{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cs224 \n",
      "last updated: 2020-04-10 \n",
      "\n",
      "CPython 3.6.10\n",
      "IPython 7.13.0\n",
      "\n",
      "numpy 1.18.1\n",
      "xarray 0.15.0\n",
      "scipy 1.4.1\n",
      "pandas 1.0.2\n",
      "sklearn 0.22.1\n",
      "matplotlib 3.1.3\n",
      "seaborn 0.10.0\n",
      "pymc3 3.8\n",
      "lifelines 0.24.2\n",
      "rpy2 3.2.6\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'cs224' -u -d -v -p numpy,xarray,scipy,pandas,sklearn,matplotlib,seaborn,pymc3,lifelines,rpy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np, scipy, scipy.stats as stats, scipy.special, scipy.misc, pandas as pd, matplotlib.pyplot as plt, seaborn as sns, xarray as xr\n",
    "import matplotlib as mpl\n",
    "\n",
    "import pymc3 as pm\n",
    "\n",
    "import theano as thno\n",
    "import theano.tensor as T\n",
    "\n",
    "import datetime, time, math\n",
    "from dateutil import relativedelta\n",
    "\n",
    "from collections import OrderedDict\n",
    "\n",
    "SEED = 41\n",
    "np.random.seed(SEED)\n",
    "\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "# pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "np.set_printoptions(edgeitems=10)\n",
    "np.set_printoptions(linewidth=1000)\n",
    "np.set_printoptions(suppress=True)\n",
    "np.core.arrayprint._line_width = 180\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:70% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "\n",
    "from IPython.display import display_html\n",
    "def display_side_by_side(*args):\n",
    "    html_str=''\n",
    "    for df in args:\n",
    "        if type(df) == np.ndarray:\n",
    "            df = pd.DataFrame(df)\n",
    "        html_str+=df.to_html()\n",
    "    html_str = html_str.replace('table','table style=\"display:inline\"')\n",
    "    # print(html_str)\n",
    "    display_html(html_str,raw=True)\n",
    "\n",
    "CSS = \"\"\"\n",
    ".output {\n",
    "    flex-direction: row;\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "def display_graphs_side_by_side(*args):\n",
    "    html_str='<table><tr>'\n",
    "    for g in args:\n",
    "        html_str += '<td>'\n",
    "        html_str += g._repr_svg_()\n",
    "        html_str += '</td>'\n",
    "    html_str += '</tr></table>'\n",
    "    display_html(html_str,raw=True)\n",
    "    \n",
    "\n",
    "display(HTML(\"<style>.container { width:70% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 1\n",
    "%aimport covid19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Estimates of the severity of coronavirus disease 2019: a model-based analysis](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext)\n",
    "    * duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2)\n",
    "    * to hospital discharge to be 24·7 days (22·9–28·1).\n",
    "* [Estimating SARS-COV-2 infections](https://observablehq.com/@danyx/estimating-sars-cov-2-infections)\n",
    "    * it takes on average 23 days from infection to death.\n",
    "    * ~5 days from infection to symptoms and ~18 days from symptoms to death so we arrive at a default value of 23 days from infection to death\n",
    "* [Characteristics of COVID-19 patients dying in Italy Report based on available data on March 20th, 2020](https://www.epicentro.iss.it/coronavirus/bollettino/Report-COVID-2019_20_marzo_eng.pdf)\n",
    "    * Median number of days between onset of symptoms to death: 8\n",
    "    * Median number of days between onset of symptoms to hospitalization: 4\n",
    "* [Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30566-3/fulltext)\n",
    "    * Time from illness onset to hospital admission, days\t11·0 (8·0–14·0)\n",
    "* [Coronavirus: how quickly do COVID-19 symptoms develop and how long do they last?](https://patient.info/news-and-features/coronavirus-how-quickly-do-covid-19-symptoms-develop-and-how-long-do-they-last)\n",
    "    * ~5 days from infection to symptoms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Gamma distribution](https://en.wikipedia.org/wiki/Gamma_distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "duration_onset_death_mean = 17.8\n",
    "duration_onset_death_02_5_pc = 16.9\n",
    "duration_onset_death_97_5_pc = 19.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_measure(in_array):\n",
    "    loc, k, theta = in_array[0], in_array[1], in_array[2]\n",
    "    dist = stats.gamma(k, loc=loc, scale=theta)\n",
    "    mean = float(dist.stats('m'))\n",
    "    q_02_5 = dist.ppf(0.025)\n",
    "    q_97_5 = dist.ppf(0.975)\n",
