZvLzzyXhJdh+bDubDm3iw4MfsvnIZlo9/6vZgkgBXzz/i8yfNJ+aETVD6pSHwSZ9xNnzmjJHk/26OdQPx9KliVSq+YwS6DSRSE5SXJjV9vuFOE5RqF2Ao3k4LSm/NLXhtCSCOuYns42N7YlubjnTC6EcB4qKOi+Fhe31qUo6ebXPgDn77IfsNCwJ7jqbKDOkqSqJxFHi8d3EYjtpatpAS8tWwj/60eg5lJbOpLR0JnmFk9lR/0km6d1ydAttXvbRsouqLuL6Sdcz5/w5WXd5MIOTqqLalpMYt2Qly+G+3245qQYPPMVpTuA2JXGakrjNSZzmoE73mxKZuNPi4bR0koCLILggDoID4iI4/u3p0rG8PLSkCIoKoaQELSlGikuguBQpKYGScqS4FCkpQ4rLoLgCp6Qcp7QCKa5ACgoscTWDjf3AnoYlwV1nE2WGDM+L09r6CbHYbuLxPZl2KtWcNU4kgpt/Ec3ORE7oKA7FWjnQeID9jfvZc3wPiVQia/yEsglMHT2VKedMYco5U6goqOjLt2X6k6p/JPb4cb+cONFeh4oePw4NJ9DG46h6oB6qHkqqk7YHmvLbkiJV6OIVOXjFDl4heEXgFTmkiiJBcbNqryhCqtCF6JmdIiCSh+PkBXU+ItGgDsdz6ygiecHYcCwaioX70U76kaxz7I3pIkuCT8OS4ICI3AAsBlxgiao+njPEJsoMCqlUkmTyGInEURKJI0FpbyeTfhsgmUrRlEjSmEjQkEjQ5EWIaQUtWsyxRD6H40kOx451+lrV5dWZpPdzoz5HeUH5me380qXwyCOwdy9MmACPPQZ3331m2zQ9pwoNDVBf7ye0ndXpJDeV6vq2RaCkBMrLoazs1KW01C/FxSed7+ofnU6QSrWSSsU7KO1x1dag35pZlh1L99uCdnvd/5xQYhwJJceRUOIcySqOEwXck+LZxe2kjoTWdTNxfz+yY/4DYbLj/rpuaJlrp0H1PZvw07AkGBD/U7oNmAfUAeuBO1X149AwmyjT6/z/wD1Uk0Fpy/nPOPs/Z8+LE0+cIJ44TixxgnhbI63JBuKJRuKJRlqTzcSTzcQ9j1bPI+Z5xJJe0E8R9zxiySTNyRTNXpSWVATXKcQJLlxyJHrSr01XXM4tOZdxZeMYVzaOsaVjM+1evbBt6VK4/37/IqS0oiL4p3+yRLgrVLMfQhC+F2y6hGPpOxmELwDLbXc3sS0pgYoKv5SX+yXdLitrj5WX+0ntILmAK30aiP9ZbAva4TocT6DaFiTmHcVagzqBanvxxyZQTQbtZNbyoUFOkSA7QdzJimcvd7LGdbwsvF5uvLNaQtuUTLy9nd7v3GUS2o6EthWOSdZyABGHvLzR5OWNPsvzbUnw6djDMnyXAztUdReAiLwE3AJ8fMq1etFPf/wVDh7b11cvNyBoF/6u6O4faeFtalatWbHsv2nSy4Ja/XZ4rAJKKrTNdC+9jt/2gjUV9S9ACtrtdYqUBjVKSlN44LeDOilB7X8RjBequ5yOpI/QkH20Jn3UCCKIQBFCheNSIS6V4lLpFFApRVQ4hYxxyhnrVDDaKSVSn370bwzYEZReEP73/Zu/yU6Awe8/8ICfjHVlG10dc7p+OBZe1lksXcJ9aE8gw2NSqZPb4VhHj1jNjeU+SSuZ7F6y2h2lpX4iW1nZXqdLeXl7vKzMv//qECQiiOTjOPmnH3wWZP+xnE6MkzklnVjnxk9dIL1dL2dZe7x9TKqDmJcpfqzjPqQADV4zOeieq9HbzjnnDkaPtj/u+9vQ/I3VfWOBcAZaB/xuX+7Ae7veZXtr5187m6FOsmsRJCfmIrgiRFQoSLlE1SEPlzx1yVe/9tsRilIuxSmXopxSnHIpTLmUehEqvShlXgQn62BBPCgA+8/+2w6rr+88/vrrfbsvg1X4YQMd3Rs2HCss9E8vKCry647a5eX+tky/8pPwCP5/2YPzAlP/QEFwbrem8BPkVCbZ9pdlx9Pj08vC7fS47G2mTlGH19VOx2Uv06x12rdDB+M1J6adtP06Gj3rR4FNF1gS7OvoK4M+/Tv15ivvof5YXa9sSwb1NyAn7/vpziNrX975uPCc+OOzZ0nSX1sRSj4F/wrz9AhJL3Uy4x3x+/52HRxxcYKr0R0cHHEyfREXV1xcieAQwXUiuBIhIi6OBGuIEMHFFYeIuLgEdajfK+fVDcRz89atg8OHT46PGgULFpx63dz305X319k6Ha3b0TKRjvvpWPqr/nA8XdKPUU23w3GRjh/JmvvY1tySXteYAaj9lAP3tGON6SuWBPvqgPGh/jjgQF/uwHW3PdmXL2fMwLNoUcfnBC9aBLW1/bdfxhhjhqTBcVXC2bceqBGRiSKSB9wBLO/nfTJmeLn7bv8iuPPP949onn++XRRnjDHmrLG7QwREpBb4Af4t0p5T1cdyhthEGWOMMWawsPOjTsOS4K6ziTLGGGPMYGFJ8GnY6RDGGGOMMWbYsSTYGGOMMcYMO5YEG2OMMcaYYceSYGOMMcYYM+zYfYK7zk4wN8YYY4wZIuxIsDHGGGOMGXYsCTbGGGOMMcOOJcHGGGOMMWbYsSR4gBCRG0Rkq4jsEJG/7O/9GYxE5DkROSQim0KxESLyhohsD+rK/tzHwURExovIL0Vks4h8JCLfDuI2pz0gIgUi8p6I/DaYz78N4hNFZF0wn/8aPLrddIOIuCLyGxFZEfRtTntIRPaIyIcislFE3g9i9pk/AyJSISI/F5Etwe/TK21OBwZLggcAEXGBfwRuBC4B7hSRS/p3rwalnwA35MT+EnhLVWuAt4K+6Zok8OeqejFwBfCnwc+lzWnPtALXqOrngenADSJyBfAEsCiYz3rgG/24j4PVt4HNob7N6Zn5kqpOV9WZQd8+82dmMbBSVScDn8f/WbU5HQAsCR4YLgd2qOouVW0DXgJu6ed9GnRUdQ1wLCd8C/BC0H4B+HKf7tQgpqqfquoHQbsR/xf3WGxOe0R9TUE3GhQFrgF+HsRtPrtJRMYBvwcsCfqCzWlvs898D4lIGXA18CyAqrap6nFsTgcES4IHhrHAvlC/LoiZMzdaVT8FP6kDzunn/RmURKQamAGsw+a0x4Kv7TcCh4A3gJ3AcVVNBkPss999PwC+C6SCfhU2p2dCgVUiskFE7g9i9pnvuQuAw8DzwSk7S0SkGJvTAcGS4IGho3sQa5/vhTEdEJES4N+B76hqQ3/vz2Cmqp6qTgfG4X8DdHFHw/p2rwYvEbkJOKSqG8LhDobanHbdbFW9FP/0vD8Vkav7e4cGuQhwKfCMqs4AmrFTHwYMS4IHhjpgfKg/DjjQT/sy1BwUkTEAQX2on/dnUBGRKH4CvFRVXwnCNqdnKPg69Ff451pXiEj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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el131398182073308807556711451\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.0, 63.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-3.0, 63.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818206702504\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818206893280\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818206893616\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el13139818206894400\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el13139818206895184\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818206413656\", \"color\": \"#FF0000\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818206416008\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818206441712\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el13139818206442776\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el13139818206443840\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data04\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818206778424\", \"dasharray\": \"none\", \"alpha\": 0.5, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [{\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818206413656pts\", \"facecolor\": \"#FF0000\", \"edgecolor\": \"#FF0000\", \"edgewidth\": 1.0, \"alpha\": 1, \"zorder\": 2, \"markerpath\": [[[0.0, 3.0], [0.7956093000000001, 3.0], [1.5587396123545605, 2.683901074764725], [2.121320343559643, 2.121320343559643], [2.683901074764725, 1.5587396123545605], [3.0, 0.7956093000000001], [3.0, 0.0], [3.0, -0.7956093000000001], [2.683901074764725, -1.5587396123545605], [2.121320343559643, -2.121320343559643], [1.5587396123545605, -2.683901074764725], [0.7956093000000001, -3.0], [0.0, -3.0], [-0.7956093000000001, -3.0], [-1.5587396123545605, -2.683901074764725], [-2.121320343559643, -2.121320343559643], [-2.683901074764725, -1.5587396123545605], [-3.0, -0.7956093000000001], [-3.0, 0.0], [-3.0, 0.7956093000000001], [-2.683901074764725, 1.5587396123545605], [-2.121320343559643, 2.121320343559643], [-1.5587396123545605, 