{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incidence de la varicelle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous téléchargeons dans un premier temps les données de sur la varicelle. Les données sont importé en local.\n",
    "Nous retirons la première ligne qui est un commentaire."
   ]
  },
  {
   "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>202222</td>\n",
       "      <td>7</td>\n",
       "      <td>21286</td>\n",
       "      <td>16284</td>\n",
       "      <td>26288</td>\n",
       "      <td>32</td>\n",
       "      <td>24</td>\n",
       "      <td>40</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202221</td>\n",
       "      <td>7</td>\n",
       "      <td>19889</td>\n",
       "      <td>15896</td>\n",
       "      <td>23882</td>\n",
       "      <td>30</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202220</td>\n",
       "      <td>7</td>\n",
       "      <td>23585</td>\n",
       "      <td>19004</td>\n",
       "      <td>28166</td>\n",
       "      <td>36</td>\n",
       "      <td>29</td>\n",
       "      <td>43</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202219</td>\n",
       "      <td>7</td>\n",
       "      <td>18593</td>\n",
       "      <td>14181</td>\n",
       "      <td>23005</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202218</td>\n",
       "      <td>7</td>\n",
       "      <td>17851</td>\n",
       "      <td>13963</td>\n",
       "      <td>21739</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>33</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1639</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>1640</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>1641</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>1642</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>1643</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",
       "<p>1644 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
       "0     202222          7  21286    16284   26288      32          24   \n",
       "1     202221          7  19889    15896   23882      30          24   \n",
       "2     202220          7  23585    19004   28166      36          29   \n",
       "3     202219          7  18593    14181   23005      28          21   \n",
       "4     202218          7  17851    13963   21739      27          21   \n",
       "...      ...        ...    ...      ...     ...     ...         ...   \n",
       "1639  199101          7  15565    10271   20859      27          18   \n",
       "1640  199052          7  19375    13295   25455      34          23   \n",
       "1641  199051          7  19080    13807   24353      34          25   \n",
       "1642  199050          7  11079     6660   15498      20          12   \n",
       "1643  199049          7   1143        0    2610       2           0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0            40        FR   France  \n",
       "1            36        FR   France  \n",
       "2            43        FR   France  \n",
       "3            35        FR   France  \n",
       "4            33        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1639         36        FR   France  \n",
       "1640         45        FR   France  \n",
       "1641         43        FR   France  \n",
       "1642         28        FR   France  \n",
       "1643          5        FR   France  \n",
       "\n",
       "[1644 rows x 10 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(\"incidence-PAY-7.csv\", skiprows=1, encoding='latin1') # encodage utf-8\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous recherchons les éventuelles points manquants."
   ]
  },
  {
   "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": [
    "Il n'y a ici aucun point manquant. Nous pouvons donc passer à l'analyse sans devoir supprimer une ligne.\n",
    "\n",
    "Nous définissons ensuite une fonction pour formater les semaines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous définissons les périodes d'observation comme index de notre jeux de données.\n",
    "\n",
    "Puis les points sont triés par période dans le sens chronologique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sorted_data = raw_data.set_index('period').sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous vérifions la cohérence des données. Entre la fin d'une période et\n",
    "le début de la période qui suit, la différence temporelle doit être\n",
    "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n",
    "d'une seconde."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": [
    "Nous affichons une première fois les données pour avoir un premier rendu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='period'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data['inc'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un zoom sur les dernières années montre mieux la situation des pics au début de chaque année scolaire et l'épidémie se maintient jusqu'à la fin de l'année scolaire (début de l'été).\n",
    "La varicelle touchant principalement les enfants. Des pics moins importants sont observé pour l'année 2020 et 2021 dû aux confinements imposée par la situtation sanitaire durant cette période.\n",
    "\n",
    "Nous remarquons un grands pic pour l'hiver 2022. En effet, les enfants ayant été peu atteint lors des années précédentes se retrouvent donc tous touché la même année."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='period'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "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",
    "Nous définissons la période d'une année. Il nous est imposé une année commençant le 1er septembre, début de l'année scolaire.\n",
    "\n",
    "Dans le même temps, l'année 1990 commençant à la semaine 49, elle ne donne pas assez d'information et n'aura que peu d'influence le résultat finale.\n",
    "Nous retirons cette année pour débuter en 1991."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W') for y in range(1991, sorted_data.index[-1].year)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous vérfions également que les années contiennent entre 51 et 52 semaines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "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": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "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": [
    "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020    221186\n",
       "2021    376290\n",
       "2002    516689\n",
       "2018    542312\n",
       "2017    551041\n",
       "1996    564901\n",
       "2019    584066\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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "L'année avec l'épidémie la plus forte est l'année 2010 d'après les résultats de l'analyse.\n",
    "Comme nous pouvions nous y attendre, l'année avec l'épidémie la plus faible est 2020. Touchant principalement les enfants, la varicelle à beaucoup moins circulé lors cette année dû aux confinements survenus au début de l'année scolaire 2020/2021. "
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "68a78512f08647dba0d657fd7997aaa8ad8c9d9af1a9b906b8cea64366ea2030"
  },
  "kernelspec": {
   "display_name": "Python 3.9.13 64-bit (windows store)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}