    "    r = (duration_onset_death_mean - mean)**2 + (duration_onset_death_02_5_pc - q_02_5)**2 + (duration_onset_death_97_5_pc - q_97_5)**2\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gamma_loc   = 2.0\n",
    "gamma_k     = 3.0\n",
    "gamme_theta = 3.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "236.40274873031794"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = fit_measure(np.array([gamma_loc, gamma_k, gamme_theta]))\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.893998550302967 25.668385368738527 0.11661697412505936\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       " final_simplex: (array([[14.89399855, 25.66838537,  0.11661697],\n",
       "       [14.88129625, 25.6250824 ,  0.11762834],\n",
       "       [14.8644492 , 25.67040316,  0.1177947 ],\n",
       "       [14.92873074, 25.7383073 ,  0.11524772]]), array([0.01317712, 0.01323173, 0.01338485, 0.0134103 ]))\n",
       "           fun: 0.01317711608372878\n",
       "       message: 'Optimization terminated successfully.'\n",
       "          nfev: 98\n",
       "           nit: 54\n",
       "        status: 0\n",
       "       success: True\n",
       "             x: array([14.89399855, 25.66838537,  0.11661697])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = scipy.optimize.minimize(fit_measure, np.array([gamma_loc, gamma_k, gamme_theta]), method='Nelder-Mead', tol=0.1)\n",
    "gamma_loc   = r.x[0]\n",
    "gamma_k     = r.x[1]\n",
    "gamme_theta = r.x[2]\n",
    "print(gamma_loc, gamma_k, gamme_theta)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17.8873679826812, 16.843433590738254, 19.151583132802926)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist = stats.gamma(gamma_k, loc=gamma_loc, scale=gamme_theta)\n",
    "gamma_mean = float(dist.stats('m'))\n",
    "gamma_q_02_5 = dist.ppf(0.025)\n",
    "gamma_q_97_5 = dist.ppf(0.975)\n",
    "gamma_mean, gamma_q_02_5, gamma_q_97_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f42bdac52b0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0.0,30.0,1000)\n",
    "y = stats.gamma(gamma_k, loc=gamma_loc, scale=gamme_theta).pdf(x)\n",
    "fig=plt.figure(figsize=(14, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(111)\n",
    "ax.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3.31, 3.18, 3.44, 3.26, 9.696409634308136)\n"
     ]
    },
    {
     "data": {
      "image/png": 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oIiQBAAAoIiQBAAAoIiQBAAAoIiQBAAAoIiQBAAAoIiQBAAAoIiQBAAAoIiQBAAAoIiQBAAAoIiQBAAAo0nBIPvfcc5k5c2Y6OjoyefLkXHPNNS+69u67785RRx2V8ePH57jjjsv999/fJ5sFAACg/zUcknPmzEmtVsvixYtz+eWXZ968efnJT37Sa93TTz+ds846K6effnruueeeHHrooTnrrLOyYcOGPt04AAAA/aOhkOzq6srChQtz7rnnZujQoRk7dmymTZuWG2+8sdfaRYsWpa2tLUcffXQGDx6cGTNmZM2aNbnnnnv6fPMAAABsfYMaWbRixYokyahRo3qOjRkzJldddVWvtZ2dnRkzZkzPxy0tLWlvb09nZ2cmTZrU0Ka6u7uTJGvWrEmtVmvoOlvTxv2tXr06zc1eZloFZlZN5lY9ZlZN5lY9ZlZN5lZN2+Lc1q5dm+T/7vvmNBSSXV1dGTJkyCbHhg0bljVr1mx27U477dTQ2hezbt26JMnKlSsbvk5/eOCBB/p7CxQys2oyt+oxs2oyt+oxs2oyt2raFue2bt26DB06dLOXNRSSra2tvUJw1apVveJy49rVq1c3tPbF7LTTTmlra8trXvOabab6AQAABoLu7u6sW7eu1wnCP9ZQSLa1tSVJHnzwwYwcOTJJsnz58rS3t/daO3r06Fx//fWbbKKzszNnnHFGwxsfNGhQdt1114bXAwAA0Hde7EzkRg2d7mttbc3UqVMzd+7crF69OsuXL8/8+fMzffr0XmsPP/zwPPzww7n11luzfv36XHnllRkyZEgmTpz4590DAAAABpSGnzc6e/bsJMnkyZMzY8aMnHPOOT0/PKejoyNLlixJkgwfPjxf/epXc+mll2bffffNokWLcskll2TQoIZOfgIAADDANdXr9Xp/bwIAAIDq8JNsAAAAKCIkAQAAKCIkAQAAKCIkAQAAKCIkAQAAKCIkAQAAKCIkCzz33HOZOXNmOjo6Mnny5FxzzTX9vSU24+qrr8706dOz11575bzzztvkss7Ozhx33HEZP358jjrqqJ73P6V/rV+/PrNmzcqUKVPS0dGRd77znbnlllt6Lje3gemf/umfMnny5EyYMFUPXvUAAAtzSURBVCFTpkzJZZdd1nOZmQ18Tz/9dPbff/8cd9xxPcfMbWC64IILstdee6Wjo6Pn1+OPP95z+RNPPJFTTz01++yzTw477LB8//vf78fd8sduv/32HHXUUdlnn31yyCGH9MzGY21g+uPHWEdHR8aOHZszzzyz53Jz+xN1GvbRj360/uEPf7i+atWq+i9/+cv6fvvtV7/zzjv7e1v8idtvv72+aNGi+qc+9an6ueee23N8/fr19SlTptQvv/zy+rp16+oLFiyoT5w4sf7MM8/0426p1+v1NWvW1L/85S/XV65cWa/VavV77rmnPmHChPq9995rbgPY/fffX3/++efr9Xq9/sQTT9SPPPLI+m233WZmFfHxj3+8/v73v7/+3ve+t16v+ztyIPv4xz9e/8IXvvCilx9//PH1OXPm1J9//vn6j370o/o+++xTf+ihh7biDtmcH//4x/W3v/3t9Xvuuadeq9XqTz31VH3lypUeaxWxYcOG+oEHHlhfsGBBvV73d+TmOCPZoK6urixcuDDnnntuhg4dmrFjx2batGm58cYb+3tr/Ikjjjgihx12WIYPH77J8bvvvjtr167NjBkzMnjw4Lz73e/Om970Jt+5HQBaW1szc+bM7LHHHmlubs6+++6bCRMmZOnSpeY2gI0aNSrbb799kqRer6epqSmPPPKImVXAXXfdlZUrV+aYY47pOWZu1bRixYosW7YsM2fOzPbbb5+/+Zu/yYEHHpibbrqpv7e2zZs3b14+/OEPZ999901zc3N23XXX7LHHHh5rFbF48eJ0dXVl6tSpSfwduTlCskErVqxI8od/OG00ZsyY3H///f20I0rdf//9GT16dJqb/++PvRkOTF1dXfnFL36R9vZ2cxvg/uVf/iX77LNPDj744HR1deXoo482swFu/fr1+fSnP53Zs2enqamp57i5DWzXXXdd9ttvvxx99NG54YYbeo7ff//9eeMb35hhw4b1HDO3/ler1bJs2bI8/fTTOfzww3PggQfm4x//eJ599lmPtYqYP39+3vnOd/Z8w9TcehOSDerq6sqQIUM2OTZs2LCsWbOmn3ZEqTVr1mTHHXfc5JgZDjz1ej2zZs3KuHHjcuCBB5rbAPfRj340S5cuzfXXX593vetdPbMxs4Hr8ssvz4EHHpg999xzk+PmNnCddNJJWbhwYe68887MmjUrX/jCF3L77bcn+cPchg4dusl6c+t/Tz31VF544YXcfvvtufrqq3Pbbbfl97//fT7zmc94rFXA73//+/zgBz/Iscce23PM3HoTkg1qbW3t9Qdl1apVveKSgWvIkCFZvXr1JsfMcGCp1+uZPXt2nnjiiXzpS19KU1OTuVVAU1NTxo0bl8GDB+fiiy82swFsxYoVuemmm3L22Wf3uszcBq6/+qu/yi677JKWlpbsv//+OfHEE7Nw4cIkf5ibf58MPDvssEOSP3wT4HWve12GDRuWM888M3fccYfHWgXccsstectb3pLx48f3HDO33oRkg9ra2pIkDz74YM+x5cuXp729vZ92RKn29vZ0dnamu7u759ivf/1rMxwg6vV6PvWpT+VXv/pVrrzyyrS2tiYxtyqp1Wp55JFHzGwAu/fee/Pkk09mypQp2X///fPpT386v/zlL7P//vvnTW96k7lVRHNzc+r1epI//B352GOPZdWqVT2X+/dJ/xs2bFje8IY3bPL08Y38HTnwzZ8/P9OnT9/kmLn1JiQb1NramqlTp2bu3LlZvXp1li9fvtk/ZPS/DRs2ZN26ddmwYUO6u7uzbt26vPDCC9lvv/0yePDgfOMb38j69etzyy235NFHH83hhx/e31smyZw5c/Kzn/0sX//61zd5mpa5DUyrVq3KggULsnr16nR3d+enP/1pvv3tb+dtb3ubmQ1g73jHO7Jo0aLcdNNNuemmmzJz5syMHj06N910Uw466CBzG6D+/d//veextmTJklx99dU9c2lra8tee+2VuXPnZu3atbnzzjuzePHivPvd7+7nXfOe97wn3/rWt/LUU09l9erVueKKKzJlyhR/Rw5wv/zlL/PAAw/0egyZW29N9Y3f0uJlPffcc7nwwguzePHiDBkyJB/60Idy4okn9ve2+BNf+cpXcvHFF29ybNq0afnsZz+b++67LxdeeGHuu+++7LHHHvnkJz+ZiRMn9tNO2eixxx7LlClTMnjw4AwaNKjn+BlnnJEzzzzT3Aag1atX58Mf/nB+9atfZcOGDXnd616X6dOn54Mf/GCamprMrCLmz5+fa6+9Ntddd12SmNsAdeKJJ+a+++5LrVbLG9/4xrz//e/PCSec0HP5E088kX/8x3/Mvffem9e+9rU5//zze37SJP1nw4YN+exnP5ubb745LS0tOfjggzNr1qwMHTrUY20A+/SnP50nnngil1xySa/LzG1TQhIAAIAintoKAABAESEJAABAESEJAABAESEJAABAESEJAABAESEJAABAESEJwDbtK1/5yibvybclzJ8/P29/+9t7Pr7gggvyD//wD1v0cwLAljTo5ZcAwKvHCSeckLe97W05++yzkySnnnpqTjrppK26h1mzZm3VzwcAfU1IArBNGzJkyFb/nDvuuONW/5wA0Jc8tRWAbcYFF1yQe++9NxdffHH23HPPTJkypddTW0866aR87nOfy4UXXpiOjo5MmTIld9xxR37zm9/k5JNPzj777JPjjz8+jz322Ca3/c1vfjOHHnpoxo8fn2OPPTZ33XXXS+7jj5/aOmXKlHzjG9/IOeeck3322SfveMc7cuedd25ynR//+MeZPn16xo0bl6lTp+aaa67po68KAJQTkgBsM2bNmpVx48bl1FNPzY9+9KPccMMNm1133XXXpb29Pd/97ndz0EEH5fzzz8+sWbPygQ98IDfeeGOS5LOf/WzP+htuuCHf/OY3M3v27Nx666055phjcvrpp+fRRx9teG9XXnllpkyZkgULFmTffffNxz72saxfvz5J8tBDD+UjH/lITjjhhHzve9/LBRdckIsvvji33XbbK/hqAMCfT0gCsM3YcccdM2jQoLS2tma33XbLLrvsstl1EyZMyAc+8IG0tbXlrLPOyjPPPJO3ve1tOeSQQzJy5MicdNJJufvuu3vWX3rppZk1a1be/va3Z4899shJJ52Uv/7rv87NN9/c8N6OOOKIHHPMMWlra8vZZ5+d//3f/82KFSuSJFdccUXe97735b3vfW/22GOPHHLIIfnABz6Q66677hV9PQDgz+U1kgDwJ0aPHt3z+9e+9rVJklGjRvUc23XXXfPMM8+kVqtl7dq1efTRR3PeeeelqampZ8369evzute97s/6nLvttluS5He/+12SpLOzM52dnbn22mt71mzYsCG777574T0DgL4hJAHgTwwa9H//e9wYh9ttt12vY/V6Pc8//3yS5Itf/GLa29s3uZ2SH+TzYrefJF1dXTnllFNy7LHHbnKdlpaWhm8fAPqSkARgmzJo0KDUarU+u71dd901u+22W5544okcdthhfXa7f2zMmDF5+OGH85a3vGWL3D4AlBKSAGxTRowYkZ/97Gd58skns/3227/i22tqasoZZ5yRuXPnprW1NRMnTsyzzz6bO++8M3vvvXcmTZr0ij/HaaedluOPPz5f+tKX8q53vSv1ej3Lli3L888/nxNPPPEV3z4AlPLDdgDYppx66ql55plncuihh2batGl9cpsnnXRSPvaxj+XKK6/MkUcemQ996EP5+c9/XvQayZey11575V//9V9zzz33ZPr06fm7v/u7zJ8/P29605v65PYBoFRTfeMLMAAAAKABzkgCAABQREgCAABQREgCAABQREgCAABQREgCAABQREgCAABQREgCAABQREgCAABQREgCAABQREgCAABQ5P8H9keWbjnwhXcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "germany_mortality_analysis = covid19.MortalityAnalysis('Germany', first_date=pd.to_datetime('2020-03-09'), init_add=1000)\n",
    "germany_mortality_analysis.fit()\n",
    "print(germany_mortality_analysis.death_rate())\n",
    "germany_mortality_analysis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>95950</td>\n",
       "      <td>26469</td>\n",
       "      <td>1452</td>\n",
       "      <td>4636</td>\n",
       "      <td>325</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>98945</td>\n",
       "      <td>36081</td>\n",
       "      <td>1578</td>\n",
       "      <td>2995</td>\n",
       "      <td>9612</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07</th>\n",
       "      <td>103036</td>\n",
       "      <td>38287</td>\n",
       "      <td>1814</td>\n",
       "      <td>4091</td>\n",
       "      <td>2206</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08</th>\n",
       "      <td>108193</td>\n",
       "      <td>43656</td>\n",
       "      <td>2070</td>\n",
       "      <td>5157</td>\n",
       "      <td>5369</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-09</th>\n",
       "      <td>112638</td>\n",
       "      <td>52407</td>\n",
       "      <td>2312</td>\n",
       "      <td>4445</td>\n",
       "      <td>8751</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "2020-04-05      95950      26469   1452           4636            325        133\n",
       "2020-04-06      98945      36081   1578           2995           9612        126\n",
       "2020-04-07     103036      38287   1814           4091           2206        236\n",