2.683901074764725], [-0.7956093000000001, 3.0], [0.0, 3.0]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]}], \"texts\": [{\"text\": \"Lockdown\", \"position\": [23.0, 50000.0], \"coordinates\": \"data\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206895968\"}, {\"text\": \"Time (days)\", \"position\": [0.5, -0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206702952\"}, {\"text\": \"SIR-Model with Lockdown. Total Population: 1000000, Days Infectious: 4, R_0: 3.0, CFR: 20.0 R_0_2: 0.9, L: 23 days\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206781056\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.9218175128771156], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206415504\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.8528329654157469], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206416624\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 0.7838484179543782], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206442328\"}, {\"text\": \"Recovered\", \"position\": [0.8803763440860216, 0.7148638704930096], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206443392\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 999999.0, 1.0, 0.0, 0.0], [1.0169491525423728, 999997.9700702443, 1.6866193168346386, 0.0, 0.3433104385728133], [2.0338983050847457, 999996.2329740275, 2.844681782949154, 0.0, 0.9223441891099943], [3.0508474576271185, 999993.303166633, 4.797881602986749, 0.0, 1.8989517634506798], [4.067796610169491, 999988.3617379002, 8.092152324503974, 0.0, 3.546109774828892], 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[0.8230286738351255, 0.6964679911699779], [0.8230286738351255, 0.6872700515084621], [0.8266129032258065, 0.6872700515084621]]}, \"id\": \"el13139818207330880\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el131398182073308807556711451\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.0, 63.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-3.0, 63.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818206702504\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818206893280\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818206893616\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el13139818206894400\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el13139818206895184\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818206413656\", \"color\": \"#FF0000\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818206416008\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818206441712\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el13139818206442776\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el13139818206443840\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data04\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818206778424\", \"dasharray\": \"none\", \"alpha\": 0.5, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [{\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818206413656pts\", \"facecolor\": \"#FF0000\", \"edgecolor\": \"#FF0000\", \"edgewidth\": 1.0, \"alpha\": 1, \"zorder\": 2, \"markerpath\": [[[0.0, 3.0], [0.7956093000000001, 3.0], [1.5587396123545605, 2.683901074764725], [2.121320343559643, 2.121320343559643], [2.683901074764725, 1.5587396123545605], [3.0, 0.7956093000000001], [3.0, 0.0], [3.0, -0.7956093000000001], [2.683901074764725, -1.5587396123545605], [2.121320343559643, -2.121320343559643], [1.5587396123545605, -2.683901074764725], [0.7956093000000001, -3.0], [0.0, -3.0], [-0.7956093000000001, -3.0], [-1.5587396123545605, -2.683901074764725], [-2.121320343559643, -2.121320343559643], [-2.683901074764725, -1.5587396123545605], [-3.0, -0.7956093000000001], [-3.0, 0.0], [-3.0, 0.7956093000000001], [-2.683901074764725, 1.5587396123545605], [-2.121320343559643, 2.121320343559643], [-1.5587396123545605, 2.683901074764725], [-0.7956093000000001, 3.0], [0.0, 3.0]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]}], \"texts\": [{\"text\": \"Lockdown\", \"position\": [23.0, 50000.0], \"coordinates\": \"data\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206895968\"}, {\"text\": \"Time (days)\", \"position\": [0.5, -0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206702952\"}, {\"text\": \"SIR-Model with Lockdown. Total Population: 1000000, Days Infectious: 4, R_0: 3.0, CFR: 20.0 R_0_2: 0.9, L: 23 days\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206781056\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.9218175128771156], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206415504\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.8528329654157469], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206416624\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 0.7838484179543782], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206442328\"}, {\"text\": \"Recovered\", \"position\": [0.8803763440860216, 0.7148638704930096], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206443392\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 999999.0, 1.0, 0.0, 0.0], [1.0169491525423728, 999997.9700702443, 1.6866193168346386, 0.0, 0.3433104385728133], [2.0338983050847457, 999996.2329740275, 2.844681782949154, 0.0, 0.9223441891099943], [3.0508474576271185, 999993.303166633, 4.797881602986749, 0.0, 1.8989517634506798], [4.067796610169491, 999988.3617379002, 8.092152324503974, 0.0, 3.546109774828892], 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[0.8230286738351255, 0.6964679911699779], [0.8230286738351255, 0.6872700515084621], [0.8266129032258065, 0.6872700515084621]]}, \"id\": \"el13139818207330880\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SIR_model_with_lockdown(N=1_000_000, D=4, R_0=3.0, CaseFatalityRate=0.2, max_days=60, L=23, R_0_2=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, with highly contagious viruses, each day counts. Let's impose even more drastic measures: a complete lockdown after 20 days that reduces R_0 to 0.1 reduces fatalities to around 8k!