{ "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": [ "Une copie locale des données est effectuée. Cela permet d'éviter d'éviter les problèmes engendrés par un éventuel changement de l'adresse url ou des données qu'elle contient." ] }, { "cell_type": "code", "execution_count": 6, "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": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202125374824762.010202.0117.015.0FRFrance
1202124349373062.06812.074.010.0FRFrance
2202123367104455.08965.0107.013.0FRFrance
3202122378795495.010263.0128.016.0FRFrance
4202121378275403.010251.0128.016.0FRFrance
52021203102787540.013016.01612.020.0FRFrance
6202119395396860.012218.01410.018.0FRFrance
72021183121359165.015105.01814.022.0FRFrance
82021173120588891.015225.01813.023.0FRFrance
920211631650512735.020275.02519.031.0FRFrance
1020211531930615398.023214.02923.035.0FRFrance
1120211432107317099.025047.03226.038.0FRFrance
1220211332641322094.030732.04033.047.0FRFrance
1320211233065825919.035397.04639.053.0FRFrance
1420211132498820718.029258.03832.044.0FRFrance
1520211031953915951.023127.03025.035.0FRFrance
1620210931757213926.021218.02721.033.0FRFrance
1720210832088216907.024857.03226.038.0FRFrance
1820210732239318303.026483.03428.040.0FRFrance
1920210632318319134.027232.03529.041.0FRFrance
2020210532242618445.026407.03428.040.0FRFrance
2120210432580421491.030117.03932.046.0FRFrance
2220210332181017894.025726.03327.039.0FRFrance
2320210231732013906.020734.02621.031.0FRFrance
2420210132179917778.025820.03327.039.0FRFrance
2520205332122016498.025942.03225.039.0FRFrance
2620205231642812285.020571.02519.031.0FRFrance
2720205132161917370.025868.03327.039.0FRFrance
2820205031684513220.020470.02620.032.0FRFrance
292020493129399923.015955.02015.025.0FRFrance
.................................
188319852132609619621.032571.04735.059.0FRFrance
188419852032789620885.034907.05138.064.0FRFrance
188519851934315432821.053487.07859.097.0FRFrance
188619851834055529935.051175.07455.093.0FRFrance
188719851733405324366.043740.06244.080.0FRFrance
188819851635036236451.064273.09166.0116.0FRFrance
188919851536388145538.082224.011683.0149.0FRFrance
18901985143134545114400.0154690.0244207.0281.0FRFrance
18911985133197206176080.0218332.0357319.0395.0FRFrance
18921985123245240223304.0267176.0445405.0485.0FRFrance
18931985113276205252399.0300011.0501458.0544.0FRFrance
18941985103353231326279.0380183.0640591.0689.0FRFrance
18951985093369895341109.0398681.0670618.0722.0FRFrance
18961985083389886359529.0420243.0707652.0762.0FRFrance
18971985073471852432599.0511105.0855784.0926.0FRFrance
18981985063565825518011.0613639.01026939.01113.0FRFrance
18991985053637302592795.0681809.011551074.01236.0FRFrance
19001985043424937390794.0459080.0770708.0832.0FRFrance
19011985033213901174689.0253113.0388317.0459.0FRFrance
190219850239758680949.0114223.0177147.0207.0FRFrance
190319850138548965918.0105060.0155120.0190.0FRFrance
190419845238483060602.0109058.0154110.0198.0FRFrance
1905198451310172680242.0123210.0185146.0224.0FRFrance
19061984503123680101401.0145959.0225184.0266.0FRFrance
1907198449310107381684.0120462.0184149.0219.0FRFrance
190819844837862060634.096606.0143110.0176.0FRFrance
190919844737202954274.089784.013199.0163.0FRFrance
191019844638733067686.0106974.0159123.0195.0FRFrance
19111984453135223101414.0169032.0246184.0308.0FRFrance
191219844436842220056.0116788.012537.0213.0FRFrance
\n", "

1913 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202125 3 7482 4762.0 10202.0 11 7.0 \n", "1 202124 3 4937 3062.0 6812.0 7 4.0 \n", "2 202123 3 6710 4455.0 8965.0 10 7.0 \n", "3 202122 3 7879 5495.0 10263.0 12 8.0 \n", "4 202121 3 7827 5403.0 10251.0 12 8.0 \n", "5 202120 3 10278 7540.0 13016.0 16 12.0 \n", "6 202119 3 9539 6860.0 12218.0 14 10.0 \n", "7 202118 3 12135 9165.0 15105.0 18 14.0 \n", "8 202117 3 12058 8891.0 15225.0 18 13.0 \n", "9 202116 3 16505 12735.0 20275.0 25 19.0 \n", "10 202115 3 19306 15398.0 23214.0 29 23.0 \n", "11 202114 3 21073 17099.0 25047.0 32 26.0 \n", "12 202113 3 26413 22094.0 30732.0 40 33.0 \n", "13 202112 3 30658 25919.0 35397.0 46 39.0 \n", "14 202111 3 24988 20718.0 29258.0 38 32.0 \n", "15 202110 3 19539 15951.0 23127.0 