{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os.path\n", "import urllib.request" ] }, { "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": [ "On vérifie que les données sont présentes en local sinon on les télécharge au lien sentiweb.\n", "Ceci pour assurer une cohérence des données (pas de mise à jour non maitrisée)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", "if not os.path.isfile(\"incidence-PAY-3.csv\"):\n", " urllib.request.urlretrieve(data_url, '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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020204534323637452.049020.06657.075.0FRFrance
120204434456138515.050607.06859.077.0FRFrance
220204334373737523.049951.06657.075.0FRFrance
320204233514529812.040478.05345.061.0FRFrance
420204132787723206.032548.04235.049.0FRFrance
520204032044316381.024505.03125.037.0FRFrance
620203931981015900.023720.03024.036.0FRFrance
720203832556221142.029982.03932.046.0FRFrance
820203731848514649.022321.02822.034.0FRFrance
92020363103907646.013134.01612.020.0FRFrance
10202035399186842.012994.01510.020.0FRFrance
11202034360843090.09078.094.014.0FRFrance
12202033361063411.08801.095.013.0FRFrance
13202032359183330.08506.095.013.0FRFrance
14202031343512269.06433.074.010.0FRFrance
15202030381795442.010916.0128.016.0FRFrance
16202029386875860.011514.0139.017.0FRFrance
17202028383405701.010979.0139.017.0FRFrance
18202027340662406.05726.063.09.0FRFrance
19202026340392389.05689.063.09.0FRFrance
20202025328531488.04218.042.06.0FRFrance
21202024330581690.04426.053.07.0FRFrance
22202023341682468.05868.063.09.0FRFrance
23202022335801947.05213.053.07.0FRFrance
24202021361144026.08202.096.012.0FRFrance
25202020393156775.011855.01410.018.0FRFrance
262020193116798722.014636.01814.022.0FRFrance
2720201831639812851.019945.02520.030.0FRFrance
2820201731808214454.021710.02721.033.0FRFrance
2920201632416519893.028437.03731.043.0FRFrance
.................................
185019852132609619621.032571.04735.059.0FRFrance
185119852032789620885.034907.05138.064.0FRFrance
185219851934315432821.053487.07859.097.0FRFrance
185319851834055529935.051175.07455.093.0FRFrance
185419851733405324366.043740.06244.080.0FRFrance
185519851635036236451.064273.09166.0116.0FRFrance
185619851536388145538.082224.011683.0149.0FRFrance
18571985143134545114400.0154690.0244207.0281.0FRFrance
18581985133197206176080.0218332.0357319.0395.0FRFrance
18591985123245240223304.0267176.0445405.0485.0FRFrance
18601985113276205252399.0300011.0501458.0544.0FRFrance
18611985103353231326279.0380183.0640591.0689.0FRFrance
18621985093369895341109.0398681.0670618.0722.0FRFrance
18631985083389886359529.0420243.0707652.0762.0FRFrance
18641985073471852432599.0511105.0855784.0926.0FRFrance
18651985063565825518011.0613639.01026939.01113.0FRFrance
18661985053637302592795.0681809.011551074.01236.0FRFrance
18671985043424937390794.0459080.0770708.0832.0FRFrance
18681985033213901174689.0253113.0388317.0459.0FRFrance
186919850239758680949.0114223.0177147.0207.0FRFrance
187019850138548965918.0105060.0155120.0190.0FRFrance
187119845238483060602.0109058.0154110.0198.0FRFrance
1872198451310172680242.0123210.0185146.0224.0FRFrance
18731984503123680101401.0145959.0225184.0266.0FRFrance
1874198449310107381684.0120462.0184149.0219.0FRFrance
187519844837862060634.096606.0143110.0176.0FRFrance
187619844737202954274.089784.013199.0163.0FRFrance
187719844638733067686.0106974.0159123.0195.0FRFrance
18781984453135223101414.0169032.0246184.0308.0FRFrance
187919844436842220056.0116788.012537.0213.0FRFrance
\n", "

1880 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202045 3 43236 37452.0 49020.0 66 57.0 \n", "1 202044 3 44561 38515.0 50607.0 68 59.0 \n", "2 202043 3 43737 37523.0 49951.0 66 57.0 \n", "3 202042 3 35145 29812.0 40478.0 53 45.0 \n", "4 202041 3 27877 23206.0 32548.0 42 35.0 \n", "5 202040 3 20443 16381.0 24505.0 31 25.0 \n", "6 202039 3 19810 15900.0 23720.0 30 24.0 \n", "7 202038 3 25562 21142.0 29982.0 39 32.0 \n", "8 202037 3 18485 14649.0 22321.0 28 22.0 \n", "9 202036 3 10390 7646.0 13134.0 16 12.0 \n", "10 202035 3 9918 6842.0 12994.0 15 10.0 \n", "11 202034 3 6084 3090.0 9078.0 9 4.0 \n", "12 202033 3 6106 3411.0 8801.0 9 5.0 \n", "13 