{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse de la varicelle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recherche des données sur le site du [Réseau Sentinelles](http://www.sentiweb.fr/) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n",
    "data_file = \"syndrome-varicelle.csv\"\n",
    "\n",
    "import os\n",
    "import urllib.request\n",
    "if not os.path.exists(data_file):\n",
    "    urllib.request.urlretrieve(data_url, data_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Description du fichier ([source](http://www.sentiweb.fr/france/fr/?page=table&maladie=7))\n",
    "\n",
    "|Name |\tType \t| \tDescription|\n",
    "|-|-|-|\n",
    "|week |\tinteger |ISO8601 Yearweek number as numeric (year*100 + week nubmer)|\n",
    "|geo_insee|string |Identifier of the geographic area, from INSEE https://www.insee.fr| |\n",
    "|geo_name| \tstring| \t\tGeographic label of the area, corresponding to INSEE code. This label is not an id and is only provided for human reading|\n",
    "|indicator| integer |Unique identifier of the indicator, see metadata document https://www.sentiweb.fr/meta.json |\n",
    "|inc |\tinteger \t|\tEstimated incidence value for the time step, in the geographic level|\n",
    "|inc_low |\tinteger |\t\tLower bound of the estimated incidence 95% Confidence Interval|\n",
    "|inc_up |\tinteger |\t\tUpper bound of the estimated incidence 95% Confidence Interval|\n",
    "|inc100 |\tinteger |\t\tEstimated rate incidence per 100,000 inhabitants|\n",
    "|inc100_low |\tinteger |\t\tLower bound of the estimated incidence 95% Confidence Interval|\n",
    "|inc100_up| \tinteger| \t\tUpper bound of the estimated rate incidence 95% Confidence Interval|\n",
    "|Missing value| \\- | \\- |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202012</td>\n",
       "      <td>7</td>\n",
       "      <td>8639</td>\n",
       "      <td>6010</td>\n",
       "      <td>11268</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202011</td>\n",
       "      <td>7</td>\n",
       "      <td>10209</td>\n",
       "      <td>7575</td>\n",
       "      <td>12843</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202010</td>\n",
       "      <td>7</td>\n",
       "      <td>9011</td>\n",
       "      <td>6691</td>\n",
       "      <td>11331</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202009</td>\n",
       "      <td>7</td>\n",
       "      <td>13631</td>\n",
       "      <td>10544</td>\n",
       "      <td>16718</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202008</td>\n",
       "      <td>7</td>\n",
       "      <td>10424</td>\n",
       "      <td>7708</td>\n",
       "      <td>13140</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1524</th>\n",
       "      <td>199101</td>\n",
       "      <td>7</td>\n",
       "      <td>15565</td>\n",
       "      <td>10271</td>\n",
       "      <td>20859</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>199052</td>\n",
       "      <td>7</td>\n",
       "      <td>19375</td>\n",
       "      <td>13295</td>\n",
       "      <td>25455</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>199051</td>\n",
       "      <td>7</td>\n",
       "      <td>19080</td>\n",
       "      <td>13807</td>\n",
       "      <td>24353</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>43</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1527</th>\n",
       "      <td>199050</td>\n",
       "      <td>7</td>\n",
       "      <td>11079</td>\n",
       "      <td>6660</td>\n",
       "      <td>15498</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528</th>\n",
       "      <td>199049</td>\n",
       "      <td>7</td>\n",
       "      <td>1143</td>\n",
       "      <td>0</td>\n",
       "      <td>2610</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
       "0     202012          7   8639     6010   11268      13           9   \n",
       "1     202011          7  10209     7575   12843      16          12   \n",
       "2     202010          7   9011     6691   11331      14          10   \n",
       "3     202009          7  13631    10544   16718      21          16   \n",
       "4     202008          7  10424     7708   13140      16          12   \n",
       "1524  199101          7  15565    10271   20859      27          18   \n",
       "1525  199052          7  19375    13295   25455      34          23   \n",
       "1526  199051          7  19080    13807   24353      34          25   \n",
       "1527  199050          7  11079     6660   15498      20          12   \n",
       "1528  199049          7   1143        0    2610       2           0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0            17        FR   France  \n",
       "1            20        FR   France  \n",
       "2            18        FR   France  \n",
       "3            26        FR   France  \n",
       "4            20        FR   France  \n",
