{ "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": 8, "metadata": {}, "outputs": [], "source": [ "data_file = \"D:\\Home\\cleserrec\\Desktop\\incidence-PAY-3.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "C'est le chemin pour trouver le fichier de données en local. On préfèrera travailler avec un fichier local plutôt que de téléchager les données à chaque fois, pour : (1) gagner du temps, (2) ne pas avoir besoin d'une connexion à internet, (3) s'assurer qu ele code fonctionne même si le site effectue un changement sur les données ou change leur adresse \n", "\n", "La bibliothèque os permet de travailler sur des fichiers. La bibliothèque urllib permet de travailler avec des URL.\n", "Si le fichier n'existe pas déjà, on le télécharge. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020211233654230199.042885.05545.065.0FRFrance
120211132520620860.029552.03831.045.0FRFrance
220211031953915951.023127.03025.035.0FRFrance
320210931757213926.021218.02721.033.0FRFrance
420210832088216907.024857.03226.038.0FRFrance
520210732239318303.026483.03428.040.0FRFrance
620210632318319134.027232.03529.041.0FRFrance
720210532242618445.026407.03428.040.0FRFrance
820210432580421491.030117.03932.046.0FRFrance
920210332181017894.025726.03327.039.0FRFrance
1020210231732013906.020734.02621.031.0FRFrance
1120210132179917778.025820.03327.039.0FRFrance
1220205332122016498.025942.03225.039.0FRFrance
1320205231642812285.020571.02519.031.0FRFrance
1420205132161917370.025868.03327.039.0FRFrance
1520205031684513220.020470.02620.032.0FRFrance
162020493129399923.015955.02015.025.0FRFrance
1720204831380410641.016967.02116.026.0FRFrance
1820204731908515285.022885.02923.035.0FRFrance
1920204632480120503.029099.03831.045.0FRFrance
2020204534251636857.048175.06556.074.0FRFrance
2120204434456738521.050613.06859.077.0FRFrance
2220204334373737523.049951.06657.075.0FRFrance
2320204233514529812.040478.05345.061.0FRFrance
2420204132787723206.032548.04235.049.0FRFrance
2520204032044316381.024505.03125.037.0FRFrance
2620203931981015900.023720.03024.036.0FRFrance
2720203832555921141.029977.03932.046.0FRFrance
2820203731848514649.022321.02822.034.0FRFrance
292020363103907646.013134.01612.020.0FRFrance
.................................
187019852132609619621.032571.04735.059.0FRFrance
187119852032789620885.034907.05138.064.0FRFrance
187219851934315432821.053487.07859.097.0FRFrance
187319851834055529935.051175.07455.093.0FRFrance
187419851733405324366.043740.06244.080.0FRFrance
187519851635036236451.064273.09166.0116.0FRFrance
187619851536388145538.082224.011683.0149.0FRFrance
18771985143134545114400.0154690.0244207.0281.0FRFrance
18781985133197206176080.0218332.0357319.0395.0FRFrance
18791985123245240223304.0267176.0445405.0485.0FRFrance
18801985113276205252399.0300011.0501458.0544.0FRFrance
18811985103353231326279.0380183.0640591.0689.0FRFrance
18821985093369895341109.0398681.0670618.0722.0FRFrance
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18851985063565825518011.0613639.01026939.01113.0FRFrance
18861985053637302592795.0681809.011551074.01236.0FRFrance
18871985043424937390794.0459080.0770708.0832.0FRFrance
18881985033213901174689.0253113.0388317.0459.0FRFrance
188919850239758680949.0114223.0177147.0207.0FRFrance
189019850138548965918.0105060.0155120.0190.0FRFrance
189119845238483060602.0109058.0154110.0198.0FRFrance
1892198451310172680242.0123210.0185146.0224.0FRFrance
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189919844436842220056.0116788.012537.0213.0FRFrance
\n", "

1900 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202112 3 36542 30199.0 42885.0 55 45.0 \n", "1 202111 3 25206 20860.0 29552.0 38 31.0 \n", "2 202110 3 19539 15951.0 23127.0 30 25.0 \n", "3 202109 3 17572 13926.0 21218.0 27 21.0 \n", "4 202108 3 20882 16907.0 24857.0 32 26.0 \n", "5 202107 3 22393 18303.0 26483.0 34 28.0 \n", "6 202106 3 23183 19134.0 27232.0 35 29.0 \n", "7 202105 3 22426 18445.0 26407.0 34 28.0 \n", "8 202104 3 25804 21491.0 30117.0 39 32.0 \n", "9 202103 3 21810 17894.0 25726.0 33 27.0 \n", "10 202102 3 17320 13906.0 20734.0 26 21.0 \n", "11 202101 3 21799 17778.0 25820.0 33 27.0 \n", "12 202053 3 21220 16498.0 25942.0 32 25.0 \n", "13 202052 3 16428 12285.0 20571.0 25 19.0 \n", "14 202051 3 21619 17370.0 25868.0 33 27.0 \n", "15 