{
 "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": [
    "Les données de l'incidence de la varicelle sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1991 et se termine avec une semaine récente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# URL de la base de données sur la varicelle\n",
    "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fichier trouvé sur le serveur\n"
     ]
    }
   ],
   "source": [
    "# Vérification de la présence du fichier en local\n",
    "# Si non, téléchargement à partir de l'URL\n",
    "import os.path\n",
    "# Vérifier si le fichier existe ou non\n",
    "if os.path.isfile('incidence-PAY-7.csv'):\n",
    "    print(\"Fichier trouvé sur le serveur\")\n",
    "    raw_data = pd.read_csv('incidence-PAY-7.csv')\n",
    "else:\n",
    "    print(\"Fichier non trouvé sur le serveur\")\n",
    "    print(\"Téléchargement du fichier sur le site Web\")\n",
    "    raw_data = pd.read_csv(data_url)\n",
    "    # Ecriture du fichier en local\n",
    "    raw_data.to_csv('incidence-PAY-7.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
    "\n",
    "| Nom de colonne | Libellé de colonne                                                                                                                |\n",
    "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
    "| week           | Semaine calendaire (ISO 8601)                                                                                                     |\n",
    "| indicator      | Code de l'indicateur de surveillance                                                                                              |\n",
    "| inc            | Estimation de l'incidence de consultations en nombre de cas                                                                       |\n",
    "| inc_low        | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation                                                     |\n",
    "| inc_up         | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation                                                     |\n",
    "| inc100         | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants)                                   |\n",
    "| inc100_low     | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
    "| inc100_up      | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
    "| geo_insee      | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/                            |\n",
    "| geo_name       | Libellé de la zone géographique (ce libellé peut être modifié sans préavis)                                                       |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Effacement de la première colonne qui est un ndex en double\n",
    "data = raw_data.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>202015</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>609</td>\n",
       "      <td>3397</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>1</th>\n",
       "      <td>202014</td>\n",
       "      <td>7</td>\n",
       "      <td>3881</td>\n",
       "      <td>2223</td>\n",
       "      <td>5539</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202013</td>\n",
       "      <td>7</td>\n",
       "      <td>7341</td>\n",
       "      <td>5247</td>\n",
       "      <td>9435</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202012</td>\n",
       "      <td>7</td>\n",
       "      <td>8123</td>\n",
       "      <td>5790</td>\n",
       "      <td>10456</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202011</td>\n",
       "      <td>7</td>\n",
       "      <td>10198</td>\n",
       "      <td>7568</td>\n",
       "      <td>12828</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>19</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  202015          7   2003      609    3397       3           1          5   \n",
       "1  202014          7   3881     2223    5539       6           3          9   \n",
       "2  202013          7   7341     5247    9435      11           8         14   \n",
       "3  202012          7   8123     5790   10456      12           8         16   \n",
       "4  202011          7  10198     7568   12828      15          11         19   \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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Affichage de quelques données.\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y a-t-il des points manquants dans ce jeux de données ? Non, il n'y a pas de manquants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nos données utilisent une convention inhabituelle: le numéro de\n",
    "semaine est collé à l'année, donnant l'impression qu'il s'agit\n",
    "de nombre entier. C'est comme ça que Pandas les interprète.\n",
    "  \n",
    "Un deuxième problème est que Pandas ne comprend pas les numéros de\n",
    "semaine.  Il faut lui fournir les dates de début et de fin de\n",
    "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n",
    "\n",
    "Comme la conversion des semaines est devenu assez complexe, nous\n",
    "écrivons une petite fonction Python pour cela. Ensuite, nous\n",
    "l'appliquons à tous les points de nos donnés. Les résultats vont\n",
    "dans une nouvelle colonne 'period'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "Il restent deux petites modifications à faire.\n",
    "\n",
    "Premièrement, nous définissons les périodes d'observation\n",
    "comme nouvel index de notre jeux de données. Ceci en fait\n",
    "une suite chronologique, ce qui sera pratique par la suite.\n",
    "\n",
    "Deuxièmement, nous trions les points par période, dans\n",
    "le sens chronologique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_data = 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.\n",
    "\n",
    "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n",
    "entre lesquelles il manque une semaine.\n",
    "\n",
    "Nous reconnaissons ces dates: c'est la semaine sans observations\n",
    "que nous avions supprimées !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "Un premier regard sur les données !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f262c5cac88>"
      ]
     },
     "execution_count": 10,
     "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": [
    "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en fin d'été."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f262a3d0898>"
      ]
     },
     "execution_count": 11,
     "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": [
    "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n",
    "entre deux années civiles, nous définissons la période de référence\n",
    "entre deux minima de l'incidence, du 1er septembre de l'année $N$ au\n",
    "1er septembre de l'année $N+1$.\n",
    "\n",
    "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
    "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
    "de référence: à la place du 1er septembre de chaque année, nous utilisons le\n",
    "premier jour de la semaine qui contient le 1er septembre.\n",
    "\n",
    "Comme l'incidence de la varicelle est très faible en été, cette\n",
    "modification ne risque pas de fausser nos conclusions.\n",
    "\n",
    "Encore un petit détail: les données commencent en décembre 1991, ce qui\n",
    "rend la première année incomplète. Nous commençons donc l'analyse en 1992."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
    "                     for y in range(1992,\n",
    "                                    sorted_data.index[-1].year)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": true
   },
   "source": [
    "En partant de cette liste des semaines qui contiennent un 1er septembre, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
    "\n",
    "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Voici les incidences annuelles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f262a363f98>"
      ]
     },
     "execution_count": 19,
     "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": [
    "Une liste triée permet de plus facilement répérer les valeurs les plus élevées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "2009    842373\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enfin, un histogramme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f262a3522b0>"
      ]
     },
     "execution_count": 16,
     "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": 1
}