       "2020-04-08     108193      43656   2070           5157           5369        256\n",
       "2020-04-09     112638      52407   2312           4445           8751        242"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "germany_mortality_analysis.df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "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>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3761.0</td>\n",
       "      <td>2312</td>\n",
       "      <td>1449.0</td>\n",
       "      <td>3410.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8525.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0          3761.0         2312       1449.0            3410.0                    162.0          21                        8525.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "germany_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f42bd096828>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3040x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(38, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(111)\n",
    "ldf = germany_mortality_analysis.df[['confirmed']]\n",
    "ldf = ldf[ldf.index >= pd.to_datetime('2020-02-25')]\n",
    "ldf.plot(ax=ax, color=['orange'])\n",
    "ldf = germany_mortality_analysis.df[['death']]\n",
    "ldf = ldf[ldf.death > 0]\n",
    "ldf.plot(ax=ax, color=['blue'])\n",
    "germany_mortality_analysis.prediction_fit_df[[germany_mortality_analysis.prediction_fit_label_fit]].plot(ax=ax, color=['green'])\n",
    "\n",
    "ldf = germany_mortality_analysis.df[['death']]\n",
    "ldf = ldf[ldf.death > 0].copy()\n",
    "ldf = ldf / float(germany_mortality_analysis.death_rate()[0] / 100.0)\n",
    "ldf.columns = ['shifted_death']\n",
    "ldf.index = ldf.index + pd.Timedelta(days=-int(np.round(germany_mortality_analysis.delay_between_new_cases_and_death_timeshift, 0)))\n",
    "ldf.plot(ax=ax, color=['darkblue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-09</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-10</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-11</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-12</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-13</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\">"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ldf1 = germany_mortality_analysis.df[['confirmed']]\n",
    "ldf1 = ldf1[ldf1.index >= pd.to_datetime('2020-02-25')]\n",
    "\n",
    "ldf2 = germany_mortality_analysis.df[['death']]\n",
    "ldf2 = ldf2[ldf2.death > 0]\n",
    "\n",
    "display_side_by_side(ldf1.head(), ldf2.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('13 days 00:00:00')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldf2.iloc[0].name - ldf1.iloc[0].name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17.8873679826812, 16.843433590738254, 19.151583132802926)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gamma_mean, gamma_q_02_5, gamma_q_97_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14.893998550302967, 25.668385368738527, 0.11661697412505936)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gamma_loc, gamma_k, gamme_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.8873679826812015"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gamma_loc_shift = gamma_mean - 13\n",
    "gamma_loc_shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.887367982681202"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gamma_loc_shift = gamma_mean - 8\n",
    "gamma_loc_shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.993369432378234"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gamma_mean - gamma_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# timeshift = loc + gamma_mean - gamma_loc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(0.0, -1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03232404781859826 9.370633408721732\n",
      "(3.22, 3.09, 3.35, 3.26, 9.696409634308136)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_name, first_date, init_add, restriction_start_date = 'Germany', pd.to_datetime('2020-03-09'), 0, datetime.datetime(2020, 3, 22)\n",
    "cfr_estimate, timeshift = covid19.calculate_delay_between_new_cases_and_death(country_name, first_date=first_date, init_add=init_add)\n",
    "print(cfr_estimate, timeshift)\n",
    "loc = max(timeshift - (gamma_mean - gamma_loc), 0.0)\n",
    "germany_mortality_analysis2 = covid19.MortalityAnalysis(country_name, first_date=first_date, init_add=1000, gamma_distribution_parameters=dict(loc=loc, k=gamma_k, theta=gamme_theta))\n",
    "germany_mortality_analysis2.fit()\n",
    "print(germany_mortality_analysis2.death_rate())\n",
