{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 2, "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": 3, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data_file = \"syndrome-grippal.csv\"\n", "\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": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020241433519428705.041683.05343.063.0FRFrance
120241333503829535.040541.05345.061.0FRFrance
220241234063934582.046696.06152.070.0FRFrance
320241135026843331.057205.07565.085.0FRFrance
420241036010752623.067591.09079.0101.0FRFrance
520240937112162920.079322.010795.0119.0FRFrance
6202408310456694520.0114612.0157142.0172.0FRFrance
72024073138078127050.0149106.0207190.0224.0FRFrance
82024063190062177955.0202169.0285267.0303.0FRFrance
92024053216237203595.0228879.0324305.0343.0FRFrance
102024043213196200547.0225845.0320301.0339.0FRFrance
112024033163457152276.0174638.0245228.0262.0FRFrance
122024023129436119453.0139419.0194179.0209.0FRFrance
132024013120769109452.0132086.0181164.0198.0FRFrance
142023523115446103738.0127154.0174156.0192.0FRFrance
152023513148755136546.0160964.0224206.0242.0FRFrance
162023503147971136787.0159155.0223206.0240.0FRFrance
172023493147552136422.0158682.0222205.0239.0FRFrance
182023483124204113479.0134929.0187171.0203.0FRFrance
192023473110948100694.0121202.0167152.0182.0FRFrance
2020234638389475134.092654.0126113.0139.0FRFrance
2120234537200363178.080828.010895.0121.0FRFrance
2220234434995242813.057091.07564.086.0FRFrance
2320234334498238170.051794.06858.078.0FRFrance
2420234235684249277.064407.08675.097.0FRFrance
2520234135835751032.065682.08877.099.0FRFrance
2620234036889460069.077719.010491.0117.0FRFrance
2720233937200363452.080554.010895.0121.0FRFrance
2820233836321855227.071209.09583.0107.0FRFrance
2920233734908542079.056091.07463.085.0FRFrance
.................................
202819852132609619621.032571.04735.059.0FRFrance
202919852032789620885.034907.05138.064.0FRFrance
203019851934315432821.053487.07859.097.0FRFrance
203119851834055529935.051175.07455.093.0FRFrance
203219851733405324366.043740.06244.080.0FRFrance
203319851635036236451.064273.09166.0116.0FRFrance
203419851536388145538.082224.011683.0149.0FRFrance
20351985143134545114400.0154690.0244207.0281.0FRFrance
20361985133197206176080.0218332.0357319.0395.0FRFrance
20371985123245240223304.0267176.0445405.0485.0FRFrance
20381985113276205252399.0300011.0501458.0544.0FRFrance
20391985103353231326279.0380183.0640591.0689.0FRFrance
20401985093369895341109.0398681.0670618.0722.0FRFrance
20411985083389886359529.0420243.0707652.0762.0FRFrance
20421985073471852432599.0511105.0855784.0926.0FRFrance
20431985063565825518011.0613639.01026939.01113.0FRFrance
20441985053637302592795.0681809.011551074.01236.0FRFrance
20451985043424937390794.0459080.0770708.0832.0FRFrance
20461985033213901174689.0253113.0388317.0459.0FRFrance
204719850239758680949.0114223.0177147.0207.0FRFrance
204819850138548965918.0105060.0155120.0190.0FRFrance
204919845238483060602.0109058.0154110.0198.0FRFrance
2050198451310172680242.0123210.0185146.0224.0FRFrance
20511984503123680101401.0145959.0225184.0266.0FRFrance
2052198449310107381684.0120462.0184149.0219.0FRFrance
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2058 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202414 3 35194 28705.0 41683.0 53 43.0 \n", "1 202413 3 35038 29535.0 40541.0 53 45.0 \n", "2 202412 3 40639 34582.0 46696.0 61 52.0 \n", "3 202411 3 50268 43331.0 57205.0 75 65.0 \n", "4 202410 3 60107 52623.0 67591.0 90 79.0 \n", "5 202409 3 71121 62920.0 79322.0 107 95.0 \n", "6 202408 3 104566 94520.0 114612.0 157 142.0 \n", "7 202407 3 138078 127050.0 149106.0 207 190.0 \n", "8 202406 3 190062 177955.0 202169.0 285 267.0 \n", "9 202405 3 216237 203595.0 228879.0 324 305.0 \n", "10 202404 3 213196 200547.0 225845.0 320 301.0 \n", "11 202403 3 163457 152276.0 174638.0 245 228.0 \n", "12 202402 3 129436 119453.0 139419.0 194 179.0 \n", "13 202401 3 120769 109452.0 132086.0 181 164.0 \n", "14 202352 3 115446 103738.0 127154.0 174 156.0 \n", "15 202351 3 148755 136546.0 160964.0 224 206.0 \n", "16 202350 3 147971 136787.0 159155.0 223 206.0 \n", "17 202349 3 147552 136422.0 158682.0 222 205.0 \n", "18 202348 3 124204 113479.0 134929.0 187 171.0 \n", "19 