{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import os\n", "import urllib.request\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": [] }, { "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 nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020251334329136033.050549.06554.076.0FRFrance
120251235309346098.060088.07969.089.0FRFrance
220251135946952154.066784.08978.0100.0FRFrance
320251036033453048.067620.09079.0101.0FRFrance
420250938453174994.094068.0126112.0140.0FRFrance
52025083136020124824.0147216.0203186.0220.0FRFrance
62025073208952195988.0221916.0312293.0331.0FRFrance
72025063273519258159.0288879.0408385.0431.0FRFrance
82025053334395318416.0350374.0499475.0523.0FRFrance
92025043350043332885.0367201.0522496.0548.0FRFrance
102025033252772238917.0266627.0377356.0398.0FRFrance
112025023257247242991.0271503.0384363.0405.0FRFrance
122025013231549214627.0248471.0345320.0370.0FRFrance
132024523201726185870.0217582.0302278.0326.0FRFrance
142024513201697187843.0215551.0302281.0323.0FRFrance
152024503136694126369.0147019.0205190.0220.0FRFrance
16202449310848799037.0117937.0163149.0177.0FRFrance
1720244838738178687.096075.0131118.0144.0FRFrance
1820244737628667626.084946.0114101.0127.0FRFrance
1920244635639949006.063792.08574.096.0FRFrance
2020244534734740843.053851.07161.081.0FRFrance
2120244433603930122.041956.05445.063.0FRFrance
2220244334657239928.053216.07060.080.0FRFrance
2320244236778560009.075561.010290.0114.0FRFrance
2420244137943571386.087484.0119107.0131.0FRFrance
2520244038496576555.093375.0127114.0140.0FRFrance
2620243939166082937.0100383.0137124.0150.0FRFrance
2720243839178682903.0100669.0138125.0151.0FRFrance
2820243735646049319.063601.08574.096.0FRFrance
2920243633365727906.039408.05041.059.0FRFrance
.................................
207919852132609619621.032571.04735.059.0FRFrance
208019852032789620885.034907.05138.064.0FRFrance
208119851934315432821.053487.07859.097.0FRFrance
208219851834055529935.051175.07455.093.0FRFrance
208319851733405324366.043740.06244.080.0FRFrance
208419851635036236451.064273.09166.0116.0FRFrance
208519851536388145538.082224.011683.0149.0FRFrance
20861985143134545114400.0154690.0244207.0281.0FRFrance
20871985133197206176080.0218332.0357319.0395.0FRFrance
20881985123245240223304.0267176.0445405.0485.0FRFrance
20891985113276205252399.0300011.0501458.0544.0FRFrance
20901985103353231326279.0380183.0640591.0689.0FRFrance
20911985093369895341109.0398681.0670618.0722.0FRFrance
20921985083389886359529.0420243.0707652.0762.0FRFrance
20931985073471852432599.0511105.0855784.0926.0FRFrance
20941985063565825518011.0613639.01026939.01113.0FRFrance
20951985053637302592795.0681809.011551074.01236.0FRFrance
20961985043424937390794.0459080.0770708.0832.0FRFrance
20971985033213901174689.0253113.0388317.0459.0FRFrance
209819850239758680949.0114223.0177147.0207.0FRFrance
209919850138548965918.0105060.0155120.0190.0FRFrance
210019845238483060602.0109058.0154110.0198.0FRFrance
2101198451310172680242.0123210.0185146.0224.0FRFrance
21021984503123680101401.0145959.0225184.0266.0FRFrance
2103198449310107381684.0120462.0184149.0219.0FRFrance
210419844837862060634.096606.0143110.0176.0FRFrance
210519844737202954274.089784.013199.0163.0FRFrance
210619844638733067686.0106974.0159123.0195.0FRFrance
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\n", "

2109 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202513 3 43291 36033.0 50549.0 65 54.0 \n", "1 202512 3 53093 46098.0 60088.0 79 69.0 \n", "2 202511 3 59469 52154.0 66784.0 89 78.0 \n", "3 202510 3 60334 53048.0 67620.0 90 79.0 \n", "4 202509 3 84531 74994.0 94068.0 126 112.0 \n", "5 202508 3 136020 124824.0 147216.0 203 186.0 \n", "6 202507 3 208952 195988.0 221916.0 312 293.0 \n", "7 202506 3 273519 258159.0 288879.0 408 385.0 \n", "8 202505 3 334395 318416.0 350374.0 499 475.0 \n", "9 202504 3 350043 332885.0 367201.0 522 496.0 \n", "10 202503 3 252772 238917.0 266627.0 377 356.0 \n", "11 202502 3 257247 242991.0 271503.0 384 363.0 \n", "12 202501 3 231549 214627.0 248471.0 345 320.0 \n", "13 202452 3 201726 185870.0 217582.0 302 278.0 \n", "14 202451 3 201697 187843.0 215551.0 302 281.0 \n", "15 202450 3 136694 126369.0 147019.0 205 190.0 \n", "16 202449 3 108487 99037.0 117937.0 163 149.0 \n", "17 202448 3 87381 78687.0 96075.0 131 118.0 \n", "18 202447 