{ "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\n", "import os" ] }, { "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": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0020234037785067191.088509.0117101.0133.0FRFrance
1120233937228363697.080869.010996.0122.0FRFrance
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3320233734908542079.056091.07463.085.0FRFrance
4420233633824732237.044257.05849.067.0FRFrance
5520233533169526013.037377.04839.057.0FRFrance
6620233432666321057.032269.04032.048.0FRFrance
7720233331914413161.025127.02920.038.0FRFrance
8820233231464110285.018997.02215.029.0FRFrance
9920233131528610705.019867.02316.030.0FRFrance
10102023303132058647.017763.02013.027.0FRFrance
11112023293111227113.015131.01711.023.0FRFrance
1212202328391795703.012655.0149.019.0FRFrance
1313202327389995763.012235.0149.019.0FRFrance
1414202326390235934.012112.0149.019.0FRFrance
15152023253100906739.013441.01510.020.0FRFrance
16162023243113087639.014977.01711.023.0FRFrance
171720232331430010661.017939.02217.027.0FRFrance
181820232231830313822.022784.02821.035.0FRFrance
191920232131646012188.020732.02519.031.0FRFrance
202020232031616211963.020361.02418.030.0FRFrance
212120231931690112577.021225.02518.032.0FRFrance
222220231831992915402.024456.03023.037.0FRFrance
232320231732700721779.032235.04133.049.0FRFrance
242420231632787522767.032983.04234.050.0FRFrance
252520231533745530993.043917.05646.066.0FRFrance
262620231434806040671.055449.07261.083.0FRFrance
272720231336485956800.072918.09886.0110.0FRFrance
282820231237275064499.081001.010997.0121.0FRFrance
292920231137463866420.082856.0112100.0124.0FRFrance
....................................
2002200219852132609619621.032571.04735.059.0FRFrance
2003200319852032789620885.034907.05138.064.0FRFrance
2004200419851934315432821.053487.07859.097.0FRFrance
2005200519851834055529935.051175.07455.093.0FRFrance
2006200619851733405324366.043740.06244.080.0FRFrance
2007200719851635036236451.064273.09166.0116.0FRFrance
2008200819851536388145538.082224.011683.0149.0FRFrance
200920091985143134545114400.0154690.0244207.0281.0FRFrance
201020101985133197206176080.0218332.0357319.0395.0FRFrance
201120111985123245240223304.0267176.0445405.0485.0FRFrance
201220121985113276205252399.0300011.0501458.0544.0FRFrance
201320131985103353231326279.0380183.0640591.0689.0FRFrance
201420141985093369895341109.0398681.0670618.0722.0FRFrance
201520151985083389886359529.0420243.0707652.0762.0FRFrance
201620161985073471852432599.0511105.0855784.0926.0FRFrance
201720171985063565825518011.0613639.01026939.01113.0FRFrance
201820181985053637302592795.0681809.011551074.01236.0FRFrance
201920191985043424937390794.0459080.0770708.0832.0FRFrance
202020201985033213901174689.0253113.0388317.0459.0FRFrance
2021202119850239758680949.0114223.0177147.0207.0FRFrance
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2032 rows × 11 columns

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" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 \\\n", "0 0 202340 3 77850 67191.0 88509.0 117 \n", "1 1 202339 3 72283 63697.0 80869.0 109 \n", "2 2 202338 3 63218 55227.0 71209.0 95 \n", "3 3 202337 3 49085 42079.0 56091.0 74 \n", "4 4 202336 3 38247 32237.0 44257.0 58 \n", "5 5 202335 3 31695 26013.0 37377.0 48 \n", "6 6 202334 3 26663 21057.0 32269.0 40 \n", "7 7 202333 3 19144 13161.0 25127.0 29 \n", "8 8 202332 3 14641 10285.0 18997.0 22 \n", "9 9 202331 3 15286 10705.0 19867.0 23 \n", "10 10 202330 3 13205 8647.0 17763.0 20 \n", "11 11 202329 3 11122 7113.0 15131.0 17 \n", "12 12 202328 3 9179 5703.0 12655.0 14 \n", "13 13 202327 3 8999 5763.0 12235.0 14 \n", "14 14 202326 3 9023 5934.0 12112.0 14 \n", "15 15 202325 3 10090 6739.0 13441.0 15 \n", "16 16 202324 3 11308 7639.0 14977.0 17 \n", "17 17 202323 3 14300 10661.0 17939.0 22 \n", "18 18 202322 3 18303 13822.0 22784.0 28 \n", "19 19 202321 3 16460 12188.0 20732.0 25 \n", "20 20 202320 3 16162 11963.0 20361.0 24 \n", "21 21 202319 3 16901 12577.0 21225.0 25 \n", "22 22 202318 3 19929 15402.0 24456.0 30 \n", "23 23 202317 3 27007 21779.0 32235.0 41 \n", "24 24 202316 3 27875 22767.0 32983.0 42 \n", "25 25 202315 3 37455 30993.0 43917.0 56 \n", "26 26 202314 3 48060 40671.0 55449.0 72 \n", "27 27 202313 3 