{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 4, "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": 5, "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": [ "Nous testons d'abord si aucun fichier local est présent et si ce n'est pas le cas, nous fabriquons ce fichier local. le but est d'éviter d'avoir des problème si les données présentes sur le site d'origine venait à être modifiée. Les données utilisées pour l'étude pourrait à tous moment être retrouvées. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import os\n", "import urllib.request\n", "fileName = 'data_grippe.csv'\n", "if not os.path.exists(fileName):\n", " urllib.request.urlretrieve(data_url, fileName) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "lecture du fichier local avec pandas" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020210432933423896.034772.04436.052.0FRFrance
120210332138017494.025266.03226.038.0FRFrance
220210231732013906.020734.02621.031.0FRFrance
320210132179917778.025820.03327.039.0FRFrance
420205332122016498.025942.03225.039.0FRFrance
520205231642812285.020571.02519.031.0FRFrance
620205132161917370.025868.03327.039.0FRFrance
720205031684513220.020470.02620.032.0FRFrance
82020493129399923.015955.02015.025.0FRFrance
920204831380410641.016967.02116.026.0FRFrance
1020204731908515285.022885.02923.035.0FRFrance
1120204632480120503.029099.03831.045.0FRFrance
1220204534251636857.048175.06556.074.0FRFrance
1320204434456738521.050613.06859.077.0FRFrance
1420204334373737523.049951.06657.075.0FRFrance
1520204233514529812.040478.05345.061.0FRFrance
1620204132787723206.032548.04235.049.0FRFrance
1720204032044316381.024505.03125.037.0FRFrance
1820203931981015900.023720.03024.036.0FRFrance
1920203832556221142.029982.03932.046.0FRFrance
2020203731848514649.022321.02822.034.0FRFrance
212020363103907646.013134.01612.020.0FRFrance
22202035399186842.012994.01510.020.0FRFrance
23202034360843090.09078.094.014.0FRFrance
24202033361063411.08801.095.013.0FRFrance
25202032359183330.08506.095.013.0FRFrance
26202031343512269.06433.074.010.0FRFrance
27202030381795442.010916.0128.016.0FRFrance
28202029386875860.011514.0139.017.0FRFrance
29202028383405701.010979.0139.017.0FRFrance
.................................
186219852132609619621.032571.04735.059.0FRFrance
186319852032789620885.034907.05138.064.0FRFrance
186419851934315432821.053487.07859.097.0FRFrance
186519851834055529935.051175.07455.093.0FRFrance
186619851733405324366.043740.06244.080.0FRFrance
186719851635036236451.064273.09166.0116.0FRFrance
186819851536388145538.082224.011683.0149.0FRFrance
18691985143134545114400.0154690.0244207.0281.0FRFrance
18701985133197206176080.0218332.0357319.0395.0FRFrance
18711985123245240223304.0267176.0445405.0485.0FRFrance
18721985113276205252399.0300011.0501458.0544.0FRFrance
18731985103353231326279.0380183.0640591.0689.0FRFrance
18741985093369895341109.0398681.0670618.0722.0FRFrance
18751985083389886359529.0420243.0707652.0762.0FRFrance
18761985073471852432599.0511105.0855784.0926.0FRFrance
18771985063565825518011.0613639.01026939.01113.0FRFrance
18781985053637302592795.0681809.011551074.01236.0FRFrance
18791985043424937390794.0459080.0770708.0832.0FRFrance
18801985033213901174689.0253113.0388317.0459.0FRFrance
188119850239758680949.0114223.0177147.0207.0FRFrance
188219850138548965918.0105060.0155120.0190.0FRFrance
188319845238483060602.0109058.0154110.0198.0FRFrance
1884198451310172680242.0123210.0185146.0224.0FRFrance
18851984503123680101401.0145959.0225184.0266.0FRFrance
1886198449310107381684.0120462.0184149.0219.0FRFrance
188719844837862060634.096606.0143110.0176.0FRFrance
188819844737202954274.089784.013199.0163.0FRFrance
188919844638733067686.0106974.0159123.0195.0FRFrance
18901984453135223101414.0169032.0246184.0308.0FRFrance
189119844436842220056.0116788.012537.0213.0FRFrance
\n", "

