{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 10, "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": [ "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": 11, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", "data_filename = \"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": "markdown", "metadata": {}, "source": [ "Pour assurer une meilleure stabilité de l'analyse et de sa réplicabilité dans le temps, et pour éviter de trop longs temps de chargement à l'excécution du Notebook, nous avons choisi de télécharger les données en local, et de les charger dans ce document à partir du fichier local plutôt que d'utiliser l'URL de téléchargement. L'URL est cependant laissée à disposition dans le cas où le fichier de données ne serait pas localement disponible." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02024063212524197757.0227291.0319297.0341.0FRFrance
12024053217508204749.0230267.0326307.0345.0FRFrance
22024043213196200547.0225845.0320301.0339.0FRFrance
32024033163457152276.0174638.0245228.0262.0FRFrance
42024023129436119453.0139419.0194179.0209.0FRFrance
52024013120769109452.0132086.0181164.0198.0FRFrance
62023523115446103738.0127154.0174156.0192.0FRFrance
72023513148755136546.0160964.0224206.0242.0FRFrance
82023503147971136787.0159155.0223206.0240.0FRFrance
92023493147552136422.0158682.0222205.0239.0FRFrance
102023483124204113479.0134929.0187171.0203.0FRFrance
112023473110910100658.0121162.0167152.0182.0FRFrance
1220234638385375096.092610.0126113.0139.0FRFrance
1320234537200363178.080828.010895.0121.0FRFrance
1420234434995242813.057091.07564.086.0FRFrance
1520234334498238170.051794.06858.078.0FRFrance
1620234235684249277.064407.08675.097.0FRFrance
1720234135835751032.065682.08877.099.0FRFrance
1820234036889460069.077719.010491.0117.0FRFrance
1920233937200363452.080554.010895.0121.0FRFrance
2020233836321855227.071209.09583.0107.0FRFrance
2120233734908542079.056091.07463.085.0FRFrance
2220233633824732237.044257.05849.067.0FRFrance
2320233533169526013.037377.04839.057.0FRFrance
2420233432666321057.032269.04032.048.0FRFrance
2520233331914413161.025127.02920.038.0FRFrance
2620233231464110285.018997.02215.029.0FRFrance
2720233131528610705.019867.02316.030.0FRFrance
282023303132058647.017763.02013.027.0FRFrance
292023293111227113.015131.01711.023.0FRFrance
.................................
202019852132609619621.032571.04735.059.0FRFrance
202119852032789620885.034907.05138.064.0FRFrance
202219851934315432821.053487.07859.097.0FRFrance
202319851834055529935.051175.07455.093.0FRFrance
202419851733405324366.043740.06244.080.0FRFrance
202519851635036236451.064273.09166.0116.0FRFrance
202619851536388145538.082224.011683.0149.0FRFrance
20271985143134545114400.0154690.0244207.0281.0FRFrance
20281985133197206176080.0218332.0357319.0395.0FRFrance
20291985123245240223304.0267176.0445405.0485.0FRFrance
20301985113276205252399.0300011.0501458.0544.0FRFrance
20311985103353231326279.0380183.0640591.0689.0FRFrance
20321985093369895341109.0398681.0670618.0722.0FRFrance
20331985083389886359529.0420243.0707652.0762.0FRFrance
20341985073471852432599.0511105.0855784.0926.0FRFrance
20351985063565825518011.0613639.01026939.01113.0FRFrance
20361985053637302592795.0681809.011551074.01236.0FRFrance
20371985043424937390794.0459080.0770708.0832.0FRFrance
20381985033213901174689.0253113.0388317.0459.0FRFrance
203919850239758680949.0114223.0177147.0207.0FRFrance
204019850138548965918.0105060.0155120.0190.0FRFrance
204119845238483060602.0109058.0154110.0198.0FRFrance
2042198451310172680242.0123210.0185146.0224.0FRFrance
20431984503123680101401.0145959.0225184.0266.0FRFrance
2044198449310107381684.0120462.0184149.0219.0FRFrance
204519844837862060634.096606.0143110.0176.0FRFrance
204619844737202954274.089784.013199.0163.0FRFrance
204719844638733067686.0106974.0159123.0195.0FRFrance
20481984453135223101414.0169032.0246184.0308.0FRFrance
204919844436842220056.0116788.012537.0213.0FRFrance
\n", "

2050 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202406 3 212524 197757.0 227291.0 319 297.0 \n", "1 202405 3 217508 204749.0 230267.0 326 307.0 \n", "2 202404 3 213196 200547.0 225845.0 320 301.0 \n", "3 202403 3 163457 152276.0 174638.0 245 228.0 \n", "4 202402 3 129436 119453.0 139419.0 194 179.0 \n", "5 202401 3 120769 109452.0 132086.0 181 164.0 \n", "6 202352 3 115446 103738.0 127154.0 174 156.0 \n", "7 202351 3 148755 136546.0 160964.0 224 206.0 \n", "8 202350 3 147971 136787.0 159155.0 223 206.0 \n", "9 202349 3 147552 136422.0 158682.0 222 205.0 \n", "10 202348 3 124204 113479.0 134929.0 187 171.0 \n", "11 202347 3 110910 100658.0 121162.0 167 152.0 \n", "12 202346 3 83853 75096.0 92610.0 126 113.0 \n", "13 202345 3 72003 63178.0 80828.0 