30 25.0 \n", "16 202109 3 17572 13926.0 21218.0 27 21.0 \n", "17 202108 3 20882 16907.0 24857.0 32 26.0 \n", "18 202107 3 22393 18303.0 26483.0 34 28.0 \n", "19 202106 3 23183 19134.0 27232.0 35 29.0 \n", "20 202105 3 22426 18445.0 26407.0 34 28.0 \n", "21 202104 3 25804 21491.0 30117.0 39 32.0 \n", "22 202103 3 21810 17894.0 25726.0 33 27.0 \n", "23 202102 3 17320 13906.0 20734.0 26 21.0 \n", "24 202101 3 21799 17778.0 25820.0 33 27.0 \n", "25 202053 3 21220 16498.0 25942.0 32 25.0 \n", "26 202052 3 16428 12285.0 20571.0 25 19.0 \n", "27 202051 3 21619 17370.0 25868.0 33 27.0 \n", "28 202050 3 16845 13220.0 20470.0 26 20.0 \n", "29 202049 3 12939 9923.0 15955.0 20 15.0 \n", "... ... ... ... ... ... ... ... \n", "1883 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1884 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1885 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1886 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1887 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1888 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1889 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1890 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1891 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1892 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1893 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1894 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1895 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1896 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1897 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1898 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1899 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1900 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1901 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1902 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1903 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1904 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1905 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1906 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1907 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1908 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1909 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1910 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1911 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1912 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 15.0 FR France \n", "1 10.0 FR France \n", "2 13.0 FR France \n", "3 16.0 FR France \n", "4 16.0 FR France \n", "5 20.0 FR France \n", "6 18.0 FR France \n", "7 22.0 FR France \n", "8 23.0 FR France \n", "9 31.0 FR France \n", "10 35.0 FR France \n", "11 38.0 FR France \n", "12 47.0 FR France \n", "13 53.0 FR France \n", "14 44.0 FR France \n", "15 35.0 FR France \n", "16 33.0 FR France \n", "17 38.0 FR France \n", "18 40.0 FR France \n", "19 41.0 FR France \n", "20 40.0 FR France \n", "21 46.0 FR France \n", "22 39.0 FR France \n", "23 31.0 FR France \n", "24 39.0 FR France \n", "25 39.0 FR France \n", "26 31.0 FR France \n", "27 39.0 FR France \n", "28 32.0 FR France \n", "29 25.0 FR France \n", "... ... ... ... \n", "1883 59.0 FR France \n", "1884 64.0 FR France \n", "1885 97.0 FR France \n", "1886 93.0 FR France \n", "1887 80.0 FR France \n", "1888 116.0 FR France \n", "1889 149.0 FR France \n", "1890 281.0 FR France \n", "1891 395.0 FR France \n", "1892 485.0 FR France \n", "1893 544.0 FR France \n", "1894 689.0 FR France \n", "1895 722.0 FR France \n", "1896 762.0 FR France \n", "1897 926.0 FR France \n", "1898 1113.0 FR France \n", "1899 1236.0 FR France \n", "1900 832.0 FR France \n", "1901 459.0 FR France \n", "1902 207.0 FR France \n", "1903 190.0 FR France \n", "1904 198.0 FR France \n", "1905 224.0 FR France \n", "1906 266.0 FR France \n", "1907 219.0 FR France \n", "1908 176.0 FR France \n", "1909 163.0 FR France \n", "1910 195.0 FR France \n", "1911 308.0 FR France \n", "1912 213.0 FR France \n", "\n", "[1913 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "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": { "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 }