{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import csv\n", "import requests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence du syndrome grippal sont téléchargées à partir d'un fichier local \"incidence-Pay-3.csv\". Si le fichier local n'existe pas, les données sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons et enregistrons sous forme d'un fichier en format CSV car rien ne garantit que l'URL reste valable ou que le format des données ne change pas. 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.\n", "\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "File does not exist, load from URL\n" ] } ], "source": [ "if Path('incidence-PAY-3.csv').is_file():\n", " print (\"File exist, load from file\")\n", " data=\"incidence-PAY-3.csv\"\n", "else:\n", " print(\"File does not exist, load from URL\")\n", " data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", " r = requests.get(data_url, allow_redirects=True)\n", " open('incidence-PAY-3.csv', 'wb').write(r.content)\n", " data=\"incidence-PAY-3.csv\"\n" ] }, { "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": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202011310170493652.0109756.0154142.0166.0FRFrance
1202010310497796650.0113304.0159146.0172.0FRFrance
22020093110696102066.0119326.0168155.0181.0FRFrance
32020083143753133984.0153522.0218203.0233.0FRFrance
42020073183610172812.0194408.0279263.0295.0FRFrance
52020063206669195481.0217857.0314297.0331.0FRFrance
62020053187957177445.0198469.0285269.0301.0FRFrance
72020043122331113492.0131170.0186173.0199.0FRFrance
820200337841371330.085496.0119108.0130.0FRFrance
920200235361447654.059574.08172.090.0FRFrance
1020200133685031608.042092.05648.064.0FRFrance
1120195232813523220.033050.04336.050.0FRFrance
1220195132978625042.034530.04538.052.0FRFrance
1320195033422329156.039290.05244.060.0FRFrance
1420194932566221414.029910.03933.045.0FRFrance
1520194832236718055.026679.03427.041.0FRFrance
1620194731866914759.022579.02822.034.0FRFrance
1720194631603012567.019493.02419.029.0FRFrance
182019453101387160.013116.01510.020.0FRFrance
19201944378225010.010634.0128.016.0FRFrance
20201943394876448.012526.0149.019.0FRFrance
21201942377475243.010251.0128.016.0FRFrance
22201941371224720.09524.0117.015.0FRFrance
23201940385055784.011226.0139.017.0FRFrance
24201939370914462.09720.0117.015.0FRFrance
25201938348972891.06903.074.010.0FRFrance
26201937331721367.04977.052.08.0FRFrance
2720193632295728.03862.031.05.0FRFrance
28201935310102.02018.020.04.0FRFrance
2920193431672279.03065.031.05.0FRFrance
.................................
181619852132609619621.032571.04735.059.0FRFrance
181719852032789620885.034907.05138.064.0FRFrance
181819851934315432821.053487.07859.097.0FRFrance
181919851834055529935.051175.07455.093.0FRFrance
182019851733405324366.043740.06244.080.0FRFrance
182119851635036236451.064273.09166.0116.0FRFrance
182219851536388145538.082224.011683.0149.0FRFrance
18231985143134545114400.0154690.0244207.0281.0FRFrance
18241985133197206176080.0218332.0357319.0395.0FRFrance
18251985123245240223304.0267176.0445405.0485.0FRFrance
18261985113276205252399.0300011.0501458.0544.0FRFrance
18271985103353231326279.0380183.0640591.0689.0FRFrance
18281985093369895341109.0398681.0670618.0722.0FRFrance
18291985083389886359529.0420243.0707652.0762.0FRFrance
18301985073471852432599.0511105.0855784.0926.0FRFrance
18311985063565825518011.0613639.01026939.01113.0FRFrance
18321985053637302592795.0681809.011551074.01236.0FRFrance
18331985043424937390794.0459080.0770708.0832.0FRFrance
18341985033213901174689.0253113.0388317.0459.0FRFrance
183519850239758680949.0114223.0177147.0207.0FRFrance
183619850138548965918.0105060.0155120.0190.0FRFrance
183719845238483060602.0109058.0154110.0198.0FRFrance
1838198451310172680242.0123210.0185146.0224.0FRFrance
18391984503123680101401.0145959.0225184.0266.0FRFrance
1840198449310107381684.0120462.0184149.0219.0FRFrance
184119844837862060634.096606.0143110.0176.0FRFrance
184219844737202954274.089784.013199.0163.0FRFrance
184319844638733067686.0106974.0159123.0195.0FRFrance
18441984453135223101414.0169032.0246184.0308.0FRFrance
184519844436842220056.0116788.012537.0213.0FRFrance
\n", "

