{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 11, "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": 12, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true }, "source": [ "Cependant, il est préférable d'utiliser une copie du fichier de données brutes, plutôt que de le télécharger à chaque fois. Cela permet notamment de se prémunir d'une panne du site [Réseau Sentinelles](www.sentiweb.fr), ou bien d'éventuelles corruption des données. Cela est réalisé en important le module `os`:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "data_file = \"syndrome-grippal.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "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": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020220438400274514.093490.0127113.0141.0FRFrance
120220337543767865.083009.0114103.0125.0FRFrance
220220235592049511.062329.08474.094.0FRFrance
320220135762950699.064559.08777.097.0FRFrance
420215235434947029.061669.08271.093.0FRFrance
520215134169835359.048037.06353.073.0FRFrance
620215033811732497.043737.05849.067.0FRFrance
720214934016834716.045620.06153.069.0FRFrance
820214834184236364.047320.06355.071.0FRFrance
920214733659831338.041858.05547.063.0FRFrance
1020214633005925302.034816.04639.053.0FRFrance
1120214532036416564.024164.03125.037.0FRFrance
1220214431899915042.022956.02923.035.0FRFrance
1320214332704021935.032145.04133.049.0FRFrance
1420214232834323382.033304.04335.051.0FRFrance
1520214132504320586.029500.03831.045.0FRFrance
1620214032628621842.030730.04033.047.0FRFrance
1720213932215518014.026296.03428.040.0FRFrance
1820213831561412310.018918.02419.029.0FRFrance
1920213731367310404.016942.02116.026.0FRFrance
202021363102897505.013073.01612.020.0FRFrance
212021353126099282.015936.01914.024.0FRFrance
222021343130159485.016545.02015.025.0FRFrance
232021333103927042.013742.01611.021.0FRFrance
2420213231558611009.020163.02417.031.0FRFrance
2520213131885513664.024046.02921.037.0FRFrance
262021303139919695.018287.02114.028.0FRFrance
272021293136269618.017634.02115.027.0FRFrance
28202128386365430.011842.0138.018.0FRFrance
292021273106936838.014548.01610.022.0FRFrance
.................................
191419852132609619621.032571.04735.059.0FRFrance
191519852032789620885.034907.05138.064.0FRFrance
191619851934315432821.053487.07859.097.0FRFrance
191719851834055529935.051175.07455.093.0FRFrance
191819851733405324366.043740.06244.080.0FRFrance
191919851635036236451.064273.09166.0116.0FRFrance
192019851536388145538.082224.011683.0149.0FRFrance
19211985143134545114400.0154690.0244207.0281.0FRFrance
19221985133197206176080.0218332.0357319.0395.0FRFrance
19231985123245240223304.0267176.0445405.0485.0FRFrance
19241985113276205252399.0300011.0501458.0544.0FRFrance
19251985103353231326279.0380183.0640591.0689.0FRFrance
19261985093369895341109.0398681.0670618.0722.0FRFrance
19271985083389886359529.0420243.0707652.0762.0FRFrance
19281985073471852432599.0511105.0855784.0926.0FRFrance
19291985063565825518011.0613639.01026939.01113.0FRFrance
19301985053637302592795.0681809.011551074.01236.0FRFrance
19311985043424937390794.0459080.0770708.0832.0FRFrance
19321985033213901174689.0253113.0388317.0459.0FRFrance
193319850239758680949.0114223.0177147.0207.0FRFrance
193419850138548965918.0105060.0155120.0190.0FRFrance
193519845238483060602.0109058.0154110.0198.0FRFrance
1936198451310172680242.0123210.0185146.0224.0FRFrance
19371984503123680101401.0145959.0225184.0266.0FRFrance
1938198449310107381684.0120462.0184149.0219.0FRFrance
193919844837862060634.096606.0143110.0176.0FRFrance
194019844737202954274.089784.013199.0163.0FRFrance
194119844638733067686.0106974.0159123.0195.0FRFrance
19421984453135223101414.0169032.0246184.0308.0FRFrance
194319844436842220056.0116788.012537.0213.0FRFrance
\n", "