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el13139818206778648121121273\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el13139818206778648121121273\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.0, 63.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-3.0, 63.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818206554992\", \"lines\": [{\"data\": \"data01\", \"xindex\": 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-0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206190784\"}, {\"text\": \"SIR-Model with Lockdown. Total Population: 1000000, Days Infectious: 4, R_0: 3.0, CFR: 20.0 R_0_2: 0.1, L: 20 days\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206302616\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.5896799116997793], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206529072\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.5206953642384106], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206415840\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el13139818206778648121121273\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.0, 63.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-3.0, 63.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818206554992\", \"lines\": [{\"data\": \"data01\", \"xindex\": 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-0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206190784\"}, {\"text\": \"SIR-Model with Lockdown. Total Population: 1000000, Days Infectious: 4, R_0: 3.0, CFR: 20.0 R_0_2: 0.1, L: 20 days\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206302616\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.5896799116997793], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206529072\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.5206953642384106], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206415840\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 0.45171081677704195], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206415056\"}, {\"text\": \"Recovered\", \"position\": [0.8803763440860216, 0.38272626931567333], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206412928\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 999999.0, 1.0, 0.0, 0.0], [1.0169491525423728, 999997.9609752608, 1.6926826328090636, 0.0, 0.34634210581181823], [2.0338983050847457, 999996.2022394188, 2.865171483360821, 0.0, 0.9325890974412829], [3.0508474576271185, 999993.2252708583, 4.849811944594084, 0.0, 1.9249171966013674], [4.067796610169491, 999988.1862505538, 8.209143202830955, 0.0, 3.6046062428876153], 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6480.905396149174, 33932.21142502818], [47.79661016949152, 966026.3932515633, 32.13079570741822, 6513.479541565126, 33941.47595272907], [48.813559322033896, 966025.683224553, 25.49084474299804, 6539.322155272358, 33948.825930703904], [49.83050847457627, 966025.1199276993, 20.223064560008925, 6559.824319027932, 33954.657007740476], [50.847457627118644, 966024.6730385213, 16.04389099693979, 6576.0896478289915, 33959.28307048161], [51.86440677966102, 966024.3185008487, 12.728359494372727, 6588.993693912973, 33962.953139656594], [52.88135593220339, 966024.037229838, 10.097995194107298, 6599.231075535627, 33965.864774967624], [53.89830508474576, 966023.8140846181, 8.011205703502544, 6607.3528667946375, 33968.17470967791], [54.91525423728813, 966023.6370532531, 6.355659251227977, 6613.796261101551, 33970.007287495224], [55.932203389830505, 966023.496606123, 5.0422378114439725, 6618.908104717464, 33971.46115606517], [56.94915254237288, 966023.3851829475, 4.00023995439017, 6622.963567131163, 33972.61457709768], [57.96610169491525, 966023.2967858096, 3.173574960081778, 6626.180953224072, 33973.5296392298], [58.983050847457626, 966023.2266562862, 2.5177434630171933, 6628.733454462713, 33974.25560025013], [60.0, 966023.171019303, 1.997442071695711, 6630.758471720999, 33974.83153862454]], \"data02\": [[20.0, 0.0]], \"data03\": [[0.8301971326164875, 0.6057763061074319, 0.5367917586460632, 0.46780721118469465, 0.39882266372332603], [0.8660394265232976, 0.6057763061074319, 0.5367917586460632, 0.46780721118469465, 0.39882266372332603]], \"data04\": [[0.8266129032258065, 0.35513245033112584], [0.9874551971326165, 0.35513245033112584], [0.9910394265232975, 0.35513245033112584], [0.9910394265232975, 0.36433038999264167], [0.9910394265232975, 0.6356696100073583], [0.9910394265232975, 0.6448675496688742], [0.9874551971326165, 0.6448675496688742], [0.8266129032258065, 0.6448675496688742], [0.8230286738351255, 0.6448675496688742], [0.8230286738351255, 0.6356696100073583], [0.8230286738351255, 0.36433038999264167], [0.8230286738351255, 0.35513245033112584], [0.8266129032258065, 0.35513245033112584]]}, \"id\": \"el13139818206778648\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el13139818206778648121121273\", {\"width\": 720.