{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os\n", "import urllib.request" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to protect us in case the Réseau Sentinelles Web server disappears or is modified, we make a local copy of this dataset that we store together with our analysis. It is unnecessary and even risky to download the data at each execution, because in case of a malfunction we might be replacing our file by a corrupted version. Therefore we download the data only if no local copy exists." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", "data_file = \"syndrome-grippal.csv\"\n", "\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "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": "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": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020243939890588489.0109321.0148132.0164.0FRFrance
120243839259183601.0101581.0139126.0152.0FRFrance
220243735646049319.063601.08574.096.0FRFrance
320243633365727906.039408.05041.059.0FRFrance
420243532740422036.032772.04133.049.0FRFrance
520243432671721003.032431.04031.049.0FRFrance
620243332062315349.025897.03123.039.0FRFrance
720243232318717532.028842.03527.043.0FRFrance
820243132603520267.031803.03930.048.0FRFrance
920243033639328593.044193.05543.067.0FRFrance
1020242933956032592.046528.05949.069.0FRFrance
1120242835434245781.062903.08168.094.0FRFrance
1220242734736440234.054494.07160.082.0FRFrance
1320242634421936956.051482.06655.077.0FRFrance
1420242534720440300.054108.07161.081.0FRFrance
1520242434111034671.047549.06252.072.0FRFrance
1620242333587530610.041140.05446.062.0FRFrance
1720242233377228274.039270.05143.059.0FRFrance
1820242132196317556.026370.03326.040.0FRFrance
1920242032005715780.024334.03024.036.0FRFrance
2020241931537511274.019476.02317.029.0FRFrance
2120241832240917653.027165.03427.041.0FRFrance
2220241732704221410.032674.04133.049.0FRFrance
2320241632888223305.034459.04335.051.0FRFrance
2420241533022924648.035810.04537.053.0FRFrance
2520241433181326529.037097.04840.056.0FRFrance
2620241333509029607.040573.05345.061.0FRFrance
2720241234063934582.046696.06152.070.0FRFrance
2820241135026843331.057205.07565.085.0FRFrance
2920241036010752623.067591.09079.0101.0FRFrance
.................................
205319852132609619621.032571.04735.059.0FRFrance
205419852032789620885.034907.05138.064.0FRFrance
205519851934315432821.053487.07859.097.0FRFrance
205619851834055529935.051175.07455.093.0FRFrance
205719851733405324366.043740.06244.080.0FRFrance
205819851635036236451.064273.09166.0116.0FRFrance
205919851536388145538.082224.011683.0149.0FRFrance
20601985143134545114400.0154690.0244207.0281.0FRFrance
20611985133197206176080.0218332.0357319.0395.0FRFrance
20621985123245240223304.0267176.0445405.0485.0FRFrance
20631985113276205252399.0300011.0501458.0544.0FRFrance
20641985103353231326279.0380183.0640591.0689.0FRFrance
20651985093369895341109.0398681.0670618.0722.0FRFrance
20661985083389886359529.0420243.0707652.0762.0FRFrance
20671985073471852432599.0511105.0855784.0926.0FRFrance
20681985063565825518011.0613639.01026939.01113.0FRFrance
20691985053637302592795.0681809.011551074.01236.0FRFrance
20701985043424937390794.0459080.0770708.0832.0FRFrance
20711985033213901174689.0253113.0388317.0459.0FRFrance
207219850239758680949.0114223.0177147.0207.0FRFrance
207319850138548965918.0105060.0155120.0190.0FRFrance
207419845238483060602.0109058.0154110.0198.0FRFrance
2075198451310172680242.0123210.0185146.0224.0FRFrance
20761984503123680101401.0145959.0225184.0266.0FRFrance
2077198449310107381684.0120462.0184149.0219.0FRFrance
207819844837862060634.096606.0143110.0176.0FRFrance
207919844737202954274.089784.013199.0163.0FRFrance
208019844638733067686.0106974.0159123.0195.0FRFrance
20811984453135223101414.0169032.0246184.0308.0FRFrance
208219844436842220056.0116788.012537.0213.0FRFrance
\n", "