64859 56800.0 72918.0 98 \n", "28 28 202312 3 72750 64499.0 81001.0 109 \n", "29 29 202311 3 74638 66420.0 82856.0 112 \n", "... ... ... ... ... ... ... ... \n", "2002 2002 198521 3 26096 19621.0 32571.0 47 \n", "2003 2003 198520 3 27896 20885.0 34907.0 51 \n", "2004 2004 198519 3 43154 32821.0 53487.0 78 \n", "2005 2005 198518 3 40555 29935.0 51175.0 74 \n", "2006 2006 198517 3 34053 24366.0 43740.0 62 \n", "2007 2007 198516 3 50362 36451.0 64273.0 91 \n", "2008 2008 198515 3 63881 45538.0 82224.0 116 \n", "2009 2009 198514 3 134545 114400.0 154690.0 244 \n", "2010 2010 198513 3 197206 176080.0 218332.0 357 \n", "2011 2011 198512 3 245240 223304.0 267176.0 445 \n", "2012 2012 198511 3 276205 252399.0 300011.0 501 \n", "2013 2013 198510 3 353231 326279.0 380183.0 640 \n", "2014 2014 198509 3 369895 341109.0 398681.0 670 \n", "2015 2015 198508 3 389886 359529.0 420243.0 707 \n", "2016 2016 198507 3 471852 432599.0 511105.0 855 \n", "2017 2017 198506 3 565825 518011.0 613639.0 1026 \n", "2018 2018 198505 3 637302 592795.0 681809.0 1155 \n", "2019 2019 198504 3 424937 390794.0 459080.0 770 \n", "2020 2020 198503 3 213901 174689.0 253113.0 388 \n", "2021 2021 198502 3 97586 80949.0 114223.0 177 \n", "2022 2022 198501 3 85489 65918.0 105060.0 155 \n", "2023 2023 198452 3 84830 60602.0 109058.0 154 \n", "2024 2024 198451 3 101726 80242.0 123210.0 185 \n", "2025 2025 198450 3 123680 101401.0 145959.0 225 \n", "2026 2026 198449 3 101073 81684.0 120462.0 184 \n", "2027 2027 198448 3 78620 60634.0 96606.0 143 \n", "2028 2028 198447 3 72029 54274.0 89784.0 131 \n", "2029 2029 198446 3 87330 67686.0 106974.0 159 \n", "2030 2030 198445 3 135223 101414.0 169032.0 246 \n", "2031 2031 198444 3 68422 20056.0 116788.0 125 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 101.0 133.0 FR France \n", "1 96.0 122.0 FR France \n", "2 83.0 107.0 FR France \n", "3 63.0 85.0 FR France \n", "4 49.0 67.0 FR France \n", "5 39.0 57.0 FR France \n", "6 32.0 48.0 FR France \n", "7 20.0 38.0 FR France \n", "8 15.0 29.0 FR France \n", "9 16.0 30.0 FR France \n", "10 13.0 27.0 FR France \n", "11 11.0 23.0 FR France \n", "12 9.0 19.0 FR France \n", "13 9.0 19.0 FR France \n", "14 9.0 19.0 FR France \n", "15 10.0 20.0 FR France \n", "16 11.0 23.0 FR France \n", "17 17.0 27.0 FR France \n", "18 21.0 35.0 FR France \n", "19 19.0 31.0 FR France \n", "20 18.0 30.0 FR France \n", "21 18.0 32.0 FR France \n", "22 23.0 37.0 FR France \n", "23 33.0 49.0 FR France \n", "24 34.0 50.0 FR France \n", "25 46.0 66.0 FR France \n", "26 61.0 83.0 FR France \n", "27 86.0 110.0 FR France \n", "28 97.0 121.0 FR France \n", "29 100.0 124.0 FR France \n", "... ... ... ... ... \n", "2002 35.0 59.0 FR France \n", "2003 38.0 64.0 FR France \n", "2004 59.0 97.0 FR France \n", "2005 55.0 93.0 FR France \n", "2006 44.0 80.0 FR France \n", "2007 66.0 116.0 FR France \n", "2008 83.0 149.0 FR France \n", "2009 207.0 281.0 FR France \n", "2010 319.0 395.0 FR France \n", "2011 405.0 485.0 FR France \n", "2012 458.0 544.0 FR France \n", "2013 591.0 689.0 FR France \n", "2014 618.0 722.0 FR France \n", "2015 652.0 762.0 FR France \n", "2016 784.0 926.0 FR France \n", "2017 939.0 1113.0 FR France \n", "2018 1074.0 1236.0 FR France \n", "2019 708.0 832.0 FR France \n", "2020 317.0 459.0 FR France \n", "2021 147.0 207.0 FR France \n", "2022 120.0 190.0 FR France \n", "2023 110.0 198.0 FR France \n", "2024 146.0 224.0 FR France \n", "2025 184.0 266.0 FR France \n", "2026 149.0 219.0 FR France \n", "2027 110.0 176.0 FR France \n", "2028 99.0 163.0 FR France \n", "2029 123.0 195.0 FR France \n", "2030 184.0 308.0 FR France \n", "2031 37.0 213.0 FR France \n", "\n", "[2032 rows x 11 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We try to get the dataset from local file and download it if it doesn't already exists\n", "try:\n", " raw_data = pd.read_csv('incidence_grippale.csv')\n", "except FileNotFoundError:\n", " raw_data = pd.read_csv(data_url, skiprows=1)\n", " raw_data.to_csv('incidence_grippale.csv')\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": {}, "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": {}, "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": {}, "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 }