    "germany_mortality_analysis2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-29</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-02</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>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>2020-04-02</th>\n",
       "      <td>80450</td>\n",
       "      <td>22440</td>\n",
       "      <td>1002</td>\n",
       "      <td>6494</td>\n",
       "      <td>3265</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>86436</td>\n",
       "      <td>24981</td>\n",
       "      <td>1153</td>\n",
       "      <td>5986</td>\n",
       "      <td>2541</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>92314</td>\n",
       "      <td>26144</td>\n",
       "      <td>1319</td>\n",
       "      <td>5878</td>\n",
       "      <td>1163</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>96950</td>\n",
       "      <td>26469</td>\n",
       "      <td>1452</td>\n",
       "      <td>4636</td>\n",
       "      <td>325</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>99945</td>\n",
       "      <td>36081</td>\n",
       "      <td>1578</td>\n",
       "      <td>2995</td>\n",
       "      <td>9612</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "2020-01-29          4          0      0              4              0          0\n",
       "2020-01-30          8          0      0              4              0          0\n",
       "2020-01-31         12          0      0              4              0          0\n",
       "2020-02-01         18          0      0              6              0          0\n",
       "2020-02-02         22          0      0              4              0          0\n",
       "...               ...        ...    ...            ...            ...        ...\n",
       "2020-04-02      80450      22440   1002           6494           3265        135\n",
       "2020-04-03      86436      24981   1153           5986           2541        151\n",
       "2020-04-04      92314      26144   1319           5878           1163        166\n",
       "2020-04-05      96950      26469   1452           4636            325        133\n",
       "2020-04-06      99945      36081   1578           2995           9612        126\n",
       "\n",
       "[69 rows x 6 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldf = germany_mortality_analysis2.prepend_df\n",
    "ldf = ldf[ldf.index <= pd.to_datetime('2020-04-06')].copy()\n",
    "ldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14.893998550302967, 25.668385368738527, 0.11661697412505936)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gamma_loc, gamma_k, gamme_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>observed_death</th>\n",
       "      <th>day_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>False</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>False</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>False</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>False</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>False</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99940</th>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99941</th>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99942</th>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99943</th>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99944</th>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99945 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      start_date   end_date  observed_death  day_count\n",
       "0     2020-01-29 2020-04-07           False         69\n",
       "1     2020-01-29 2020-04-07           False         69\n",
       "2     2020-01-29 2020-04-07           False         69\n",
       "3     2020-01-29 2020-04-07           False         69\n",
       "4     2020-01-30 2020-04-07           False         68\n",
       "...          ...        ...             ...        ...\n",
       "99940 2020-04-06 2020-04-07           False          1\n",
       "99941 2020-04-06 2020-04-07           False          1\n",
       "99942 2020-04-06 2020-04-07           False          1\n",
       "99943 2020-04-06 2020-04-07           False          1\n",
       "99944 2020-04-06 2020-04-07           False          1\n",
       "\n",
       "[99945 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lldf = covid19.generate_life_lines(ldf, gamma_distribution_parameters=dict(loc=gamma_loc-5, k=gamma_k, theta=gamme_theta))\n",
