{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import os\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": 4, "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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données. Nous téléchargons le fichier des données de l'incidence du syndrome grippal manuellement depuis http://www.sentiweb.fr/ . Il faut passer par les menus \"Surveillance continue\" - \"Base de données\" - \"Accès aux données\"\n", "et cliquer sur l'onglet \"Télécharger\", puis choisir les données au format CSV pour la France Métropolitaine. Nous le placons ensuite dans le même dossier que mon document Jupyter." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "file_adress = \"incidence-PAY-3.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions que le fichier existe à la bonne adresse. Sinon, nous le téléchargons depuis l'url.\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": [ { "name": "stdout", "output_type": "stream", "text": [ "Data imported via the local file.\n" ] }, { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020213731391010584.017236.02116.026.0FRFrance
12021363102897505.013073.01612.020.0FRFrance
22021353126099282.015936.01914.024.0FRFrance
32021343130159485.016545.02015.025.0FRFrance
42021333103927042.013742.01611.021.0FRFrance
520213231558611009.020163.02417.031.0FRFrance
620213131885513664.024046.02921.037.0FRFrance
72021303139919695.018287.02114.028.0FRFrance
82021293136269618.017634.02115.027.0FRFrance
9202128386365430.011842.0138.018.0FRFrance
102021273106936838.014548.01610.022.0FRFrance
11202126370864109.010063.0116.016.0FRFrance
12202125379425540.010344.0128.016.0FRFrance
13202124348553011.06699.074.010.0FRFrance
14202123367104455.08965.0107.013.0FRFrance
15202122378795495.010263.0128.016.0FRFrance
16202121378275403.010251.0128.016.0FRFrance
172021203102787540.013016.01612.020.0FRFrance
18202119395396860.012218.01410.018.0FRFrance
192021183121359165.015105.01814.022.0FRFrance
202021173120588891.015225.01813.023.0FRFrance
2120211631650512735.020275.02519.031.0FRFrance
2220211531930615398.023214.02923.035.0FRFrance
2320211432107317099.025047.03226.038.0FRFrance
2420211332641322094.030732.04033.047.0FRFrance
2520211233065825919.035397.04639.053.0FRFrance
2620211132498820718.029258.03832.044.0FRFrance
2720211031953915951.023127.03025.035.0FRFrance
2820210931757213926.021218.02721.033.0FRFrance
2920210832088216907.024857.03226.038.0FRFrance
.................................
189519852132609619621.032571.04735.059.0FRFrance
189619852032789620885.034907.05138.064.0FRFrance
189719851934315432821.053487.07859.097.0FRFrance
189819851834055529935.051175.07455.093.0FRFrance
189919851733405324366.043740.06244.080.0FRFrance
190019851635036236451.064273.09166.0116.0FRFrance
190119851536388145538.082224.011683.0149.0FRFrance
19021985143134545114400.0154690.0244207.0281.0FRFrance
19031985133197206176080.0218332.0357319.0395.0FRFrance
19041985123245240223304.0267176.0445405.0485.0FRFrance
19051985113276205252399.0300011.0501458.0544.0FRFrance
19061985103353231326279.0380183.0640591.0689.0FRFrance
19071985093369895341109.0398681.0670618.0722.0FRFrance
19081985083389886359529.0420243.0707652.0762.0FRFrance
19091985073471852432599.0511105.0855784.0926.0FRFrance
19101985063565825518011.0613639.01026939.01113.0FRFrance
19111985053637302592795.0681809.011551074.01236.0FRFrance
19121985043424937390794.0459080.0770708.0832.0FRFrance
19131985033213901174689.0253113.0388317.0459.0FRFrance
191419850239758680949.0114223.0177147.0207.0FRFrance
191519850138548965918.0105060.0155120.0190.0FRFrance
191619845238483060602.0109058.0154110.0198.0FRFrance
1917198451310172680242.0123210.0185146.0224.0FRFrance
19181984503123680101401.0145959.0225184.0266.0FRFrance
1919198449310107381684.0120462.0184149.0219.0FRFrance
192019844837862060634.096606.0143110.0176.0FRFrance
192119844737202954274.089784.013199.0163.0FRFrance
192219844638733067686.0106974.0159123.0195.0FRFrance
19231984453135223101414.0169032.0246184.0308.0FRFrance
192419844436842220056.0116788.012537.0213.0FRFrance
\n", "

