{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incidence de la varicelle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importation du jeu de données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données de l'incidence de la varicelle sont disponibles sur le [site du réseau Sentinelles](https://www.sentiweb.fr/france/fr/?page=accueil), dans la section \"Surveillance continue\" puis \"Bases de données\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>202434</td>\n",
       "      <td>7</td>\n",
       "      <td>2831</td>\n",
       "      <td>415</td>\n",
       "      <td>5247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202433</td>\n",
       "      <td>7</td>\n",
       "      <td>1955</td>\n",
       "      <td>511</td>\n",
       "      <td>3399</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202432</td>\n",
       "      <td>7</td>\n",
       "      <td>4399</td>\n",
       "      <td>1944</td>\n",
       "      <td>6854</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202431</td>\n",
       "      <td>7</td>\n",
       "      <td>4500</td>\n",
       "      <td>2213</td>\n",
       "      <td>6787</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202430</td>\n",
       "      <td>7</td>\n",
       "      <td>7004</td>\n",
       "      <td>4278</td>\n",
       "      <td>9730</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>15</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  inc100_up  \\\n",
       "0  202434          7  2831      415    5247       4           0          8   \n",
       "1  202433          7  1955      511    3399       3           1          5   \n",
       "2  202432          7  4399     1944    6854       7           3         11   \n",
       "3  202431          7  4500     2213    6787       7           4         10   \n",
       "4  202430          7  7004     4278    9730      11           7         15   \n",
       "\n",
       "  geo_insee geo_name  \n",
       "0        FR   France  \n",
       "1        FR   France  \n",
       "2        FR   France  \n",
       "3        FR   France  \n",
       "4        FR   France  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vérifions maintenant qu'il n'y a pas de données manquantes ainsi que le type de la colonne ```\"inc\"```, qui contient les données qui nous intéressent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[\"inc\"].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il n'y a pas de données manquantes dans le jeu de données, et l'incidence a bien été traitée comme une série de nombres entiers. Nous pouvons donc travailler directement sur les données brutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = raw_data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Préparation du jeu de données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Désormais, il nous faut convertir le format de semaine donné dans le jeu de données en un format compréhensible par Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
    "data['period'] = [convert_week(yw) for yw in data['week']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Définissons la nouvelle série ```\"period\"``` comme l'index du jeu de données et trions-le en conséquence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_data = data.set_index('period').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-03/1990-12-09</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",
       "    <tr>\n",
       "      <th>1990-12-10/1990-12-16</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>1990-12-17/1990-12-23</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>1990-12-24/1990-12-30</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>1990-12-31/1991-01-06</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         week  indicator    inc  inc_low  inc_up  inc100  \\\n",
       "period                                                                     \n",
       "1990-12-03/1990-12-09  199049          7   1143        0    2610       2   \n",
       "1990-12-10/1990-12-16  199050          7  11079     6660   15498      20   \n",
       "1990-12-17/1990-12-23  199051          7  19080    13807   24353      34   \n",
       "1990-12-24/1990-12-30  199052          7  19375    13295   25455      34   \n",
       "1990-12-31/1991-01-06  199101          7  15565    10271   20859      27   \n",
       "\n",
       "                       inc100_low  inc100_up geo_insee geo_name  \n",
       "period                                                           \n",
       "1990-12-03/1990-12-09           0          5        FR   France  \n",
       "1990-12-10/1990-12-16          12         28        FR   France  \n",
       "1990-12-17/1990-12-23          25         43        FR   France  \n",
       "1990-12-24/1990-12-30          23         45        FR   France  \n",
       "1990-12-31/1991-01-06          18         36        FR   France  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vérifions la cohérence du jeu de données, en s'assurant que les dates de fin d'une période correspondent bien aux dates de début de la période suivante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "Aucune ligne n'est ressortie comme étant anormale ; nous pouvons donc poursuivre notre analyse. Portons donc un premier regard sur les données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f655ec45390>"
      ]
     },
     "execution_count": 21,
     "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": [
    "sorted_data['inc'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le même regard, sur une période plus récente :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f655eb2a5f8>"
      ]
     },
     "execution_count": 23,
     "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": [
    "sorted_data['inc'][-200:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etude de l'incidence annuelle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous observons que l'incidence semble périodique, avec un creux chaque année autour de septembre. Vérifions cela en traçant sur le même graphique que précédemment des lignes horizontales là où nous aimerions définir le début de notre période."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
    "                     for y in range(1991,\n",
    "                                    sorted_data.index[-1].year)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": [
    "ax = sorted_data['inc'][-200:].plot()\n",
    "for w in first_september_week:\n",
    "    if w >= sorted_data.index[-200]:\n",
    "        ax.axvline(w, color=\"red\", linestyle=\"--\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ce découpage a l'air tout à fait satisfaisant visuellement. Nous pouvons donc le conserver et procéder à une analyse annuelle où nos périodes commencent le premier septembre de chaque année."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_september_week[:-1],\n",
    "                        first_september_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": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f655e447940>"
      ]
     },
     "execution_count": 45,
     "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": [
    "On constate d'emblée une incidence remarquablement faible en 2020, année du Covid et des confinements qui en ont découlé. En triant les données, on peut ainsi observer les années ayant vu le plus de cas se produire."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020    221186\n",
       "2023    366227\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",
       "2022    641397\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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enfin, un histogramme montre la distribution des incidences anuelles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f655e5024e0>"
      ]
     },
     "execution_count": 49,
     "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)"
   ]
  }
 ],
 "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
}