    "lldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1578"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lldf.observed_death.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lifelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.KaplanMeierFitter:\"kmf\", fitted with 99945 total observations, 98367 right-censored observations>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf = lifelines.KaplanMeierFitter()\n",
    "kmf.fit(lldf.day_count, lldf.observed_death, label='kmf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f42b8ae2518>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "fig = plt.figure(figsize=(14, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(111)\n",
    "ax.set_ylim([0.0, 1.0])\n",
    "\n",
    "kmf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.04, 3.85, 4.24)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = np.round(float(1 - kmf.survival_function_.iloc[-1].values) * 100, 2)\n",
    "lower = np.round(float(1 - kmf.confidence_interval_.iloc[-1, 1]) * 100, 2)\n",
    "upper = np.round(float(1 - kmf.confidence_interval_.iloc[-1, 0]) * 100, 2)\n",
    "(mean, lower, upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>observed_death</th>\n",
       "      <th>day_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37564</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37691</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37935</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38117</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38176</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      start_date   end_date  observed_death  day_count\n",
       "37564 2020-03-25 2020-04-06            True         12\n",
       "37691 2020-03-25 2020-04-06            True         12\n",
       "37935 2020-03-25 2020-04-06            True         12\n",
       "38117 2020-03-25 2020-04-06            True         12\n",
       "38176 2020-03-25 2020-04-06            True         12"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lldf[lldf.observed_death].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x7f42b3b12ba8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3040x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lldf = covid19.generate_life_lines(ldf, gamma_distribution_parameters=dict(loc=gamma_loc-0, k=gamma_k, theta=gamme_theta))\n",
    "fig = plt.figure(figsize=(38, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(111)\n",
    "lldf_alive = lldf[~lldf.observed_death]\n",
    "lldf_dead  = lldf[lldf.observed_death]\n",
    "ax.vlines(lldf_alive.index, ymin=0, ymax=lldf_alive.day_count, color='orange')\n",
    "ax.vlines(lldf_dead.index, ymin=0, ymax=lldf_dead.day_count, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x7f42b0864ba8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3040x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lldf = covid19.generate_life_lines(ldf, gamma_distribution_parameters=dict(loc=gamma_loc-5, k=gamma_k, theta=gamme_theta))\n",
    "fig = plt.figure(figsize=(38, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(111)\n",
    "lldf_alive = lldf[~lldf.observed_death]\n",
    "lldf_dead  = lldf[lldf.observed_death]\n",
    "ax.vlines(lldf_alive.index, ymin=0, ymax=lldf_alive.day_count, color='orange')\n",
    "ax.vlines(lldf_dead.index, ymin=0, ymax=lldf_dead.day_count, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2.6\n"
     ]
    }
   ],
   "source": [
    "import rpy2\n",
    "print(rpy2.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$version.string\n",
      "[1] \"R version 3.6.1 (2019-07-05)\"\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import rpy2.robjects.packages as rpackages\n",
    "baseR = rpackages.importr('base')\n",
    "print(baseR.R_Version().rx('version.string'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from rpy2.rinterface import R_VERSION_BUILD\n",
    "# print(R_VERSION_BUILD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/local/cs/local/install/anaconda3-5.3.1-Linux-x86_64/envs/py36ds/lib/python3.6/site-packages/rpy2/robjects/pandas2ri.py:14: FutureWarning: pandas.core.index is deprecated and will be removed in a future version.  The public classes are available in the top-level namespace.\n",
      "  from pandas.core.index import Index as PandasIndex\n",
      "/home/local/cs/local/install/anaconda3-5.3.1-Linux-x86_64/envs/py36ds/lib/python3.6/site-packages/rpy2/robjects/pandas2ri.py:34: UserWarning: pandas >= 1.0 is not supported.\n",
      "  warnings.warn('pandas >= 1.0 is not supported.')\n",
      "/home/local/cs/local/install/anaconda3-5.3.1-Linux-x86_64/envs/py36ds/lib/python3.6/site-packages/rpy2/robjects/lib/ggplot2.py:72: UserWarning: This was designed againt ggplot2 version 3.2.1 but you have 3.3.0\n",
