{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 1, "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": 8, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://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": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020213231856812351.024785.02819.037.0FRFrance
120213131914413861.024427.02921.037.0FRFrance
22021303139919695.018287.02114.028.0FRFrance
32021293136269618.017634.02115.027.0FRFrance
4202128386365430.011842.0138.018.0FRFrance
52021273106936838.014548.01610.022.0FRFrance
6202126370864109.010063.0116.016.0FRFrance
7202125379425540.010344.0128.016.0FRFrance
8202124348553011.06699.074.010.0FRFrance
9202123367104455.08965.0107.013.0FRFrance
10202122378795495.010263.0128.016.0FRFrance
11202121378275403.010251.0128.016.0FRFrance
122021203102787540.013016.01612.020.0FRFrance
13202119395396860.012218.01410.018.0FRFrance
142021183121359165.015105.01814.022.0FRFrance
152021173120588891.015225.01813.023.0FRFrance
1620211631650512735.020275.02519.031.0FRFrance
1720211531930615398.023214.02923.035.0FRFrance
1820211432107317099.025047.03226.038.0FRFrance
1920211332641322094.030732.04033.047.0FRFrance
2020211233065825919.035397.04639.053.0FRFrance
2120211132498820718.029258.03832.044.0FRFrance
2220211031953915951.023127.03025.035.0FRFrance
2320210931757213926.021218.02721.033.0FRFrance
2420210832088216907.024857.03226.038.0FRFrance
2520210732239318303.026483.03428.040.0FRFrance
2620210632318319134.027232.03529.041.0FRFrance
2720210532242618445.026407.03428.040.0FRFrance
2820210432580421491.030117.03932.046.0FRFrance
2920210332181017894.025726.03327.039.0FRFrance
.................................
189019852132609619621.032571.04735.059.0FRFrance
189119852032789620885.034907.05138.064.0FRFrance
189219851934315432821.053487.07859.097.0FRFrance
189319851834055529935.051175.07455.093.0FRFrance
189419851733405324366.043740.06244.080.0FRFrance
189519851635036236451.064273.09166.0116.0FRFrance
189619851536388145538.082224.011683.0149.0FRFrance
18971985143134545114400.0154690.0244207.0281.0FRFrance
18981985133197206176080.0218332.0357319.0395.0FRFrance
18991985123245240223304.0267176.0445405.0485.0FRFrance
19001985113276205252399.0300011.0501458.0544.0FRFrance
19011985103353231326279.0380183.0640591.0689.0FRFrance
19021985093369895341109.0398681.0670618.0722.0FRFrance
19031985083389886359529.0420243.0707652.0762.0FRFrance
19041985073471852432599.0511105.0855784.0926.0FRFrance
19051985063565825518011.0613639.01026939.01113.0FRFrance
19061985053637302592795.0681809.011551074.01236.0FRFrance
19071985043424937390794.0459080.0770708.0832.0FRFrance
19081985033213901174689.0253113.0388317.0459.0FRFrance
190919850239758680949.0114223.0177147.0207.0FRFrance
191019850138548965918.0105060.0155120.0190.0FRFrance
191119845238483060602.0109058.0154110.0198.0FRFrance
1912198451310172680242.0123210.0185146.0224.0FRFrance
19131984503123680101401.0145959.0225184.0266.0FRFrance
1914198449310107381684.0120462.0184149.0219.0FRFrance
191519844837862060634.096606.0143110.0176.0FRFrance
191619844737202954274.089784.013199.0163.0FRFrance
191719844638733067686.0106974.0159123.0195.0FRFrance
19181984453135223101414.0169032.0246184.0308.0FRFrance
191919844436842220056.0116788.012537.0213.0FRFrance
\n", "

1920 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202132 3 18568 12351.0 24785.0 28 19.0 \n", "1 202131 3 19144 13861.0 24427.0 29 21.0 \n", "2 202130 3 13991 9695.0 18287.0 21 14.0 \n", "3 202129 3 13626 9618.0 17634.0 21 15.0 \n", "4 202128 3 8636 5430.0 11842.0 13 8.0 \n", "5 202127 3 10693 6838.0 14548.0 16 10.0 \n", "6 202126 3 7086 4109.0 10063.0 11 6.0 \n", "7 202125 3 7942 5540.0 10344.0 12 8.0 \n", "8 202124 3 4855 3011.0 6699.0 7 4.0 \n", "9 202123 3 6710 4455.0 8965.0 10 7.0 \n", "10 202122 3 7879 5495.0 10263.0 12 8.0 \n", "11 202121 3 7827 5403.0 10251.0 12 8.0 \n", "12 202120 3 10278 7540.0 13016.0 16 12.0 \n", "13 202119 3 9539 6860.0 12218.0 14 10.0 \n", "14 202118 3 12135 9165.0 15105.0 18 14.0 \n", "15 202117 3 12058 8891.0 15225.0 18 13.0 \n", "16 202116 3 16505 12735.0 20275.0 25 19.0 \n", "17 202115 3 19306 15398.0 23214.0 29 23.0 \n", "18 202114 3 