202050 3 16845 13220.0 20470.0 26 20.0 \n", "16 202049 3 12939 9923.0 15955.0 20 15.0 \n", "17 202048 3 13804 10641.0 16967.0 21 16.0 \n", "18 202047 3 19085 15285.0 22885.0 29 23.0 \n", "19 202046 3 24801 20503.0 29099.0 38 31.0 \n", "20 202045 3 42516 36857.0 48175.0 65 56.0 \n", "21 202044 3 44567 38521.0 50613.0 68 59.0 \n", "22 202043 3 43737 37523.0 49951.0 66 57.0 \n", "23 202042 3 35145 29812.0 40478.0 53 45.0 \n", "24 202041 3 27877 23206.0 32548.0 42 35.0 \n", "25 202040 3 20443 16381.0 24505.0 31 25.0 \n", "26 202039 3 19810 15900.0 23720.0 30 24.0 \n", "27 202038 3 25559 21141.0 29977.0 39 32.0 \n", "28 202037 3 18485 14649.0 22321.0 28 22.0 \n", "29 202036 3 10390 7646.0 13134.0 16 12.0 \n", "... ... ... ... ... ... ... ... \n", "1870 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1871 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1872 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1873 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1874 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1875 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1876 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1877 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1878 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1879 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1880 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1881 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1882 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1883 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1884 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1885 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1886 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1887 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1888 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1889 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1890 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1891 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1892 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1893 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1894 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1895 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1896 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1897 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1898 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1899 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 65.0 FR France \n", "1 45.0 FR France \n", "2 35.0 FR France \n", "3 33.0 FR France \n", "4 38.0 FR France \n", "5 40.0 FR France \n", "6 41.0 FR France \n", "7 40.0 FR France \n", "8 46.0 FR France \n", "9 39.0 FR France \n", "10 31.0 FR France \n", "11 39.0 FR France \n", "12 39.0 FR France \n", "13 31.0 FR France \n", "14 39.0 FR France \n", "15 32.0 FR France \n", "16 25.0 FR France \n", "17 26.0 FR France \n", "18 35.0 FR France \n", "19 45.0 FR France \n", "20 74.0 FR France \n", "21 77.0 FR France \n", "22 75.0 FR France \n", "23 61.0 FR France \n", "24 49.0 FR France \n", "25 37.0 FR France \n", "26 36.0 FR France \n", "27 46.0 FR France \n", "28 34.0 FR France \n", "29 20.0 FR France \n", "... ... ... ... \n", "1870 59.0 FR France \n", "1871 64.0 FR France \n", "1872 97.0 FR France \n", "1873 93.0 FR France \n", "1874 80.0 FR France \n", "1875 116.0 FR France \n", "1876 149.0 FR France \n", "1877 281.0 FR France \n", "1878 395.0 FR France \n", "1879 485.0 FR France \n", "1880 544.0 FR France \n", "1881 689.0 FR France \n", "1882 722.0 FR France \n", "1883 762.0 FR France \n", "1884 926.0 FR France \n", "1885 1113.0 FR France \n", "1886 1236.0 FR France \n", "1887 832.0 FR France \n", "1888 459.0 FR France \n", "1889 207.0 FR France \n", "1890 190.0 FR France \n", "1891 198.0 FR France \n", "1892 224.0 FR France \n", "1893 266.0 FR France \n", "1894 219.0 FR France \n", "1895 176.0 FR France \n", "1896 163.0 FR France \n", "1897 195.0 FR France \n", "1898 308.0 FR France \n", "1899 213.0 FR France \n", "\n", "[1900 rows x 10 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os.path\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)\n", "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": 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 }