      "  'have %s' % (TARGET_VERSION, ggplot2.__version__))\n",
      "/home/local/cs/local/install/anaconda3-5.3.1-Linux-x86_64/envs/py36ds/lib/python3.6/site-packages/rpy2/robjects/vectors.py:927: UserWarning: R object inheriting from \"POSIXct\" but without attribute \"tzone\".\n",
      "  warnings.warn('R object inheriting from \"POSIXct\" but without '\n"
     ]
    }
   ],
   "source": [
    "import IPython.display\n",
    "import rpy2, rpy2.robjects, rpy2.robjects.pandas2ri, rpy2.rinterface, rpy2.robjects.packages, rpy2.interactive, rpy2.robjects.lib.ggplot2, rpy2.robjects.lib.grdevices\n",
    "rpy2.robjects.pandas2ri.activate()\n",
    "\n",
    "from rpy2.robjects.packages import importr\n",
    "# import R's \"base\" package\n",
    "base = importr('base')\n",
    "\n",
    "# import rpy2's package module\n",
    "import rpy2.robjects.packages as rpackages\n",
    "\n",
    "# import R's utility package\n",
    "utils = rpackages.importr('utils')\n",
    "\n",
    "# select a mirror for R packages\n",
    "utils.chooseCRANmirror(ind=1) # select the first mirror in the list\n",
    "\n",
    "# R package names\n",
    "packnames = ('LexisPlotR',)\n",
    "\n",
    "# R vector of strings\n",
    "from rpy2.robjects.vectors import StrVector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rpy2.robjects.packages.Package as a <module 'LexisPlotR'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grdevices = rpy2.robjects.packages.importr('grDevices')\n",
    "# Selectively install what needs to be install.\n",
    "# We are fancy, just because we can.\n",
    "names_to_install = [x for x in packnames if not rpackages.isinstalled(x)]\n",
    "if len(names_to_install) > 0:\n",
    "    utils.install_packages(StrVector(names_to_install))\n",
    "\n",
    "lexis = importr('LexisPlotR')\n",
    "lexis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "lexis_grid = rpy2.robjects.r['lexis.grid']\n",
    "lexis_lifeline = rpy2.robjects.r['lexis.lifeline']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_lexis(df_lifelines_individual):\n",
    "    mylexis = lexis_grid(year_start = 2020, year_end = 2021, age_start = 0, age_end = 1) #  lwd = 0.1\n",
    "\n",
    "    alpha = 1.0\n",
    "    ix_present = ~df_lifelines_individual.observed_death\n",
    "    ix_lost    = df_lifelines_individual.observed_death\n",
    "    mylexis = lexis_lifeline(lg = mylexis , entry = df_lifelines_individual['start_date'][ix_present], exit = df_lifelines_individual['end_date'][ix_present], colour = \"orange\", alpha = alpha, lwd = 0.4)\n",
    "    mylexis = lexis_lifeline(lg = mylexis , entry = df_lifelines_individual['start_date'][ix_lost]   , exit = df_lifelines_individual['end_date'][ix_lost]   , colour = \"blue\"  , alpha = alpha, lwd = 0.4, lineends = True)\n",
    "\n",
    "    with rpy2.robjects.lib.grdevices.render_to_bytesio(grdevices.png, width=1.5*1024, height=1.5*896, res=90) as img:\n",
    "        rpy2.robjects.r.print(mylexis)   \n",
    "    IPython.display.display(IPython.display.Image(data=img.getvalue(), format='png', embed=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lldf = covid19.generate_life_lines(ldf, gamma_distribution_parameters=dict(loc=gamma_loc-0, k=gamma_k, theta=gamme_theta))\n",
    "plot_lexis(lldf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lldf = covid19.generate_life_lines(ldf, gamma_distribution_parameters=dict(loc=gamma_loc-5, k=gamma_k, theta=gamme_theta))\n",
    "plot_lexis(lldf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigmoid+linear 2020-05-18 00:00:00 [32756.31569272     0.24542165    -3.72501745 19638.61483769] 0.04482483253939229\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2560x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_name, first_date, init_add, restriction_start_date = 'Germany', pd.to_datetime('2020-03-09'), 0, datetime.datetime(2020, 3, 22)\n",
    "ldf, lpopt, lpcov, lsqdiff, lgrowthRate, idx, label = covid19.prepare_country_prediction(country_name, first_date=first_date, init_add=init_add)\n",
    "print(label, ldf.index[-1], lpopt, lgrowthRate)\n",
    "fig = plt.figure(figsize=(32,8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1,1,1)\n",
    "ldf[['confirmed', label + '_fit']].plot(ax=ax, marker=mpl.path.Path.unit_circle(), markersize=5);\n",
    "ax.axvline(restriction_start_date);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>x</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>sigmoid+linear_fit</th>\n",