21073 17099.0 25047.0 32 26.0 \n", "19 202113 3 26413 22094.0 30732.0 40 33.0 \n", "20 202112 3 30658 25919.0 35397.0 46 39.0 \n", "21 202111 3 24988 20718.0 29258.0 38 32.0 \n", "22 202110 3 19539 15951.0 23127.0 30 25.0 \n", "23 202109 3 17572 13926.0 21218.0 27 21.0 \n", "24 202108 3 20882 16907.0 24857.0 32 26.0 \n", "25 202107 3 22393 18303.0 26483.0 34 28.0 \n", "26 202106 3 23183 19134.0 27232.0 35 29.0 \n", "27 202105 3 22426 18445.0 26407.0 34 28.0 \n", "28 202104 3 25804 21491.0 30117.0 39 32.0 \n", "29 202103 3 21810 17894.0 25726.0 33 27.0 \n", "... ... ... ... ... ... ... ... \n", "1890 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1891 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1892 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1893 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1894 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1895 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1896 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1897 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1898 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1899 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1900 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1901 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1902 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1903 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1904 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1905 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1906 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1907 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1908 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1909 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1910 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1911 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1912 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1913 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1914 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1915 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1916 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1917 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1918 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1919 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 37.0 FR France \n", "1 37.0 FR France \n", "2 28.0 FR France \n", "3 27.0 FR France \n", "4 18.0 FR France \n", "5 22.0 FR France \n", "6 16.0 FR France \n", "7 16.0 FR France \n", "8 10.0 FR France \n", "9 13.0 FR France \n", "10 16.0 FR France \n", "11 16.0 FR France \n", "12 20.0 FR France \n", "13 18.0 FR France \n", "14 22.0 FR France \n", "15 23.0 FR France \n", "16 31.0 FR France \n", "17 35.0 FR France \n", "18 38.0 FR France \n", "19 47.0 FR France \n", "20 53.0 FR France \n", "21 44.0 FR France \n", "22 35.0 FR France \n", "23 33.0 FR France \n", "24 38.0 FR France \n", "25 40.0 FR France \n", "26 41.0 FR France \n", "27 40.0 FR France \n", "28 46.0 FR France \n", "29 39.0 FR France \n", "... ... ... ... \n", "1890 59.0 FR France \n", "1891 64.0 FR France \n", "1892 97.0 FR France \n", "1893 93.0 FR France \n", "1894 80.0 FR France \n", "1895 116.0 FR France \n", "1896 149.0 FR France \n", "1897 281.0 FR France \n", "1898 395.0 FR France \n", "1899 485.0 FR France \n", "1900 544.0 FR France \n", "1901 689.0 FR France \n", "1902 722.0 FR France \n", "1903 762.0 FR France \n", "1904 926.0 FR France \n", "1905 1113.0 FR France \n", "1906 1236.0 FR France \n", "1907 832.0 FR France \n", "1908 459.0 FR France \n", "1909 207.0 FR France \n", "1910 190.0 FR France \n", "1911 198.0 FR France \n", "1912 224.0 FR France \n", "1913 266.0 FR France \n", "1914 219.0 FR France \n", "1915 176.0 FR France \n", "1916 163.0 FR France \n", "1917 195.0 FR France \n", "1918 308.0 FR France \n", "1919 213.0 FR France \n", "\n", "[1920 rows x 10 columns]" ] }, "execution_count": 9, "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": {}, "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": {}, "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": {}, "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 }