{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 2, "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": 3, "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": "markdown", "metadata": {}, "source": [ "Afin d'éviter une éventuelle modification du fichier sur le serveur du réseau sentinelle, nous faisons une copie locale du jeu de données. Ainsi les données ne sont téléchargées que si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fichier existant\n" ] } ], "source": [ "# Si le fichier csv n'existe pas dans le repertoire local je l'importe de l'url:\n", "import urllib.request\n", "import os\n", "data_file = \"incidence-PAY-3.csv\"\n", "if not os.path.isfile(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
160919891930NaNNaN0NaNNaNFRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1609 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1609 FR France " ] }, "execution_count": 6, "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": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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22020093110696102066.0119326.0168155.0181.0FRFrance
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52020063206669195481.0217857.0314297.0331.0FRFrance
62020053187957177445.0198469.0285269.0301.0FRFrance
72020043122331113492.0131170.0186173.0199.0FRFrance
820200337841371330.085496.0119108.0130.0FRFrance
920200235361447654.059574.08172.090.0FRFrance
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1320195033422329156.039290.05244.060.0FRFrance
1420194932566221414.029910.03933.045.0FRFrance
1520194832236718055.026679.03427.041.0FRFrance
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1720194631603012567.019493.02419.029.0FRFrance
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19201944378225010.010634.0128.016.0FRFrance
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22201941371224720.09524.0117.015.0FRFrance
23201940385055784.011226.0139.017.0FRFrance
24201939370914462.09720.0117.015.0FRFrance
25201938348972891.06903.074.010.0FRFrance
26201937331721367.04977.052.08.0FRFrance
2720193632295728.03862.031.05.0FRFrance
28201935310102.02018.020.04.0FRFrance
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.................................
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1845 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202011 3 101704 93652.0 109756.0 154 142.0 \n", "1 202010 3 104977 96650.0 113304.0 159 146.0 \n", "2 202009 3 110696 102066.0 119326.0 168 155.0 \n", "3 202008 3 143753 133984.0 153522.0 218 203.0 \n", "4 202007 3 183610 172812.0 194408.0 279 263.0 \n", "5 202006 3 206669 195481.0 217857.0 314 297.0 \n", "6 202005 3 187957 177445.0 198469.0 285 269.0 \n", "7 202004 3 122331 113492.0 131170.0 186 173.0 \n", "8 202003 3 78413 71330.0 85496.0 119 108.0 \n", "9 202002 3 53614 47654.0 59574.0 81 72.0 \n", "10 202001 3 36850 31608.0 42092.0 56 48.0 \n", "11 201952 3 28135 23220.0 33050.0 43 36.0 \n", "12 201951 3 29786 25042.0 34530.0 45 38.0 \n", "13 201950 3 34223 29156.0 39290.0 52 44.0 \n", "14 201949 3 25662 21414.0 29910.0 39 33.0 \n", "15 201948 3 22367 18055.0 26679.0 34 27.0 \n", "16 201947 3 18669 14759.0 22579.0 28 22.0 \n", "17 201946 3 16030 12567.0 19493.0 24 19.0 \n", "18 201945 3 10138 7160.0 13116.0 15 10.0 \n", "19 201944 3 7822 5010.0 10634.0 12 8.0 \n", "20 201943 3 9487 6448.0 12526.0 14 9.0 \n", "21 201942 3 7747 5243.0 10251.0 12 8.0 \n", "22 201941 3 7122 4720.0 9524.0 11 7.0 \n", "23 201940 3 8505 5784.0 11226.0 13 9.0 \n", "24 201939 3 7091 4462.0 9720.0 11 7.0 \n", "25 201938 3 4897 2891.0 6903.0 7 4.0 \n", "26 201937 3 3172 1367.0 4977.0 5 2.0 \n", "27 201936 3 2295 728.0 3862.0 3 1.0 \n", "28 201935 3 1010 2.0 2018.0 2 0.0 \n", "29 201934 3 1672 279.0 3065.0 3 1.0 \n", "... ... ... ... ... ... ... ... \n", "1816 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1817 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1818 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1819 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1820 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1821 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1822 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1823 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1824 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1825 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1826 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1827 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1828 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1829 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1830 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1831 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1832 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1833 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1834 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1835 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1836 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1837 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1838 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1839 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1840 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1841 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1842 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1843 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1844 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1845 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 166.0 FR France \n", "1 172.0 FR France \n", "2 181.0 FR France \n", "3 233.0 FR France \n", "4 295.0 FR France \n", "5 331.0 FR France \n", "6 301.0 FR France \n", "7 199.0 FR France \n", "8 130.0 FR France \n", "9 90.0 FR France \n", "10 64.0 FR France \n", "11 50.0 FR France \n", "12 52.0 FR France \n", "13 60.0 FR France \n", "14 45.0 FR France \n", "15 41.0 FR France \n", "16 34.0 FR France \n", "17 29.0 FR France \n", "18 20.0 FR France \n", "19 16.0 FR France \n", "20 19.0 FR France \n", "21 16.0 FR France \n", "22 15.0 FR France \n", "23 17.0 FR France \n", "24 15.0 FR France \n", "25 10.0 FR France \n", "26 8.0 FR France \n", "27 5.0 FR France \n", "28 4.0 FR France \n", "29 5.0 FR France \n", "... ... ... ... \n", "1816 59.0 FR France \n", "1817 64.0 FR France \n", "1818 97.0 FR France \n", "1819 93.0 FR France \n", "1820 80.0 FR France \n", "1821 116.0 FR France \n", "1822 149.0 FR France \n", "1823 281.0 FR France \n", "1824 395.0 FR France \n", "1825 485.0 FR France \n", "1826 544.0 FR France \n", "1827 689.0 FR France \n", "1828 722.0 FR France \n", "1829 762.0 FR France \n", "1830 926.0 FR France \n", "1831 1113.0 FR France \n", "1832 1236.0 FR France \n", "1833 832.0 FR France \n", "1834 459.0 FR France \n", "1835 207.0 FR France \n", "1836 190.0 FR France \n", "1837 198.0 FR France \n", "1838 224.0 FR France \n", "1839 266.0 FR France \n", "1840 219.0 FR France \n", "1841 176.0 FR France \n", "1842 163.0 FR France \n", "1843 195.0 FR France \n", "1844 308.0 FR France \n", "1845 213.0 FR France \n", "\n", "[1845 rows x 10 columns]" ] }, "execution_count": 7, "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": 8, "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": 9, "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": 10, "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": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "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": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "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": 13, "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": 14, "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": 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.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": 16, "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": 16, "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": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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