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.0, 63.0], \"ylim\": [-49999.950000000004, 1049998.95], \"xdomain\": [-3.0, 63.0], \"ydomain\": [-49999.950000000004, 1049998.95], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#FFFFFF\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818206554992\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818206553816\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818206554768\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el13139818206552640\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el13139818206553088\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818206531144\", \"color\": \"#FF0000\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818206530696\", \"color\": \"#0000FF\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el13139818206415728\", \"color\": \"#BFBF00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el13139818206413488\", \"color\": \"#FF0000\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el13139818206414328\", \"color\": \"#007F00\", \"linewidth\": 2.0, \"dasharray\": \"none\", \"alpha\": 0.7, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data04\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818206527840\", \"dasharray\": \"none\", \"alpha\": 0.5, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [{\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818206531144pts\", \"facecolor\": \"#FF0000\", \"edgecolor\": \"#FF0000\", \"edgewidth\": 1.0, \"alpha\": 1, \"zorder\": 2, \"markerpath\": [[[0.0, 3.0], [0.7956093000000001, 3.0], [1.5587396123545605, 2.683901074764725], [2.121320343559643, 2.121320343559643], [2.683901074764725, 1.5587396123545605], [3.0, 0.7956093000000001], [3.0, 0.0], [3.0, -0.7956093000000001], [2.683901074764725, -1.5587396123545605], [2.121320343559643, -2.121320343559643], [1.5587396123545605, -2.683901074764725], [0.7956093000000001, -3.0], [0.0, -3.0], [-0.7956093000000001, -3.0], [-1.5587396123545605, -2.683901074764725], [-2.121320343559643, -2.121320343559643], [-2.683901074764725, -1.5587396123545605], [-3.0, -0.7956093000000001], [-3.0, 0.0], [-3.0, 0.7956093000000001], [-2.683901074764725, 1.5587396123545605], [-2.121320343559643, 2.121320343559643], [-1.5587396123545605, 2.683901074764725], [-0.7956093000000001, 3.0], [0.0, 3.0]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]}], \"texts\": [{\"text\": \"Lockdown\", \"position\": [20.0, 50000.0], \"coordinates\": \"data\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206552752\"}, {\"text\": \"Time (days)\", \"position\": [0.5, -0.08048197203826342], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206190784\"}, {\"text\": \"SIR-Model with Lockdown. Total Population: 1000000, Days Infectious: 4, R_0: 3.0, CFR: 20.0 R_0_2: 0.1, L: 20 days\", \"position\": [0.5, 1.0275938189845473], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818206302616\"}, {\"text\": \"Susceptible\", \"position\": [0.8803763440860216, 0.5896799116997793], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206529072\"}, {\"text\": \"Infected\", \"position\": [0.8803763440860216, 0.5206953642384106], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206415840\"}, {\"text\": \"Dead\", \"position\": [0.8803763440860216, 0.45171081677704195], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206415056\"}, {\"text\": \"Recovered\", \"position\": [0.8803763440860216, 0.38272626931567333], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818206412928\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 999999.0, 1.0, 0.0, 0.0], [1.0169491525423728, 999997.9609752608, 1.6926826328090636, 0.0, 0.34634210581181823], [2.0338983050847457, 999996.2022394188, 2.865171483360821, 0.0, 0.9325890974412829], [3.0508474576271185, 999993.2252708583, 4.849811944594084, 0.0, 1.9249171966013674], [4.067796610169491, 999988.1862505538, 8.209143202830955, 0.0, 3.6046062428876153], 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SIR_model_with_lockdown(N=1_000_000, D=4, R_0=3.0, CaseFatalityRate=0.2, max_days=60, L=20, R_0_2=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Fitting SIR with Lockdown to real-world data\n",
    "\n",
    "We now try to fit the SIR-Model's Dead Curve to real data by tweaking the variables. Some of them are constant:\n",
    "- max_days is set to `len(train.groupby(\"Date\").sum().index)` so that we can compare against all available data\n",
    "- N is fixed for each country, that's just the total population\n",
    "- L is fixed for each country (the date it went into lockdown)\n",
    "- D is set to vary from 5 to 20 (according to [this study](https://www.ncbi.nlm.nih.gov/pubmed/32150748), it takes on avg. 5 days to show symptoms, at most 14; according to [this source (German)](https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Steckbrief.html#doc13776792bodyText5), people are infectious up to 5 days after onset of symptoms).\n",
    "- CFR set to vary from $0.1\\% - 10\\%$ (according to [this study](https://wwwnc.cdc.gov/eid/article/26/6/20-0320_article))\n",
    "- R_0 and R_0_2 are set to vary from 0.1 to 3.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SIR-Model's Fatality Curve (no plotting etc.):\n",
    "def SIR_model_with_lockdown_deaths(x, N, D, R_0, CaseFatalityRate, max_days, L, R_0_2):\n",
    "    # BEFORE LOCKDOWN (same code as first model)\n",
    "    I0, R0 = 1, 0  # Initial number of infected and recovered individuals (1 infected, 0 recovered) [this R0 has nothing to do with the basic reproduction number R0]\n",
    "    S0 = N - I0 - R0 # Initial number of susceptible (everyone else)\n",
    "\n",
    "    gamma = 1.0 / D  # see texts above\n",
    "    beta = R_0 * gamma  # see texts above\n",
    "    alpha = CaseFatalityRate\n",
    "\n",
    "    t = np.linspace(0, L, L)  # Grid of time points (in days)\n",
    "    \n",
    "    # Initial conditions vector\n",
    "    y0 = S0, I0, R0\n",
    "    # Integrate the SIR equations over the time grid, t.\n",
    "    ret = odeint(deriv, y0, t, args=(N, beta, gamma))\n",
    "    S, I, R = ret.T\n",
    "    \n",
    "    \n",
    "    # AFTER LOCKDOWN\n",
    "    I0_2, R0_2, S0_2 = I[-1], R[-1], S[-1]  # beginning of lockdown -> starting Infected/Susceptible/Recovered numbers are the numbers at the end of no-lockdown period\n",
    "\n",
    "    gamma = 1.0 / D  # constant\n",
    "    beta_2 = R_0_2 * gamma\n",
    "    alpha = CaseFatalityRate  # constant\n",
    "\n",
    "    t_2 = np.linspace(0, max_days - L + 1, max_days - L + 1)\n",
    "    \n",
    "    # Initial conditions vector\n",
    "    y0_2 = S0_2, I0_2, R0_2\n",
    "    # Integrate the SIR equations over the time grid, t.\n",
    "    ret_2 = odeint(deriv, y0_2, t_2, args=(N, beta_2, gamma))\n",
    "    S_2, I_2, R_2 = ret_2.T\n",
    "\n",
    "    \n",
    "    # COMBINING PERIODS\n",
    "    S_full = np.concatenate((S, S_2[1:]))\n",
    "    I_full = np.concatenate((I, I_2[1:]))\n",
    "    R_full = np.concatenate((R, R_2[1:]))\n",
    "    t_full = np.linspace(0, max_days, max_days)\n",
    "    \n",
    "    # Adding deaths (see text above)\n",
    "    X = np.zeros(max_days)\n",
    "    for day in range(12, max_days):\n",
    "        X[day] = sum(I_full[:day-12])\n",
    "    # X[13:] = I[:-13]\n",
    "    X = alpha * X * gamma\n",
    "    return X[x]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The (hidden as it's almost the same as before) code above defines a function with signature\n",
    "\n",
    "`SIR_model_with_lockdown_deaths(x, N, D, R_0, CaseFatalityRate, max_days, L, R_0_2)`\n",
    "\n",
    "that takes as input the same variables as before and an x and returns the number of fatalities on day x. This function will be used to find suited parameters D, CFR, R_0 and R_0_2 for the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting lmfit\r\n",
      "  Downloading lmfit-1.0.0.tar.gz (258 kB)\r\n",
      "\u001b[K     |████████████████████████████████| 258 kB 7.1 MB/s \r\n",
      "\u001b[?25hCollecting asteval>=0.9.16\r\n",
      "  Downloading asteval-0.9.18.tar.gz (52 kB)\r\n",
      "\u001b[K     |████████████████████████████████| 52 kB 829 kB/s \r\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.16 in /opt/conda/lib/python3.6/site-packages (from lmfit) (1.18.1)\r\n",
      "Requirement already satisfied: scipy>=1.2 in /opt/conda/lib/python3.6/site-packages (from lmfit) (1.4.1)\r\n",
      "Collecting uncertainties>=3.0.1\r\n",
      "  Downloading uncertainties-3.1.2.tar.gz (232 kB)\r\n",
      "\u001b[K     |████████████████████████████████| 232 kB 17.9 MB/s \r\n",
      "\u001b[?25hBuilding wheels for collected packages: lmfit, asteval, uncertainties\r\n",
      "  Building wheel for lmfit (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for lmfit: filename=lmfit-1.0.0-py3-none-any.whl size=87914 sha256=13fba00e9ab4bef9a168df6952e7e5efe411a17b36c87e3cef4e2ae057b14165\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/e2/c7/ed/ccfe7d5de2af8a029a77c9c54f4a592381977d42da50d6be7e\r\n",
      "  Building wheel for asteval (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for asteval: filename=asteval-0.9.18-py3-none-any.whl size=17382 sha256=42f376a52d2ab0caccac37fb30fa27fdbaf0d7d4b050bb159f8feb0f76bed865\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/e4/91/a3/08d0c0afc17a193dc65245d605087dc1c8b2848ec70b18d545\r\n",
      "  Building wheel for uncertainties (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for uncertainties: filename=uncertainties-3.1.2-py3-none-any.whl size=96455 sha256=82f91c5501e6f0b009b808ffe1bea6bf81ddedb5d9db493b83e71125de68567b\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/78/52/c1/1028f3e019b7ee67e1fe1ecfaa049c464aba5fc756acbd0dd0\r\n",
      "Successfully built lmfit asteval uncertainties\r\n",
      "Installing collected packages: asteval, uncertainties, lmfit\r\n",
      "Successfully installed asteval-0.9.18 lmfit-1.0.0 uncertainties-3.1.2\r\n"
     ]
    }
   ],
   "source": [
    "!pip install lmfit\n",
    "from lmfit import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load countries data file (from https://www.kaggle.com/saga21/covid-global-forecast-sir-model-ml-regressions)\n",
    "world_population = pd.read_csv(\"/kaggle/input/population-by-country-2020/population_by_country_2020.csv\")\n",
    "\n",
    "# Select desired columns and rename some of them\n",
    "world_population = world_population[['Country (or dependency)', 'Population (2020)', 'Density (P/Km²)', 'Land Area (Km²)', 'Med. Age', 'Urban Pop %']]\n",
    "world_population.columns = ['Country (or dependency)', 'Population (2020)', 'Density', 'Land Area', 'Med Age', 'Urban Pop']\n",
    "\n",
    "# Replace United States by US\n",
    "world_population.loc[world_population['Country (or dependency)']=='United States', 'Country (or dependency)'] = 'US'\n",
    "\n",
    "# Remove the % character from Urban Pop values\n",