1892 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202104 3 29334 23896.0 34772.0 44 36.0 \n", "1 202103 3 21380 17494.0 25266.0 32 26.0 \n", "2 202102 3 17320 13906.0 20734.0 26 21.0 \n", "3 202101 3 21799 17778.0 25820.0 33 27.0 \n", "4 202053 3 21220 16498.0 25942.0 32 25.0 \n", "5 202052 3 16428 12285.0 20571.0 25 19.0 \n", "6 202051 3 21619 17370.0 25868.0 33 27.0 \n", "7 202050 3 16845 13220.0 20470.0 26 20.0 \n", "8 202049 3 12939 9923.0 15955.0 20 15.0 \n", "9 202048 3 13804 10641.0 16967.0 21 16.0 \n", "10 202047 3 19085 15285.0 22885.0 29 23.0 \n", "11 202046 3 24801 20503.0 29099.0 38 31.0 \n", "12 202045 3 42516 36857.0 48175.0 65 56.0 \n", "13 202044 3 44567 38521.0 50613.0 68 59.0 \n", "14 202043 3 43737 37523.0 49951.0 66 57.0 \n", "15 202042 3 35145 29812.0 40478.0 53 45.0 \n", "16 202041 3 27877 23206.0 32548.0 42 35.0 \n", "17 202040 3 20443 16381.0 24505.0 31 25.0 \n", "18 202039 3 19810 15900.0 23720.0 30 24.0 \n", "19 202038 3 25562 21142.0 29982.0 39 32.0 \n", "20 202037 3 18485 14649.0 22321.0 28 22.0 \n", "21 202036 3 10390 7646.0 13134.0 16 12.0 \n", "22 202035 3 9918 6842.0 12994.0 15 10.0 \n", "23 202034 3 6084 3090.0 9078.0 9 4.0 \n", "24 202033 3 6106 3411.0 8801.0 9 5.0 \n", "25 202032 3 5918 3330.0 8506.0 9 5.0 \n", "26 202031 3 4351 2269.0 6433.0 7 4.0 \n", "27 202030 3 8179 5442.0 10916.0 12 8.0 \n", "28 202029 3 8687 5860.0 11514.0 13 9.0 \n", "29 202028 3 8340 5701.0 10979.0 13 9.0 \n", "... ... ... ... ... ... ... ... \n", "1862 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1863 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1864 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1865 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1866 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1867 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1868 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1869 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1870 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1871 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1872 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1873 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1874 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1875 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1876 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1877 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1878 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1879 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1880 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1881 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1882 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1883 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1884 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1885 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1886 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1887 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1888 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1889 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1890 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1891 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 52.0 FR France \n", "1 38.0 FR France \n", "2 31.0 FR France \n", "3 39.0 FR France \n", "4 39.0 FR France \n", "5 31.0 FR France \n", "6 39.0 FR France \n", "7 32.0 FR France \n", "8 25.0 FR France \n", "9 26.0 FR France \n", "10 35.0 FR France \n", "11 45.0 FR France \n", "12 74.0 FR France \n", "13 77.0 FR France \n", "14 75.0 FR France \n", "15 61.0 FR France \n", "16 49.0 FR France \n", "17 37.0 FR France \n", "18 36.0 FR France \n", "19 46.0 FR France \n", "20 34.0 FR France \n", "21 20.0 FR France \n", "22 20.0 FR France \n", "23 14.0 FR France \n", "24 13.0 FR France \n", "25 13.0 FR France \n", "26 10.0 FR France \n", "27 16.0 FR France \n", "28 17.0 FR France \n", "29 17.0 FR France \n", "... ... ... ... \n", "1862 59.0 FR France \n", "1863 64.0 FR France \n", "1864 97.0 FR France \n", "1865 93.0 FR France \n", "1866 80.0 FR France \n", "1867 116.0 FR France \n", "1868 149.0 FR France \n", "1869 281.0 FR France \n", "1870 395.0 FR France \n", "1871 485.0 FR France \n", "1872 544.0 FR France \n", "1873 689.0 FR France \n", "1874 722.0 FR France \n", "1875 762.0 FR France \n", "1876 926.0 FR France \n", "1877 1113.0 FR France \n", "1878 1236.0 FR France \n", "1879 832.0 FR France \n", "1880 459.0 FR France \n", "1881 207.0 FR France \n", "1882 190.0 FR France \n", "1883 198.0 FR France \n", "1884 224.0 FR France \n", "1885 266.0 FR France \n", "1886 219.0 FR France \n", "1887 176.0 FR France \n", "1888 163.0 FR France \n", "1889 195.0 FR France \n", "1890 308.0 FR France \n", "1891 213.0 FR France \n", "\n", "[1892 rows x 10 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(fileName, 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 }