202032 3 5918 3330.0 8506.0 9 5.0 \n", "14 202031 3 4351 2269.0 6433.0 7 4.0 \n", "15 202030 3 8179 5442.0 10916.0 12 8.0 \n", "16 202029 3 8687 5860.0 11514.0 13 9.0 \n", "17 202028 3 8340 5701.0 10979.0 13 9.0 \n", "18 202027 3 4066 2406.0 5726.0 6 3.0 \n", "19 202026 3 4039 2389.0 5689.0 6 3.0 \n", "20 202025 3 2853 1488.0 4218.0 4 2.0 \n", "21 202024 3 3058 1690.0 4426.0 5 3.0 \n", "22 202023 3 4168 2468.0 5868.0 6 3.0 \n", "23 202022 3 3580 1947.0 5213.0 5 3.0 \n", "24 202021 3 6114 4026.0 8202.0 9 6.0 \n", "25 202020 3 9315 6775.0 11855.0 14 10.0 \n", "26 202019 3 11679 8722.0 14636.0 18 14.0 \n", "27 202018 3 16398 12851.0 19945.0 25 20.0 \n", "28 202017 3 18082 14454.0 21710.0 27 21.0 \n", "29 202016 3 24165 19893.0 28437.0 37 31.0 \n", "... ... ... ... ... ... ... ... \n", "1850 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1851 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1852 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1853 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1854 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1855 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1856 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1857 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1858 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1859 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1860 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1861 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1862 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1863 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1864 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1865 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1866 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1867 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1868 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1869 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1870 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1871 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1872 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1873 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1874 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1875 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1876 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1877 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1878 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1879 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 75.0 FR France \n", "1 77.0 FR France \n", "2 75.0 FR France \n", "3 61.0 FR France \n", "4 49.0 FR France \n", "5 37.0 FR France \n", "6 36.0 FR France \n", "7 46.0 FR France \n", "8 34.0 FR France \n", "9 20.0 FR France \n", "10 20.0 FR France \n", "11 14.0 FR France \n", "12 13.0 FR France \n", "13 13.0 FR France \n", "14 10.0 FR France \n", "15 16.0 FR France \n", "16 17.0 FR France \n", "17 17.0 FR France \n", "18 9.0 FR France \n", "19 9.0 FR France \n", "20 6.0 FR France \n", "21 7.0 FR France \n", "22 9.0 FR France \n", "23 7.0 FR France \n", "24 12.0 FR France \n", "25 18.0 FR France \n", "26 22.0 FR France \n", "27 30.0 FR France \n", "28 33.0 FR France \n", "29 43.0 FR France \n", "... ... ... ... \n", "1850 59.0 FR France \n", "1851 64.0 FR France \n", "1852 97.0 FR France \n", "1853 93.0 FR France \n", "1854 80.0 FR France \n", "1855 116.0 FR France \n", "1856 149.0 FR France \n", "1857 281.0 FR France \n", "1858 395.0 FR France \n", "1859 485.0 FR France \n", "1860 544.0 FR France \n", "1861 689.0 FR France \n", "1862 722.0 FR France \n", "1863 762.0 FR France \n", "1864 926.0 FR France \n", "1865 1113.0 FR France \n", "1866 1236.0 FR France \n", "1867 832.0 FR France \n", "1868 459.0 FR France \n", "1869 207.0 FR France \n", "1870 190.0 FR France \n", "1871 198.0 FR France \n", "1872 224.0 FR France \n", "1873 266.0 FR France \n", "1874 219.0 FR France \n", "1875 176.0 FR France \n", "1876 163.0 FR France \n", "1877 195.0 FR France \n", "1878 308.0 FR France \n", "1879 213.0 FR France \n", "\n", "[1880 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(\"incidence-PAY-3.csv\", 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 }