    "world_population['Urban Pop'] = world_population['Urban Pop'].str.rstrip('%')\n",
    "\n",
    "# Replace Urban Pop and Med Age \"N.A\" by their respective modes, then transform to int\n",
    "world_population.loc[world_population['Urban Pop']=='N.A.', 'Urban Pop'] = int(world_population.loc[world_population['Urban Pop']!='N.A.', 'Urban Pop'].mode()[0])\n",
    "world_population['Urban Pop'] = world_population['Urban Pop'].astype('int16')\n",
    "world_population.loc[world_population['Med Age']=='N.A.', 'Med Age'] = int(world_population.loc[world_population['Med Age']!='N.A.', 'Med Age'].mode()[0])\n",
    "world_population['Med Age'] = world_population['Med Age'].astype('int16')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now define \n",
    "1. `fit_SIR`: this function takes a country name, lockdown data (and opt. region name) and first gathers the data (fatalities progression, population, etc.) and then fits the `SIR_model_with_lockdown_deaths`-function from above with fixed N (population), max_days (however many dates are supplied), L (lockdown date) and varying D, R_0, R_0_2, CFR. The function returns the lmfit-module's result object and the country name. The result object contains all we want to know about the curve fitting.\n",
    "2. `fitted_plot`: this functino takes a lmfit-result-object and country name and plots the fitted SIR-model against the real curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_SIR(country_name, lockdown_date, region_name=None):\n",
    "    \"\"\"\n",
    "    y_data: the fatalities data of one country/region (array)\n",
    "    population: total population of country\n",
    "    lockdown_date: format YYYY-MM-DD\n",
    "    \"\"\"\n",
    "    if region_name:\n",
    "        y_data = train[(train[\"Country_Region\"] == country_name) & (train[\"Region\"] == region_name)].Fatalities.values\n",
    "    else:\n",
    "        if len(train[\"Country_Region\"] == country_name) > len(train[\"Country_Region\"] == \"Germany\"):  # country with several regions and no region provided\n",
    "            y_data = train[(train[\"Country_Region\"] == country_name) & (train[\"Region\"].isnull())].Fatalities.values\n",
    "        else:\n",
    "            y_data = train[train[\"Country_Region\"] == country_name].Fatalities.values\n",
    "        \n",
    "    max_days = len(train.groupby(\"Date\").sum().index) # constant for all countries\n",
    "\n",
    "    # country specific values\n",
    "    N = world_population.loc[world_population['Country (or dependency)'] == country_name][\"Population (2020)\"].values[0]\n",
    "    L = train.groupby(\"Date\").sum().index.tolist().index(lockdown_date)  # index of the lockdown date\n",
    "\n",
    "    # x_data is just [0, 1, ..., max_days] array\n",
    "    x_data = np.linspace(0, max_days - 1, max_days, dtype=int)\n",
    "    \n",
    "    # curve fitting from here\n",
    "    mod = Model(SIR_model_with_lockdown_deaths)\n",
    "\n",
    "    # initial values and bounds\n",
    "    mod.set_param_hint('N', value=N)\n",
    "    mod.set_param_hint('max_days', value=max_days)\n",
    "    mod.set_param_hint('L', value=L)\n",
    "    mod.set_param_hint('D', value=10, min=2, max=25)\n",
    "    mod.set_param_hint('CaseFatalityRate', value=0.01, min=0.0001, max=0.1)\n",
    "    mod.set_param_hint('R_0', value=2.0, min=0.1, max=5.0)\n",
    "    mod.set_param_hint('R_0_2', value=2.0, min=0.1, max=5.0)\n",
    "\n",
    "    params = mod.make_params()\n",
    "\n",
    "    # fixing constant parameters\n",
    "    params['N'].vary = False\n",
    "    params['max_days'].vary = False\n",
    "    params['L'].vary = False\n",
    "\n",
    "    result = mod.fit(y_data, params, x=x_data)\n",
    "    \n",
    "    return result, country_name\n",
    "\n",
    "def fitted_plot(result, country_name, region_name=None):\n",
    "    if region_name:\n",
    "        y_data = train[(train[\"Country_Region\"] == country_name) & (train[\"Region\"] == region_name)].Fatalities.values\n",
    "    else:\n",
    "        if len(train[\"Country_Region\"] == country_name) > len(train[\"Country_Region\"] == \"Germany\"):  # country with several regions and no region provided\n",
    "            y_data = train[(train[\"Country_Region\"] == country_name) & (train[\"Region\"].isnull())].Fatalities.values\n",
    "        else:\n",
    "            y_data = train[train[\"Country_Region\"] == country_name].Fatalities.values\n",
    "\n",
    "    max_days = len(train.groupby(\"Date\").sum().index)\n",
    "    x_data = np.linspace(0, max_days - 1, max_days, dtype=int)\n",
    "    x_ticks = train[train[\"Country_Region\"] == \"Germany\"].Date.values  # same for all countries\n",
    "    \n",
    "    plt.figure(figsize=(10,5))\n",
    "    \n",
    "    real_data, = plt.plot(x_data, y_data, 'bo', label=\"real data\")\n",
    "    SIR_fit = plt.plot(x_data, result.best_fit, 'r-', label=\"SIR model\")\n",
    "    \n",
    "    plt.xlabel(\"Day\")\n",
    "    plt.xticks(x_data[::10], x_ticks[::10])\n",
    "    plt.ylabel(\"Fatalities\")\n",
    "    plt.title(\"Real Data vs SIR-Model in \" + country_name)\n",
    "    plt.legend(numpoints=1, loc=2, frameon=None)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Model]]\n",
      "    Model(SIR_model_with_lockdown_deaths)\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 2497\n",
      "    # data points      = 66\n",
      "    # variables        = 4\n",
