{ "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": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Afin de se prémunir contre un éventuel changement d'adresse, changement de mise en forme voire disparition des données en ligne, nous téléchargeons une copie locale de ce fichier de données. Le téléchargement ne s'effectuera pas à chaque fois, mais uniquement si le fichier de données est absent de notre machine." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file=\"incidence-PAY-3.csv\"\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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202016300.00.000.00.0FRFrance
1202015300.00.000.00.0FRFrance
2202014300.00.000.00.0FRFrance
3202013300.00.000.00.0FRFrance
4202012383215873.010769.0139.017.0FRFrance
5202011310170493652.0109756.0154142.0166.0FRFrance
6202010310497796650.0113304.0159146.0172.0FRFrance
72020093110696102066.0119326.0168155.0181.0FRFrance
82020083143753133984.0153522.0218203.0233.0FRFrance
92020073183610172812.0194408.0279263.0295.0FRFrance
102020063206669195481.0217857.0314297.0331.0FRFrance
112020053187957177445.0198469.0285269.0301.0FRFrance
122020043122331113492.0131170.0186173.0199.0FRFrance
1320200337841371330.085496.0119108.0130.0FRFrance
1420200235361447654.059574.08172.090.0FRFrance
1520200133685031608.042092.05648.064.0FRFrance
1620195232813523220.033050.04336.050.0FRFrance
1720195132978625042.034530.04538.052.0FRFrance
1820195033422329156.039290.05244.060.0FRFrance
1920194932566221414.029910.03933.045.0FRFrance
2020194832236718055.026679.03427.041.0FRFrance
2120194731866914759.022579.02822.034.0FRFrance
2220194631603012567.019493.02419.029.0FRFrance
232019453101387160.013116.01510.020.0FRFrance
24201944378225010.010634.0128.016.0FRFrance
25201943394876448.012526.0149.019.0FRFrance
26201942377475243.010251.0128.016.0FRFrance
27201941371224720.09524.0117.015.0FRFrance
28201940385055784.011226.0139.017.0FRFrance
29201939370914462.09720.0117.015.0FRFrance
.................................
182119852132609619621.032571.04735.059.0FRFrance
182219852032789620885.034907.05138.064.0FRFrance
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182719851536388145538.082224.011683.0149.0FRFrance
18281985143134545114400.0154690.0244207.0281.0FRFrance
18291985133197206176080.0218332.0357319.0395.0FRFrance
18301985123245240223304.0267176.0445405.0485.0FRFrance
18311985113276205252399.0300011.0501458.0544.0FRFrance
18321985103353231326279.0380183.0640591.0689.0FRFrance
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184019850239758680949.0114223.0177147.0207.0FRFrance
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1845198449310107381684.0120462.0184149.0219.0FRFrance
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184719844737202954274.089784.013199.0163.0FRFrance
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185019844436842220056.0116788.012537.0213.0FRFrance
\n", "

1851 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202016 3 0 0.0 0.0 0 0.0 \n", "1 202015 3 0 0.0 0.0 0 0.0 \n", "2 202014 3 0 0.0 0.0 0 0.0 \n", "3 202013 3 0 0.0 0.0 0 0.0 \n", "4 202012 3 8321 5873.0 10769.0 13 9.0 \n", "5 202011 3 101704 93652.0 109756.0 154 142.0 \n", "6 202010 3 104977 96650.0 113304.0 159 146.0 \n", "7 202009 3 110696 102066.0 119326.0 168 155.0 \n", "8 202008 3 143753 133984.0 153522.0 218 203.0 \n", "9 202007 3 183610 172812.0 194408.0 279 263.0 \n", "10 202006 3 206669 195481.0 217857.0 314 297.0 \n", "11 202005 3 187957 177445.0 198469.0 285 269.0 \n", "12 202004 3 122331 113492.0 131170.0 186 173.0 \n", "13 202003 3 78413 71330.0 85496.0 119 108.0 \n", "14 202002 3 53614 47654.0 59574.0 81 72.0 \n", "15 202001 3 36850 31608.0 42092.0 56 48.0 \n", "16 201952 3 28135 23220.0 33050.0 43 36.0 \n", "17 201951 3 29786 25042.0 34530.0 45 38.0 \n", "18 201950 3 34223 29156.0 39290.0 52 44.0 \n", "19 201949 3 25662 21414.0 29910.0 39 33.0 \n", "20 201948 3 22367 18055.0 26679.0 34 27.0 \n", "21 201947 3 18669 14759.0 22579.0 28 22.0 \n", "22 201946 3 16030 12567.0 19493.0 24 19.0 \n", "23 201945 3 10138 7160.0 13116.0 15 10.0 \n", "24 201944 3 7822 5010.0 10634.0 12 8.0 \n", "25 201943 3 9487 6448.0 12526.0 14 9.0 \n", "26 201942 3 7747 5243.0 10251.0 12 8.0 \n", "27 201941 3 7122 4720.0 9524.0 11 7.0 \n", "28 201940 3 8505 5784.0 11226.0 13 9.0 \n", "29 201939 3 7091 4462.0 9720.0 11 7.0 \n", "... ... ... ... ... ... ... ... \n", "1821 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1822 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1823 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1824 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1825 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1826 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1827 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1828 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1829 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1830 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1831 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1832 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1833 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1834 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1835 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1836 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1837 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1838 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1839 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1840 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1841 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1842 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1843 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1844 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1845 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1846 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1847 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1848 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1849 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1850 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 0.0 FR France \n", "1 0.0 FR France \n", "2 0.0 FR France \n", "3 0.0 FR France \n", "4 17.0 FR France \n", "5 166.0 FR France \n", "6 172.0 FR France \n", "7 181.0 FR France \n", "8 233.0 FR France \n", "9 295.0 FR France \n", "10 331.0 FR France \n", "11 301.0 FR France \n", "12 199.0 FR France \n", "13 130.0 FR France \n", "14 90.0 FR France \n", "15 64.0 FR France \n", "16 50.0 FR France \n", "17 52.0 FR France \n", "18 60.0 FR France \n", "19 45.0 FR France \n", "20 41.0 FR France \n", "21 34.0 FR France \n", "22 29.0 FR France \n", "23 20.0 FR France \n", "24 16.0 FR France \n", "25 19.0 FR France \n", "26 16.0 FR France \n", "27 15.0 FR France \n", "28 17.0 FR France \n", "29 15.0 FR France \n", "... ... ... ... \n", "1821 59.0 FR France \n", "1822 64.0 FR France \n", "1823 97.0 FR France \n", "1824 93.0 FR France \n", "1825 80.0 FR France \n", "1826 116.0 FR France \n", "1827 149.0 FR France \n", "1828 281.0 FR France \n", "1829 395.0 FR France \n", "1830 485.0 FR France \n", "1831 544.0 FR France \n", "1832 689.0 FR France \n", "1833 722.0 FR France \n", "1834 762.0 FR France \n", "1835 926.0 FR France \n", "1836 1113.0 FR France \n", "1837 1236.0 FR France \n", "1838 832.0 FR France \n", "1839 459.0 FR France \n", "1840 207.0 FR France \n", "1841 190.0 FR France \n", "1842 198.0 FR France \n", "1843 224.0 FR France \n", "1844 266.0 FR France \n", "1845 219.0 FR France \n", "1846 176.0 FR France \n", "1847 163.0 FR France \n", "1848 195.0 FR France \n", "1849 308.0 FR France \n", "1850 213.0 FR France \n", "\n", "[1851 rows x 10 columns]" ] }, "execution_count": 5, "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": {}, "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 }