1925 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202137 3 13910 10584.0 17236.0 21 16.0 \n", "1 202136 3 10289 7505.0 13073.0 16 12.0 \n", "2 202135 3 12609 9282.0 15936.0 19 14.0 \n", "3 202134 3 13015 9485.0 16545.0 20 15.0 \n", "4 202133 3 10392 7042.0 13742.0 16 11.0 \n", "5 202132 3 15586 11009.0 20163.0 24 17.0 \n", "6 202131 3 18855 13664.0 24046.0 29 21.0 \n", "7 202130 3 13991 9695.0 18287.0 21 14.0 \n", "8 202129 3 13626 9618.0 17634.0 21 15.0 \n", "9 202128 3 8636 5430.0 11842.0 13 8.0 \n", "10 202127 3 10693 6838.0 14548.0 16 10.0 \n", "11 202126 3 7086 4109.0 10063.0 11 6.0 \n", "12 202125 3 7942 5540.0 10344.0 12 8.0 \n", "13 202124 3 4855 3011.0 6699.0 7 4.0 \n", "14 202123 3 6710 4455.0 8965.0 10 7.0 \n", "15 202122 3 7879 5495.0 10263.0 12 8.0 \n", "16 202121 3 7827 5403.0 10251.0 12 8.0 \n", "17 202120 3 10278 7540.0 13016.0 16 12.0 \n", "18 202119 3 9539 6860.0 12218.0 14 10.0 \n", "19 202118 3 12135 9165.0 15105.0 18 14.0 \n", "20 202117 3 12058 8891.0 15225.0 18 13.0 \n", "21 202116 3 16505 12735.0 20275.0 25 19.0 \n", "22 202115 3 19306 15398.0 23214.0 29 23.0 \n", "23 202114 3 21073 17099.0 25047.0 32 26.0 \n", "24 202113 3 26413 22094.0 30732.0 40 33.0 \n", "25 202112 3 30658 25919.0 35397.0 46 39.0 \n", "26 202111 3 24988 20718.0 29258.0 38 32.0 \n", "27 202110 3 19539 15951.0 23127.0 30 25.0 \n", "28 202109 3 17572 13926.0 21218.0 27 21.0 \n", "29 202108 3 20882 16907.0 24857.0 32 26.0 \n", "... ... ... ... ... ... ... ... \n", "1895 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1896 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1897 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1898 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1899 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1900 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1901 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1902 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1903 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1904 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1905 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1906 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1907 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1908 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1909 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1910 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1911 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1912 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1913 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1914 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1915 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1916 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1917 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1918 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1919 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1920 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1921 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1922 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1923 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1924 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 26.0 FR France \n", "1 20.0 FR France \n", "2 24.0 FR France \n", "3 25.0 FR France \n", "4 21.0 FR France \n", "5 31.0 FR France \n", "6 37.0 FR France \n", "7 28.0 FR France \n", "8 27.0 FR France \n", "9 18.0 FR France \n", "10 22.0 FR France \n", "11 16.0 FR France \n", "12 16.0 FR France \n", "13 10.0 FR France \n", "14 13.0 FR France \n", "15 16.0 FR France \n", "16 16.0 FR France \n", "17 20.0 FR France \n", "18 18.0 FR France \n", "19 22.0 FR France \n", "20 23.0 FR France \n", "21 31.0 FR France \n", "22 35.0 FR France \n", "23 38.0 FR France \n", "24 47.0 FR France \n", "25 53.0 FR France \n", "26 44.0 FR France \n", "27 35.0 FR France \n", "28 33.0 FR France \n", "29 38.0 FR France \n", "... ... ... ... \n", "1895 59.0 FR France \n", "1896 64.0 FR France \n", "1897 97.0 FR France \n", "1898 93.0 FR France \n", "1899 80.0 FR France \n", "1900 116.0 FR France \n", "1901 149.0 FR France \n", "1902 281.0 FR France \n", "1903 395.0 FR France \n", "1904 485.0 FR France \n", "1905 544.0 FR France \n", "1906 689.0 FR France \n", "1907 722.0 FR France \n", "1908 762.0 FR France \n", "1909 926.0 FR France \n", "1910 1113.0 FR France \n", "1911 1236.0 FR France \n", "1912 832.0 FR France \n", "1913 459.0 FR France \n", "1914 207.0 FR France \n", "1915 190.0 FR France \n", "1916 198.0 FR France \n", "1917 224.0 FR France \n", "1918 266.0 FR France \n", "1919 219.0 FR France \n", "1920 176.0 FR France \n", "1921 163.0 FR France \n", "1922 195.0 FR France \n", "1923 308.0 FR France \n", "1924 213.0 FR France \n", "\n", "[1925 rows x 10 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "if os.path.isfile(file_adress):\n", " raw_data = pd.read_csv(file_adress, skiprows=1)\n", " print(\"Data imported via the local file.\")\n", "else:\n", " raw_data = pd.read_csv(data_url, skiprows=1)\n", " print(\"Data downloaded via the url\")\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 }