2083 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202439 3 98905 88489.0 109321.0 148 132.0 \n", "1 202438 3 92591 83601.0 101581.0 139 126.0 \n", "2 202437 3 56460 49319.0 63601.0 85 74.0 \n", "3 202436 3 33657 27906.0 39408.0 50 41.0 \n", "4 202435 3 27404 22036.0 32772.0 41 33.0 \n", "5 202434 3 26717 21003.0 32431.0 40 31.0 \n", "6 202433 3 20623 15349.0 25897.0 31 23.0 \n", "7 202432 3 23187 17532.0 28842.0 35 27.0 \n", "8 202431 3 26035 20267.0 31803.0 39 30.0 \n", "9 202430 3 36393 28593.0 44193.0 55 43.0 \n", "10 202429 3 39560 32592.0 46528.0 59 49.0 \n", "11 202428 3 54342 45781.0 62903.0 81 68.0 \n", "12 202427 3 47364 40234.0 54494.0 71 60.0 \n", "13 202426 3 44219 36956.0 51482.0 66 55.0 \n", "14 202425 3 47204 40300.0 54108.0 71 61.0 \n", "15 202424 3 41110 34671.0 47549.0 62 52.0 \n", "16 202423 3 35875 30610.0 41140.0 54 46.0 \n", "17 202422 3 33772 28274.0 39270.0 51 43.0 \n", "18 202421 3 21963 17556.0 26370.0 33 26.0 \n", "19 202420 3 20057 15780.0 24334.0 30 24.0 \n", "20 202419 3 15375 11274.0 19476.0 23 17.0 \n", "21 202418 3 22409 17653.0 27165.0 34 27.0 \n", "22 202417 3 27042 21410.0 32674.0 41 33.0 \n", "23 202416 3 28882 23305.0 34459.0 43 35.0 \n", "24 202415 3 30229 24648.0 35810.0 45 37.0 \n", "25 202414 3 31813 26529.0 37097.0 48 40.0 \n", "26 202413 3 35090 29607.0 40573.0 53 45.0 \n", "27 202412 3 40639 34582.0 46696.0 61 52.0 \n", "28 202411 3 50268 43331.0 57205.0 75 65.0 \n", "29 202410 3 60107 52623.0 67591.0 90 79.0 \n", "... ... ... ... ... ... ... ... \n", "2053 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2054 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2055 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2056 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2057 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2058 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2059 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2060 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2061 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2062 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2063 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2064 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2065 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2066 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2067 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2068 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2069 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2070 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2071 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2072 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2073 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2074 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2075 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2076 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2077 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2078 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2079 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2080 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2081 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2082 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 164.0 FR France \n", "1 152.0 FR France \n", "2 96.0 FR France \n", "3 59.0 FR France \n", "4 49.0 FR France \n", "5 49.0 FR France \n", "6 39.0 FR France \n", "7 43.0 FR France \n", "8 48.0 FR France \n", "9 67.0 FR France \n", "10 69.0 FR France \n", "11 94.0 FR France \n", "12 82.0 FR France \n", "13 77.0 FR France \n", "14 81.0 FR France \n", "15 72.0 FR France \n", "16 62.0 FR France \n", "17 59.0 FR France \n", "18 40.0 FR France \n", "19 36.0 FR France \n", "20 29.0 FR France \n", "21 41.0 FR France \n", "22 49.0 FR France \n", "23 51.0 FR France \n", "24 53.0 FR France \n", "25 56.0 FR France \n", "26 61.0 FR France \n", "27 70.0 FR France \n", "28 85.0 FR France \n", "29 101.0 FR France \n", "... ... ... ... \n", "2053 59.0 FR France \n", "2054 64.0 FR France \n", "2055 97.0 FR France \n", "2056 93.0 FR France \n", "2057 80.0 FR France \n", "2058 116.0 FR France \n", "2059 149.0 FR France \n", "2060 281.0 FR France \n", "2061 395.0 FR France \n", "2062 485.0 FR France \n", "2063 544.0 FR France \n", "2064 689.0 FR France \n", "2065 722.0 FR France \n", "2066 762.0 FR France \n", "2067 926.0 FR France \n", "2068 1113.0 FR France \n", "2069 1236.0 FR France \n", "2070 832.0 FR France \n", "2071 459.0 FR France \n", "2072 207.0 FR France \n", "2073 190.0 FR France \n", "2074 198.0 FR France \n", "2075 224.0 FR France \n", "2076 266.0 FR France \n", "2077 219.0 FR France \n", "2078 176.0 FR France \n", "2079 163.0 FR France \n", "2080 195.0 FR France \n", "2081 308.0 FR France \n", "2082 213.0 FR France \n", "\n", "[2083 rows x 10 columns]" ] }, "execution_count": 8, "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 }