108 95.0 \n", "14 202344 3 49952 42813.0 57091.0 75 64.0 \n", "15 202343 3 44982 38170.0 51794.0 68 58.0 \n", "16 202342 3 56842 49277.0 64407.0 86 75.0 \n", "17 202341 3 58357 51032.0 65682.0 88 77.0 \n", "18 202340 3 68894 60069.0 77719.0 104 91.0 \n", "19 202339 3 72003 63452.0 80554.0 108 95.0 \n", "20 202338 3 63218 55227.0 71209.0 95 83.0 \n", "21 202337 3 49085 42079.0 56091.0 74 63.0 \n", "22 202336 3 38247 32237.0 44257.0 58 49.0 \n", "23 202335 3 31695 26013.0 37377.0 48 39.0 \n", "24 202334 3 26663 21057.0 32269.0 40 32.0 \n", "25 202333 3 19144 13161.0 25127.0 29 20.0 \n", "26 202332 3 14641 10285.0 18997.0 22 15.0 \n", "27 202331 3 15286 10705.0 19867.0 23 16.0 \n", "28 202330 3 13205 8647.0 17763.0 20 13.0 \n", "29 202329 3 11122 7113.0 15131.0 17 11.0 \n", "... ... ... ... ... ... ... ... \n", "2020 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2021 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2022 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2023 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2024 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2025 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2026 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2027 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2028 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2029 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2030 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2031 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2032 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2033 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2034 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2035 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2036 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2037 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2038 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2039 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2040 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2041 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2042 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2043 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2044 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2045 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2046 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2047 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2048 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2049 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 341.0 FR France \n", "1 345.0 FR France \n", "2 339.0 FR France \n", "3 262.0 FR France \n", "4 209.0 FR France \n", "5 198.0 FR France \n", "6 192.0 FR France \n", "7 242.0 FR France \n", "8 240.0 FR France \n", "9 239.0 FR France \n", "10 203.0 FR France \n", "11 182.0 FR France \n", "12 139.0 FR France \n", "13 121.0 FR France \n", "14 86.0 FR France \n", "15 78.0 FR France \n", "16 97.0 FR France \n", "17 99.0 FR France \n", "18 117.0 FR France \n", "19 121.0 FR France \n", "20 107.0 FR France \n", "21 85.0 FR France \n", "22 67.0 FR France \n", "23 57.0 FR France \n", "24 48.0 FR France \n", "25 38.0 FR France \n", "26 29.0 FR France \n", "27 30.0 FR France \n", "28 27.0 FR France \n", "29 23.0 FR France \n", "... ... ... ... \n", "2020 59.0 FR France \n", "2021 64.0 FR France \n", "2022 97.0 FR France \n", "2023 93.0 FR France \n", "2024 80.0 FR France \n", "2025 116.0 FR France \n", "2026 149.0 FR France \n", "2027 281.0 FR France \n", "2028 395.0 FR France \n", "2029 485.0 FR France \n", "2030 544.0 FR France \n", "2031 689.0 FR France \n", "2032 722.0 FR France \n", "2033 762.0 FR France \n", "2034 926.0 FR France \n", "2035 1113.0 FR France \n", "2036 1236.0 FR France \n", "2037 832.0 FR France \n", "2038 459.0 FR France \n", "2039 207.0 FR France \n", "2040 190.0 FR France \n", "2041 198.0 FR France \n", "2042 224.0 FR France \n", "2043 266.0 FR France \n", "2044 219.0 FR France \n", "2045 176.0 FR France \n", "2046 163.0 FR France \n", "2047 195.0 FR France \n", "2048 308.0 FR France \n", "2049 213.0 FR France \n", "\n", "[2050 rows x 10 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "if not os.path.exists(data_filename):\n", " raise OSError(\"No data file. You may use URL to download data if the file is not available.\")\n", " \n", "raw_data = pd.read_csv(data_filename, 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": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18131989193-NaNNaN-NaNNaNFRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1813 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1813 FR France " ] }, "execution_count": 8, "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 }