      "    chi-square         = 218741.018\n",
      "    reduced chi-square = 3528.08094\n",
      "    Akaike info crit   = 542.995274\n",
      "    Bayesian info crit = 551.753893\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    D:                 at boundary\n",
      "    R_0_2:             at initial value\n",
      "[[Variables]]\n",
      "    N:                 4.675478e+07 (fixed)\n",
      "    D:                 2.00000003 (init = 10)\n",
      "    R_0:               1.44953252 (init = 2)\n",
      "    CaseFatalityRate:  0.01504491 (init = 0.01)\n",
      "    max_days:          66 (fixed)\n",
      "    L:                 53 (fixed)\n",
      "    R_0_2:             2.00000000 (init = 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el131398187679730082855992533\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el131398187679730082855992533\", {\"width\": 720.0, \"height\": 360.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.25, 68.25], \"ylim\": [-269.00906606985177, 5649.190387466887], \"xdomain\": [-3.25, 68.25], \"ydomain\": [-269.00906606985177, 5649.190387466887], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": [0, 10, 20, 30, 40, 50, 60], \"tickformat\": [\"2020-01-22\", \"2020-02-01\", \"2020-02-11\", \"2020-02-21\", \"2020-03-02\", \"2020-03-12\", \"2020-03-22\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818206951016\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el13139818179487952\", \"color\": \"#FF0000\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818179575424\", \"color\": \"#FF0000\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data04\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el13139818179572064\", \"dasharray\": \"none\", \"alpha\": 0.8, \"facecolor\": \"#FFFFFF\", \"edgecolor\": \"#CCCCCC\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el13139818179487448pts\", \"facecolor\": \"#0000FF\", 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\"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818179574416pts\", \"facecolor\": \"#0000FF\", \"edgecolor\": \"#0000FF\", \"edgewidth\": 1.0, \"alpha\": 1, \"zorder\": 1000002.0, \"markerpath\": [[[0.0, 3.0], [0.7956093000000001, 3.0], [1.5587396123545605, 2.683901074764725], [2.121320343559643, 2.121320343559643], [2.683901074764725, 1.5587396123545605], [3.0, 0.7956093000000001], [3.0, 0.0], [3.0, -0.7956093000000001], [2.683901074764725, -1.5587396123545605], [2.121320343559643, -2.121320343559643], [1.5587396123545605, -2.683901074764725], [0.7956093000000001, -3.0], [0.0, -3.0], [-0.7956093000000001, -3.0], [-1.5587396123545605, -2.683901074764725], [-2.121320343559643, -2.121320343559643], [-2.683901074764725, -1.5587396123545605], [-3.0, -0.7956093000000001], [-3.0, 0.0], [-3.0, 0.7956093000000001], [-2.683901074764725, 1.5587396123545605], [-2.121320343559643, 2.121320343559643], [-1.5587396123545605, 2.683901074764725], [-0.7956093000000001, 3.0], [0.0, 3.0]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]}], \"texts\": [{\"text\": \"Day\", \"position\": [0.5, -0.0772626931567329], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818179817032\"}, {\"text\": \"Fatalities\", \"position\": [-0.06474014336917562, 0.5], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -90.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818179814792\"}, {\"text\": \"Real Data vs SIR-Model in Spain\", \"position\": [0.5, 1.0220750551876379], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 12.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el13139818179538728\"}, {\"text\": \"real data\", \"position\": [0.06630824372759858, 0.9374540103016924], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818179573800\"}, {\"text\": \"SIR model\", \"position\": [0.06630824372759858, 0.8822663723325976], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818179574976\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [4.0, 0.0, 0.0], [5.0, 0.0, 0.0], [6.0, 0.0, 0.0], [7.0, 0.0, 0.0], [8.0, 0.0, 0.0], [9.0, 0.0, 0.0], [10.0, 0.0, 0.0], [11.0, 0.0, 0.0], [12.0, 0.0, 0.0], [13.0, 0.0, 0.007522456600427622], [14.0, 0.0, 0.016981596133817438], [15.0, 0.0, 0.028876024266349113], [16.0, 0.0, 0.04383271465665291], [17.0, 0.0, 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88.6352984150139], [48.0, 35.0, 111.45371794686113], [49.0, 54.0, 140.14183801107748], [50.0, 55.0, 176.2079402189566], [51.0, 133.0, 221.54693935205523], [52.0, 195.0, 278.53894982807674], [53.0, 289.0, 350.17266533447645], [54.0, 342.0, 440.1996142414704], [55.0, 533.0, 553.3267288854153], [56.0, 623.0, 695.4562892785984], [57.0, 830.0, 873.9841740461553], [58.0, 1043.0, 1098.1694541207373], [59.0, 1375.0, 1379.590606075428], [60.0, 1772.0, 1732.7058311391884], [61.0, 2311.0, 2175.5368077418975], [62.0, 2808.0, 2730.4961139393076], [63.0, 3647.0, 3425.3775911197117], [64.0, 4365.0, 4294.5246025951055], [65.0, 5138.0, 5380.181321397035]], \"data02\": [[0.03405017921146955, 0.9503311258278145]], \"data03\": [[0.01612903225806453, 0.8951434878587198], [0.051971326164874564, 0.8951434878587198]], \"data04\": [[0.01254480286738352, 0.8601913171449596], [0.15972222222222224, 0.8601913171449596], [0.16330645161290322, 0.8601913171449596], [0.16330645161290322, 0.8675496688741722], 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el131398187679730082855992533\", {\"width\": 720.0, \"height\": 360.