1944 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202204 3 84002 74514.0 93490.0 127 113.0 \n", "1 202203 3 75437 67865.0 83009.0 114 103.0 \n", "2 202202 3 55920 49511.0 62329.0 84 74.0 \n", "3 202201 3 57629 50699.0 64559.0 87 77.0 \n", "4 202152 3 54349 47029.0 61669.0 82 71.0 \n", "5 202151 3 41698 35359.0 48037.0 63 53.0 \n", "6 202150 3 38117 32497.0 43737.0 58 49.0 \n", "7 202149 3 40168 34716.0 45620.0 61 53.0 \n", "8 202148 3 41842 36364.0 47320.0 63 55.0 \n", "9 202147 3 36598 31338.0 41858.0 55 47.0 \n", "10 202146 3 30059 25302.0 34816.0 46 39.0 \n", "11 202145 3 20364 16564.0 24164.0 31 25.0 \n", "12 202144 3 18999 15042.0 22956.0 29 23.0 \n", "13 202143 3 27040 21935.0 32145.0 41 33.0 \n", "14 202142 3 28343 23382.0 33304.0 43 35.0 \n", "15 202141 3 25043 20586.0 29500.0 38 31.0 \n", "16 202140 3 26286 21842.0 30730.0 40 33.0 \n", "17 202139 3 22155 18014.0 26296.0 34 28.0 \n", "18 202138 3 15614 12310.0 18918.0 24 19.0 \n", "19 202137 3 13673 10404.0 16942.0 21 16.0 \n", "20 202136 3 10289 7505.0 13073.0 16 12.0 \n", "21 202135 3 12609 9282.0 15936.0 19 14.0 \n", "22 202134 3 13015 9485.0 16545.0 20 15.0 \n", "23 202133 3 10392 7042.0 13742.0 16 11.0 \n", "24 202132 3 15586 11009.0 20163.0 24 17.0 \n", "25 202131 3 18855 13664.0 24046.0 29 21.0 \n", "26 202130 3 13991 9695.0 18287.0 21 14.0 \n", "27 202129 3 13626 9618.0 17634.0 21 15.0 \n", "28 202128 3 8636 5430.0 11842.0 13 8.0 \n", "29 202127 3 10693 6838.0 14548.0 16 10.0 \n", "... ... ... ... ... ... ... ... \n", "1914 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1915 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1916 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1917 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1918 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1919 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1920 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1921 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1922 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1923 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1924 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1925 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1926 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1927 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1928 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1929 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1930 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1931 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1932 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1933 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1934 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1935 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1936 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1937 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1938 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1939 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1940 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1941 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1942 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1943 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 141.0 FR France \n", "1 125.0 FR France \n", "2 94.0 FR France \n", "3 97.0 FR France \n", "4 93.0 FR France \n", "5 73.0 FR France \n", "6 67.0 FR France \n", "7 69.0 FR France \n", "8 71.0 FR France \n", "9 63.0 FR France \n", "10 53.0 FR France \n", "11 37.0 FR France \n", "12 35.0 FR France \n", "13 49.0 FR France \n", "14 51.0 FR France \n", "15 45.0 FR France \n", "16 47.0 FR France \n", "17 40.0 FR France \n", "18 29.0 FR France \n", "19 26.0 FR France \n", "20 20.0 FR France \n", "21 24.0 FR France \n", "22 25.0 FR France \n", "23 21.0 FR France \n", "24 31.0 FR France \n", "25 37.0 FR France \n", "26 28.0 FR France \n", "27 27.0 FR France \n", "28 18.0 FR France \n", "29 22.0 FR France \n", "... ... ... ... \n", "1914 59.0 FR France \n", "1915 64.0 FR France \n", "1916 97.0 FR France \n", "1917 93.0 FR France \n", "1918 80.0 FR France \n", "1919 116.0 FR France \n", "1920 149.0 FR France \n", "1921 281.0 FR France \n", "1922 395.0 FR France \n", "1923 485.0 FR France \n", "1924 544.0 FR France \n", "1925 689.0 FR France \n", "1926 722.0 FR France \n", "1927 762.0 FR France \n", "1928 926.0 FR France \n", "1929 1113.0 FR France \n", "1930 1236.0 FR France \n", "1931 832.0 FR France \n", "1932 459.0 FR France \n", "1933 207.0 FR France \n", "1934 190.0 FR France \n", "1935 198.0 FR France \n", "1936 224.0 FR France \n", "1937 266.0 FR France \n", "1938 219.0 FR France \n", "1939 176.0 FR France \n", "1940 163.0 FR France \n", "1941 195.0 FR France \n", "1942 308.0 FR France \n", "1943 213.0 FR France \n", "\n", "[1944 rows x 10 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "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 }