       "1524         36        FR   France  \n",
       "1525         45        FR   France  \n",
       "1526         43        FR   France  \n",
       "1527         28        FR   France  \n",
       "1528          5        FR   France  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(data_file, skiprows=1)\n",
    "raw_data.iloc[np.r_[0:5, -5:0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recherche de missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n",
       "Index: []"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conversion des dates du format isoweek -> period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = raw_data\n",
    "\n",
    "def convert_week(year_and_week_int):\n",
    "    year_and_week_str = str(year_and_week_int)\n",
    "    year = int(year_and_week_str[:4])\n",
    "    week = int(year_and_week_str[4:])\n",
    "    w = isoweek.Week(year, week)\n",
    "    return pd.Period(w.day(0), 'W')\n",
    "\n",
    "data['period'] = [convert_week(yw) for yw in data['week']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tri par ordre chornologique et vérification de la durée entre 2 observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_data = data.set_index('period').sort_index()\n",
    "\n",
    "periods = sorted_data.index\n",
    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
    "    delta = p2.to_timestamp() - p1.end_time\n",
    "    if delta > pd.Timedelta('1s'):\n",
    "        print(p1, p2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot des données "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data['inc'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data['inc'][-200:].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Etude de l'incidence annuelle\n",
    "\n",
    "La cyclicité des incidences de varicelle est minimum vers le mois de septembre. Cette date est donc le point de départ d'une année 'varicelle'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
    "                     for y in range(sorted_data.index[0].year,\n",
    "                                    sorted_data.index[-1].year)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verification de la durée des années 'varicelles'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-968f7301f6ee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m                         first_sept_week[1:]):\n\u001b[1;32m      5\u001b[0m     \u001b[0mone_year\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mweek1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m52\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0myear\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_sept_week[:-1],\n",
    "                        first_sept_week[1:]):\n",
    "    one_year = sorted_data['inc'][week1:week2-1]\n",
    "    assert abs(len(one_year)-52) < 2\n",
    "    yearly_incidence.append(one_year.sum())\n",
    "    year.append(week2.year)\n",
    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La première et dernière année sont incomplètes, on les retire de l'étude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
    "                     for y in range(sorted_data.index[0].year+1,\n",
    "                                    sorted_data.index[-1].year-1)]\n",
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_sept_week[:-1],\n",
    "                        first_sept_week[1:]):\n",
    "    one_year = sorted_data['inc'][week1:week2-1]\n",
    "    assert abs(len(one_year)-52) < 2\n",
    "    yearly_incidence.append(one_year.sum())\n",
    "    year.append(week2.year)\n",
    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot du nb de cas par année"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8936ad3ac8>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence.plot(style='*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table classée du nombre de cas par année"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2002    516689\n",
       "2018    542312\n",
       "2017    551041\n",
       "1996    564901\n",
       "2015    604382\n",
       "2000    617597\n",
       "2001    619041\n",
       "2012    624573\n",
       "2005    628464\n",
       "2006    632833\n",
       "2011    642368\n",
       "1993    643387\n",
       "1995    652478\n",
       "1994    661409\n",
       "1998    677775\n",
       "1997    683434\n",
       "2014    685769\n",
       "2013    698332\n",
       "2007    717352\n",
       "2008    749478\n",
       "1999    756456\n",
       "2003    758363\n",
       "2004    777388\n",
       "2016    782114\n",
       "2010    829911\n",
       "1992    832939\n",
       "2009    842373\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution du nombre de cas par année"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f89369c2390>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence.hist(xrot=20)"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}