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.25, 68.25], \"ylim\": [-269.00906606985177, 5649.190387466887], \"xdomain\": [-3.25, 68.25], \"ydomain\": [-269.00906606985177, 5649.190387466887], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": [0, 10, 20, 30, 40, 50, 60], \"tickformat\": [\"2020-01-22\", \"2020-02-01\", \"2020-02-11\", \"2020-02-21\", \"2020-03-02\", \"2020-03-12\", \"2020-03-22\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818206951016\", \"lines\": [{\"data\": 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\"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818179574416pts\", \"facecolor\": \"#0000FF\", \"edgecolor\": \"#0000FF\", \"edgewidth\": 1.0, \"alpha\": 1, \"zorder\": 1000002.0, \"markerpath\": [[[0.0, 3.0], [0.7956093000000001, 3.0], [1.5587396123545605, 2.683901074764725], [2.121320343559643, 2.121320343559643], [2.683901074764725, 1.5587396123545605], [3.0, 0.7956093000000001], [3.0, 0.0], [3.0, -0.7956093000000001], [2.683901074764725, -1.5587396123545605], [2.121320343559643, -2.121320343559643], [1.5587396123545605, -2.683901074764725], [0.7956093000000001, -3.0], [0.0, -3.0], [-0.7956093000000001, -3.0], [-1.5587396123545605, -2.683901074764725], [-2.121320343559643, -2.121320343559643], [-2.683901074764725, -1.5587396123545605], [-3.0, -0.7956093000000001], [-3.0, 0.0], [-3.0, 0.7956093000000001], [-2.683901074764725, 1.5587396123545605], [-2.121320343559643, 2.121320343559643], [-1.5587396123545605, 2.683901074764725], [-0.7956093000000001, 3.0], [0.0, 3.0]], 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0.9374540103016924], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818179573800\"}, {\"text\": \"SIR model\", \"position\": [0.06630824372759858, 0.8822663723325976], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818179574976\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [4.0, 0.0, 0.0], [5.0, 0.0, 0.0], [6.0, 0.0, 0.0], [7.0, 0.0, 0.0], [8.0, 0.0, 0.0], [9.0, 0.0, 0.0], [10.0, 0.0, 0.0], [11.0, 0.0, 0.0], [12.0, 0.0, 0.0], [13.0, 0.0, 0.007522456600427622], [14.0, 0.0, 0.016981596133817438], [15.0, 0.0, 0.028876024266349113], [16.0, 0.0, 0.04383271465665291], [17.0, 0.0, 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       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el131398187679730082855992533\", {\"width\": 720.0, \"height\": 360.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3.25, 68.25], \"ylim\": [-269.00906606985177, 5649.190387466887], \"xdomain\": [-3.25, 68.25], \"ydomain\": [-269.00906606985177, 5649.190387466887], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": [0, 10, 20, 30, 40, 50, 60], \"tickformat\": [\"2020-01-22\", \"2020-02-01\", \"2020-02-11\", \"2020-02-21\", \"2020-03-02\", \"2020-03-12\", \"2020-03-22\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el13139818206951016\", \"lines\": 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\"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el13139818179574416pts\", \"facecolor\": \"#0000FF\", \"edgecolor\": \"#0000FF\", \"edgewidth\": 1.0, \"alpha\": 1, \"zorder\": 1000002.0, \"markerpath\": [[[0.0, 3.0], [0.7956093000000001, 3.0], [1.5587396123545605, 2.683901074764725], [2.121320343559643, 2.121320343559643], [2.683901074764725, 1.5587396123545605], [3.0, 0.7956093000000001], [3.0, 0.0], [3.0, -0.7956093000000001], [2.683901074764725, -1.5587396123545605], [2.121320343559643, -2.121320343559643], [1.5587396123545605, -2.683901074764725], [0.7956093000000001, -3.0], [0.0, -3.0], [-0.7956093000000001, -3.0], [-1.5587396123545605, -2.683901074764725], [-2.121320343559643, -2.121320343559643], [-2.683901074764725, -1.5587396123545605], [-3.0, -0.7956093000000001], [-3.0, 0.0], [-3.0, 0.7956093000000001], [-2.683901074764725, 1.5587396123545605], [-2.121320343559643, 2.121320343559643], [-1.5587396123545605, 2.683901074764725], [-0.7956093000000001, 3.0], 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[0.06630824372759858, 0.9374540103016924], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818179573800\"}, {\"text\": \"SIR model\", \"position\": [0.06630824372759858, 0.8822663723325976], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el13139818179574976\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [4.0, 0.0, 0.0], [5.0, 0.0, 0.0], [6.0, 0.0, 0.0], [7.0, 0.0, 0.0], [8.0, 0.0, 0.0], [9.0, 0.0, 0.0], [10.0, 0.0, 0.0], [11.0, 0.0, 0.0], [12.0, 0.0, 0.0], [13.0, 0.0, 0.007522456600427622], [14.0, 0.0, 0.016981596133817438], [15.0, 0.0, 0.028876024266349113], [16.0, 0.0, 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[0.16330645161290322, 0.8675496688741722], [0.16330645161290322, 0.9742457689477557], [0.16330645161290322, 0.9816041206769683], [0.15972222222222224, 0.9816041206769683], [0.01254480286738352, 0.9816041206769683], [0.00896057347670251, 0.9816041206769683], [0.00896057347670251, 0.9742457689477557], [0.00896057347670251, 0.8675496688741722], [0.00896057347670251, 0.8601913171449596], [0.01254480286738352, 0.8601913171449596]]}, \"id\": \"el13139818767973008\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result, _ = fit_SIR(\"Spain\", \"2020-03-15\")\n",
    "print(result.fit_report())\n",
    "fitted_plot(result, \"Spain\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# //TODO: \n",
    "1. use fitted SIR-Models to predict for all countries\n",
    "2. use ML to predict"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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