{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 5, "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": "markdown", "metadata": {}, "source": [ "**Import des données** \n", "\n", "Si les données locales existent, je les utilise. Sinon j'utilise l'url pour les télécharger." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using local file\n" ] } ], "source": [ "import os\n", "\n", "filename = 'incidence-PAY-3.csv'\n", "if os.path.exists(filename):\n", " print('Using local file')\n", " data_path = filename\n", "else:\n", " print('Downloading data from url')\n", " data_path = 'http://www.sentiweb.fr/datasets/incidence-PAY-3.csv'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02023293102136146.014280.0159.021.0FRFrance
1202328391875694.012680.0149.019.0FRFrance
2202327390485809.012287.0149.019.0FRFrance
3202326390235934.012112.0149.019.0FRFrance
42023253100906739.013441.01510.020.0FRFrance
52023243113087639.014977.01711.023.0FRFrance
620232331430010661.017939.02217.027.0FRFrance
720232231830313822.022784.02821.035.0FRFrance
820232131646012188.020732.02519.031.0FRFrance
920232031616211963.020361.02418.030.0FRFrance
1020231931690112577.021225.02518.032.0FRFrance
1120231831992915402.024456.03023.037.0FRFrance
1220231732700721779.032235.04133.049.0FRFrance
1320231632787522767.032983.04234.050.0FRFrance
1420231533745530993.043917.05646.066.0FRFrance
1520231434806040671.055449.07261.083.0FRFrance
1620231336485956800.072918.09886.0110.0FRFrance
1720231237275064499.081001.010997.0121.0FRFrance
1820231137463866420.082856.0112100.0124.0FRFrance
1920231037636868243.084493.0115103.0127.0FRFrance
2020230936206254778.069346.09382.0104.0FRFrance
2120230837639168065.084717.0115102.0128.0FRFrance
2220230738985180397.099305.0135121.0149.0FRFrance
2320230639736887636.0107100.0146131.0161.0FRFrance
2420230539546986268.0104670.0144130.0158.0FRFrance
2520230437490166916.082886.0113101.0125.0FRFrance
2620230336957061893.077247.010593.0117.0FRFrance
2720230237826070090.086430.0118106.0130.0FRFrance
282023013121773111024.0132522.0183167.0199.0FRFrance
292022523155371142004.0168738.0234214.0254.0FRFrance
.................................
199119852132609619621.032571.04735.059.0FRFrance
199219852032789620885.034907.05138.064.0FRFrance
199319851934315432821.053487.07859.097.0FRFrance
199419851834055529935.051175.07455.093.0FRFrance
199519851733405324366.043740.06244.080.0FRFrance
199619851635036236451.064273.09166.0116.0FRFrance
199719851536388145538.082224.011683.0149.0FRFrance
19981985143134545114400.0154690.0244207.0281.0FRFrance
19991985133197206176080.0218332.0357319.0395.0FRFrance
20001985123245240223304.0267176.0445405.0485.0FRFrance
20011985113276205252399.0300011.0501458.0544.0FRFrance
20021985103353231326279.0380183.0640591.0689.0FRFrance
20031985093369895341109.0398681.0670618.0722.0FRFrance
20041985083389886359529.0420243.0707652.0762.0FRFrance
20051985073471852432599.0511105.0855784.0926.0FRFrance
20061985063565825518011.0613639.01026939.01113.0FRFrance
20071985053637302592795.0681809.011551074.01236.0FRFrance
20081985043424937390794.0459080.0770708.0832.0FRFrance
20091985033213901174689.0253113.0388317.0459.0FRFrance
201019850239758680949.0114223.0177147.0207.0FRFrance
201119850138548965918.0105060.0155120.0190.0FRFrance
201219845238483060602.0109058.0154110.0198.0FRFrance
2013198451310172680242.0123210.0185146.0224.0FRFrance
20141984503123680101401.0145959.0225184.0266.0FRFrance
2015198449310107381684.0120462.0184149.0219.0FRFrance
201619844837862060634.096606.0143110.0176.0FRFrance
201719844737202954274.089784.013199.0163.0FRFrance
201819844638733067686.0106974.0159123.0195.0FRFrance
20191984453135223101414.0169032.0246184.0308.0FRFrance
202019844436842220056.0116788.012537.0213.0FRFrance
\n", "

2021 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202329 3 10213 6146.0 14280.0 15 9.0 \n", "1 202328 3 9187 5694.0 12680.0 14 9.0 \n", "2 202327 3 9048 5809.0 12287.0 14 9.0 \n", "3 202326 3 9023 5934.0 12112.0 14 9.0 \n", "4 202325 3 10090 6739.0 13441.0 15 10.0 \n", "5 202324 3 11308 7639.0 14977.0 17 11.0 \n", "6 202323 3 14300 10661.0 17939.0 22 17.0 \n", "7 202322 3 18303 13822.0 22784.0 28 21.0 \n", "8 202321 3 16460 12188.0 20732.0 25 19.0 \n", "9 202320 3 16162 11963.0 20361.0 24 18.0 \n", "10 202319 3 16901 12577.0 21225.0 25 18.0 \n", "11 202318 3 19929 15402.0 24456.0 30 23.0 \n", "12 202317 3 27007 21779.0 32235.0 41 33.0 \n", "13 202316 3 27875 22767.0 32983.0 42 34.0 \n", "14 202315 3 37455 30993.0 43917.0 56 46.0 \n", "15 202314 3 48060 40671.0 55449.0 72 61.0 \n", "16 202313 3 64859 56800.0 72918.0 98 86.0 \n", "17 202312 3 72750 64499.0 81001.0 109 97.0 \n", "18 202311 3 74638 66420.0 82856.0 112 100.0 \n", "19 202310 3 76368 