       "      <th>sigmoid+linear_fit_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>282524.751559</td>\n",
       "      <td>4819.752096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-15</th>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>287344.501277</td>\n",
       "      <td>4819.749717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-16</th>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>292164.249133</td>\n",
       "      <td>4819.747856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-17</th>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>296983.995533</td>\n",
       "      <td>4819.746400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-18</th>\n",
       "      <td>71.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>301803.740793</td>\n",
       "      <td>4819.745261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               x  confirmed  sigmoid+linear_fit  sigmoid+linear_fit_diff\n",
       "2020-05-14  67.0        NaN       282524.751559              4819.752096\n",
       "2020-05-15  68.0        NaN       287344.501277              4819.749717\n",
       "2020-05-16  69.0        NaN       292164.249133              4819.747856\n",
       "2020-05-17  70.0        NaN       296983.995533              4819.746400\n",
       "2020-05-18  71.0        NaN       301803.740793              4819.745261"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.03232405, 9.37063341])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_name, first_date, init_add, restriction_start_date = 'Germany', pd.to_datetime('2020-03-09'), 0, datetime.datetime(2020, 3, 22)\n",
    "covid19.calculate_delay_between_new_cases_and_death(country_name, first_date=first_date, init_add=init_add)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigmoid 2020-04-12 00:00:00 [14350.32961458     0.20569832    -2.90468878] 0.010223236574044892\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2560x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# country_name, first_date, init_add, restriction_start_date = 'Austria', pd.to_datetime('2020-02-19'), 600, datetime.datetime(2020, 3, 15)\n",
    "country_name, first_date, init_add, restriction_start_date = 'Austria', pd.to_datetime('2020-03-12'), 600, datetime.datetime(2020, 3, 15)\n",
    "# country_name, first_date, init_add, restriction_start_date = 'Austria', pd.to_datetime('2020-03-12'), 0, datetime.datetime(2020, 3, 15)\n",
    "ldf, lpopt, lpcov, lsqdiff, lgrowthRate, idx, label = covid19.prepare_country_prediction(country_name, first_date=first_date, init_add=init_add) # , new_confirmed_threshold=10.0\n",
    "print(label, ldf.index[-1], lpopt, lgrowthRate)\n",
    "fig = plt.figure(figsize=(32,8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1,1,1)\n",
    "ldf[['confirmed', label + '_fit']].plot(ax=ax, marker=mpl.path.Path.unit_circle(), markersize=5);\n",
    "ax.axvline(restriction_start_date);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigmoid 2020-05-18 00:00:00 [350.97260087   0.23105626  -5.30500305] 0.051598920068677945\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2560x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# country_name, first_date, init_add, restriction_start_date = 'Austria', pd.to_datetime('2020-02-19'), 600, datetime.datetime(2020, 3, 15)\n",
    "country_name, first_date, init_add, restriction_start_date = 'Austria', pd.to_datetime('2020-03-12'), 600, datetime.datetime(2020, 3, 15)\n",
    "# country_name, first_date, init_add, restriction_start_date = 'Austria', pd.to_datetime('2020-03-12'), 0, datetime.datetime(2020, 3, 15)\n",
    "ldf, lpopt, lpcov, lsqdiff, lgrowthRate, idx, label = covid19.prepare_country_prediction(country_name, first_date=first_date, init_add=init_add, fit_column='death', new_confirmed_threshold=0.0) # , new_confirmed_threshold=10.0\n",
    "print(label, ldf.index[-1], lpopt, lgrowthRate)\n",
    "fig = plt.figure(figsize=(32,8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1,1,1)\n",
    "ldf[['death', label + '_fit']].plot(ax=ax, marker=mpl.path.Path.unit_circle(), markersize=5);\n",
    "ax.axvline(restriction_start_date);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02566408, 9.45371412])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_name, first_date, init_add, restriction_start_date = 'Austria', pd.to_datetime('2020-03-12'), 600, datetime.datetime(2020, 3, 15)\n",
    "covid19.calculate_delay_between_new_cases_and_death(country_name, first_date=first_date, init_add=init_add)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}