3 76286 67626.0 84946.0 114 101.0 \n", "19 202446 3 56399 49006.0 63792.0 85 74.0 \n", "20 202445 3 47347 40843.0 53851.0 71 61.0 \n", "21 202444 3 36039 30122.0 41956.0 54 45.0 \n", "22 202443 3 46572 39928.0 53216.0 70 60.0 \n", "23 202442 3 67785 60009.0 75561.0 102 90.0 \n", "24 202441 3 79435 71386.0 87484.0 119 107.0 \n", "25 202440 3 84965 76555.0 93375.0 127 114.0 \n", "26 202439 3 91660 82937.0 100383.0 137 124.0 \n", "27 202438 3 91786 82903.0 100669.0 138 125.0 \n", "28 202437 3 56460 49319.0 63601.0 85 74.0 \n", "29 202436 3 33657 27906.0 39408.0 50 41.0 \n", "... ... ... ... ... ... ... ... \n", "2079 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2080 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2081 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2082 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2083 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2084 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2085 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2086 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2087 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2088 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2089 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2090 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2091 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2092 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2093 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2094 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2095 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2096 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2097 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2098 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2099 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2100 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2101 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2102 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2103 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2104 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2105 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2106 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2107 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2108 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 76.0 FR France \n", "1 89.0 FR France \n", "2 100.0 FR France \n", "3 101.0 FR France \n", "4 140.0 FR France \n", "5 220.0 FR France \n", "6 331.0 FR France \n", "7 431.0 FR France \n", "8 523.0 FR France \n", "9 548.0 FR France \n", "10 398.0 FR France \n", "11 405.0 FR France \n", "12 370.0 FR France \n", "13 326.0 FR France \n", "14 323.0 FR France \n", "15 220.0 FR France \n", "16 177.0 FR France \n", "17 144.0 FR France \n", "18 127.0 FR France \n", "19 96.0 FR France \n", "20 81.0 FR France \n", "21 63.0 FR France \n", "22 80.0 FR France \n", "23 114.0 FR France \n", "24 131.0 FR France \n", "25 140.0 FR France \n", "26 150.0 FR France \n", "27 151.0 FR France \n", "28 96.0 FR France \n", "29 59.0 FR France \n", "... ... ... ... \n", "2079 59.0 FR France \n", "2080 64.0 FR France \n", "2081 97.0 FR France \n", "2082 93.0 FR France \n", "2083 80.0 FR France \n", "2084 116.0 FR France \n", "2085 149.0 FR France \n", "2086 281.0 FR France \n", "2087 395.0 FR France \n", "2088 485.0 FR France \n", "2089 544.0 FR France \n", "2090 689.0 FR France \n", "2091 722.0 FR France \n", "2092 762.0 FR France \n", "2093 926.0 FR France \n", "2094 1113.0 FR France \n", "2095 1236.0 FR France \n", "2096 832.0 FR France \n", "2097 459.0 FR France \n", "2098 207.0 FR France \n", "2099 190.0 FR France \n", "2100 198.0 FR France \n", "2101 224.0 FR France \n", "2102 266.0 FR France \n", "2103 219.0 FR France \n", "2104 176.0 FR France \n", "2105 163.0 FR France \n", "2106 195.0 FR France \n", "2107 308.0 FR France \n", "2108 213.0 FR France \n", "\n", "[2109 rows x 10 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = ''\n", "if not os.path.isfile('data.csv'):\n", " data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", " urllib.request.urlretrieve(data_url, \"data.csv\")\n", "raw_data = pd.read_csv('data.csv', encoding = 'iso-8859-1', skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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 }