{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Incidence du syndrome grippal"
]
},
{
"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 du syndrome grippal 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 1984 et se termine avec une semaine récente."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.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) |\n",
"\n",
"La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"1634 198919 3 0 NaN NaN 0 NaN NaN \n",
"\n",
" geo_insee geo_name \n",
"1634 FR France "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202036 3 10888 8050.0 13726.0 17 13.0 \n",
"1 202035 3 9918 6842.0 12994.0 15 10.0 \n",
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},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
]
},
{
"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": 6,
"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": 7,
"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": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
]
}
],
"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": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"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 été."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"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 août de l'année $N$ au\n",
"1er août 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 août de chaque année, nous utilisons le\n",
"premier jour de la semaine qui contient le 1er août.\n",
"\n",
"Comme l'incidence de syndrome grippal est très faible en été, cette\n",
"modification ne risque pas de fausser nos conclusions.\n",
"\n",
"Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
"rend la première année incomplète. Nous commençons donc l'analyse en 1985."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
" for y in range(1985,\n",
" sorted_data.index[-1].year)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En partant de cette liste des semaines qui contiennent un 1er août, 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": 12,
"metadata": {},
"outputs": [],
"source": [
"year = []\n",
"yearly_incidence = []\n",
"for week1, week2 in zip(first_august_week[:-1],\n",
" first_august_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": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"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": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2014 1600941\n",
"1991 1659249\n",
"1995 1840410\n",
"2012 2175217\n",
"2003 2234584\n",
"2019 2254386\n",
"2006 2307352\n",
"2017 2321583\n",
"2001 2529279\n",
"1992 2574578\n",
"1993 2703886\n",
"2018 2705325\n",
"1988 2765617\n",
"2007 2780164\n",
"1987 2855570\n",
"2016 2856393\n",
"2011 2857040\n",
"2008 2973918\n",
"1998 3034904\n",
"2002 3125418\n",
"2009 3444020\n",
"1994 3514763\n",
"1996 3539413\n",
"2004 3567744\n",
"1997 3620066\n",
"2015 3654892\n",
"2000 3826372\n",
"2005 3835025\n",
"1999 3908112\n",
"2010 4111392\n",
"2013 4182691\n",
"1986 5115251\n",
"1990 5235827\n",
"1989 5466192\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_incidence.sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
" française, sont assez rares: il y en eu trois au cours des 35 dernières années."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"yearly_incidence.hist(xrot=20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"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": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}