202347 3 110948 100694.0 121202.0 167 152.0 \n", "20 202346 3 83894 75134.0 92654.0 126 113.0 \n", "21 202345 3 72003 63178.0 80828.0 108 95.0 \n", "22 202344 3 49952 42813.0 57091.0 75 64.0 \n", "23 202343 3 44982 38170.0 51794.0 68 58.0 \n", "24 202342 3 56842 49277.0 64407.0 86 75.0 \n", "25 202341 3 58357 51032.0 65682.0 88 77.0 \n", "26 202340 3 68894 60069.0 77719.0 104 91.0 \n", "27 202339 3 72003 63452.0 80554.0 108 95.0 \n", "28 202338 3 63218 55227.0 71209.0 95 83.0 \n", "29 202337 3 49085 42079.0 56091.0 74 63.0 \n", "... ... ... ... ... ... ... ... \n", "2028 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2029 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2030 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2031 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2032 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2033 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2034 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2035 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2036 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2037 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2038 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2039 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2040 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2041 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2042 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2043 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2044 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2045 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2046 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2047 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2048 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2049 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2050 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2051 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2052 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2053 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2054 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2055 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2056 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2057 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 63.0 FR France \n", "1 61.0 FR France \n", "2 70.0 FR France \n", "3 85.0 FR France \n", "4 101.0 FR France \n", "5 119.0 FR France \n", "6 172.0 FR France \n", "7 224.0 FR France \n", "8 303.0 FR France \n", "9 343.0 FR France \n", "10 339.0 FR France \n", "11 262.0 FR France \n", "12 209.0 FR France \n", "13 198.0 FR France \n", "14 192.0 FR France \n", "15 242.0 FR France \n", "16 240.0 FR France \n", "17 239.0 FR France \n", "18 203.0 FR France \n", "19 182.0 FR France \n", "20 139.0 FR France \n", "21 121.0 FR France \n", "22 86.0 FR France \n", "23 78.0 FR France \n", "24 97.0 FR France \n", "25 99.0 FR France \n", "26 117.0 FR France \n", "27 121.0 FR France \n", "28 107.0 FR France \n", "29 85.0 FR France \n", "... ... ... ... \n", "2028 59.0 FR France \n", "2029 64.0 FR France \n", "2030 97.0 FR France \n", "2031 93.0 FR France \n", "2032 80.0 FR France \n", "2033 116.0 FR France \n", "2034 149.0 FR France \n", "2035 281.0 FR France \n", "2036 395.0 FR France \n", "2037 485.0 FR France \n", "2038 544.0 FR France \n", "2039 689.0 FR France \n", "2040 722.0 FR France \n", "2041 762.0 FR France \n", "2042 926.0 FR France \n", "2043 1113.0 FR France \n", "2044 1236.0 FR France \n", "2045 832.0 FR France \n", "2046 459.0 FR France \n", "2047 207.0 FR France \n", "2048 190.0 FR France \n", "2049 198.0 FR France \n", "2050 224.0 FR France \n", "2051 266.0 FR France \n", "2052 219.0 FR France \n", "2053 176.0 FR France \n", "2054 163.0 FR France \n", "2055 195.0 FR France \n", "2056 308.0 FR France \n", "2057 213.0 FR France \n", "\n", "[2058 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": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18211989193-NaNNaN-NaNNaNFRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1821 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1821 FR France " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "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 }