1846 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202011 3 101704 93652.0 109756.0 154 142.0 \n", "1 202010 3 104977 96650.0 113304.0 159 146.0 \n", "2 202009 3 110696 102066.0 119326.0 168 155.0 \n", "3 202008 3 143753 133984.0 153522.0 218 203.0 \n", "4 202007 3 183610 172812.0 194408.0 279 263.0 \n", "5 202006 3 206669 195481.0 217857.0 314 297.0 \n", "6 202005 3 187957 177445.0 198469.0 285 269.0 \n", "7 202004 3 122331 113492.0 131170.0 186 173.0 \n", "8 202003 3 78413 71330.0 85496.0 119 108.0 \n", "9 202002 3 53614 47654.0 59574.0 81 72.0 \n", "10 202001 3 36850 31608.0 42092.0 56 48.0 \n", "11 201952 3 28135 23220.0 33050.0 43 36.0 \n", "12 201951 3 29786 25042.0 34530.0 45 38.0 \n", "13 201950 3 34223 29156.0 39290.0 52 44.0 \n", "14 201949 3 25662 21414.0 29910.0 39 33.0 \n", "15 201948 3 22367 18055.0 26679.0 34 27.0 \n", "16 201947 3 18669 14759.0 22579.0 28 22.0 \n", "17 201946 3 16030 12567.0 19493.0 24 19.0 \n", "18 201945 3 10138 7160.0 13116.0 15 10.0 \n", "19 201944 3 7822 5010.0 10634.0 12 8.0 \n", "20 201943 3 9487 6448.0 12526.0 14 9.0 \n", "21 201942 3 7747 5243.0 10251.0 12 8.0 \n", "22 201941 3 7122 4720.0 9524.0 11 7.0 \n", "23 201940 3 8505 5784.0 11226.0 13 9.0 \n", "24 201939 3 7091 4462.0 9720.0 11 7.0 \n", "25 201938 3 4897 2891.0 6903.0 7 4.0 \n", "26 201937 3 3172 1367.0 4977.0 5 2.0 \n", "27 201936 3 2295 728.0 3862.0 3 1.0 \n", "28 201935 3 1010 2.0 2018.0 2 0.0 \n", "29 201934 3 1672 279.0 3065.0 3 1.0 \n", "... ... ... ... ... ... ... ... \n", "1816 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1817 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1818 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1819 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1820 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1821 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1822 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1823 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1824 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1825 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1826 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1827 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1828 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1829 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1830 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1831 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1832 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1833 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1834 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1835 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1836 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1837 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1838 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1839 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1840 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1841 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1842 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1843 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1844 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1845 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 166.0 FR France \n", "1 172.0 FR France \n", "2 181.0 FR France \n", "3 233.0 FR France \n", "4 295.0 FR France \n", "5 331.0 FR France \n", "6 301.0 FR France \n", "7 199.0 FR France \n", "8 130.0 FR France \n", "9 90.0 FR France \n", "10 64.0 FR France \n", "11 50.0 FR France \n", "12 52.0 FR France \n", "13 60.0 FR France \n", "14 45.0 FR France \n", "15 41.0 FR France \n", "16 34.0 FR France \n", "17 29.0 FR France \n", "18 20.0 FR France \n", "19 16.0 FR France \n", "20 19.0 FR France \n", "21 16.0 FR France \n", "22 15.0 FR France \n", "23 17.0 FR France \n", "24 15.0 FR France \n", "25 10.0 FR France \n", "26 8.0 FR France \n", "27 5.0 FR France \n", "28 4.0 FR France \n", "29 5.0 FR France \n", "... ... ... ... \n", "1816 59.0 FR France \n", "1817 64.0 FR France \n", "1818 97.0 FR France \n", "1819 93.0 FR France \n", "1820 80.0 FR France \n", "1821 116.0 FR France \n", "1822 149.0 FR France \n", "1823 281.0 FR France \n", "1824 395.0 FR France \n", "1825 485.0 FR France \n", "1826 544.0 FR France \n", "1827 689.0 FR France \n", "1828 722.0 FR France \n", "1829 762.0 FR France \n", "1830 926.0 FR France \n", "1831 1113.0 FR France \n", "1832 1236.0 FR France \n", "1833 832.0 FR France \n", "1834 459.0 FR France \n", "1835 207.0 FR France \n", "1836 190.0 FR France \n", "1837 198.0 FR France \n", "1838 224.0 FR France \n", "1839 266.0 FR France \n", "1840 219.0 FR France \n", "1841 176.0 FR France \n", "1842 163.0 FR France \n", "1843 195.0 FR France \n", "1844 308.0 FR France \n", "1845 213.0 FR France \n", "\n", "[1846 rows x 10 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data, 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": {}, "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 }