68243.0 84493.0 115 103.0 \n", "20 202309 3 62062 54778.0 69346.0 93 82.0 \n", "21 202308 3 76391 68065.0 84717.0 115 102.0 \n", "22 202307 3 89851 80397.0 99305.0 135 121.0 \n", "23 202306 3 97368 87636.0 107100.0 146 131.0 \n", "24 202305 3 95469 86268.0 104670.0 144 130.0 \n", "25 202304 3 74901 66916.0 82886.0 113 101.0 \n", "26 202303 3 69570 61893.0 77247.0 105 93.0 \n", "27 202302 3 78260 70090.0 86430.0 118 106.0 \n", "28 202301 3 121773 111024.0 132522.0 183 167.0 \n", "29 202252 3 155371 142004.0 168738.0 234 214.0 \n", "... ... ... ... ... ... ... ... \n", "1991 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1992 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1993 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1994 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1995 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1996 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1997 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1998 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1999 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2000 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2001 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2002 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2003 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2004 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2005 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2006 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2007 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2008 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2009 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2010 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2011 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2012 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2013 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2014 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2015 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2016 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2017 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2018 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2019 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2020 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 21.0 FR France \n", "1 19.0 FR France \n", "2 19.0 FR France \n", "3 19.0 FR France \n", "4 20.0 FR France \n", "5 23.0 FR France \n", "6 27.0 FR France \n", "7 35.0 FR France \n", "8 31.0 FR France \n", "9 30.0 FR France \n", "10 32.0 FR France \n", "11 37.0 FR France \n", "12 49.0 FR France \n", "13 50.0 FR France \n", "14 66.0 FR France \n", "15 83.0 FR France \n", "16 110.0 FR France \n", "17 121.0 FR France \n", "18 124.0 FR France \n", "19 127.0 FR France \n", "20 104.0 FR France \n", "21 128.0 FR France \n", "22 149.0 FR France \n", "23 161.0 FR France \n", "24 158.0 FR France \n", "25 125.0 FR France \n", "26 117.0 FR France \n", "27 130.0 FR France \n", "28 199.0 FR France \n", "29 254.0 FR France \n", "... ... ... ... \n", "1991 59.0 FR France \n", "1992 64.0 FR France \n", "1993 97.0 FR France \n", "1994 93.0 FR France \n", "1995 80.0 FR France \n", "1996 116.0 FR France \n", "1997 149.0 FR France \n", "1998 281.0 FR France \n", "1999 395.0 FR France \n", "2000 485.0 FR France \n", "2001 544.0 FR France \n", "2002 689.0 FR France \n", "2003 722.0 FR France \n", "2004 762.0 FR France \n", "2005 926.0 FR France \n", "2006 1113.0 FR France \n", "2007 1236.0 FR France \n", "2008 832.0 FR France \n", "2009 459.0 FR France \n", "2010 207.0 FR France \n", "2011 190.0 FR France \n", "2012 198.0 FR France \n", "2013 224.0 FR France \n", "2014 266.0 FR France \n", "2015 219.0 FR France \n", "2016 176.0 FR France \n", "2017 163.0 FR France \n", "2018 195.0 FR France \n", "2019 308.0 FR France \n", "2020 213.0 FR France \n", "\n", "[2021 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_path, skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": null, "metadata": {}, "outputs": [], "source": [ "#raw_data = pd.read_csv(data_url, 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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": null, "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": null, "metadata": { "collapsed": true }, "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": null, "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": { "collapsed": true }, "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": null, "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "source": [ "yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": 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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }