{ "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" ] }, { "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": 14, "metadata": {}, "outputs": [], "source": [ "data_url = \"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`.\n", "Lire les données depuis le fichier local." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02024043238623222823.0254423.0358334.0382.0FRFrance
12024033163970152714.0175226.0246229.0263.0FRFrance
22024023129436119453.0139419.0194179.0209.0FRFrance
32024013120769109452.0132086.0181164.0198.0FRFrance
42023523115446103738.0127154.0174156.0192.0FRFrance
52023513148755136546.0160964.0224206.0242.0FRFrance
62023503147971136787.0159155.0223206.0240.0FRFrance
72023493147552136422.0158682.0222205.0239.0FRFrance
82023483124204113479.0134929.0187171.0203.0FRFrance
92023473110910100658.0121162.0167152.0182.0FRFrance
1020234638385375096.092610.0126113.0139.0FRFrance
1120234537200363178.080828.010895.0121.0FRFrance
1220234434995242813.057091.07564.086.0FRFrance
1320234334498238170.051794.06858.078.0FRFrance
1420234235684249277.064407.08675.097.0FRFrance
1520234135835751032.065682.08877.099.0FRFrance
1620234036889460069.077719.010491.0117.0FRFrance
1720233937200363452.080554.010895.0121.0FRFrance
1820233836321855227.071209.09583.0107.0FRFrance
1920233734908542079.056091.07463.085.0FRFrance
2020233633824732237.044257.05849.067.0FRFrance
2120233533169526013.037377.04839.057.0FRFrance
2220233432666321057.032269.04032.048.0FRFrance
2320233331914413161.025127.02920.038.0FRFrance
2420233231464110285.018997.02215.029.0FRFrance
2520233131528610705.019867.02316.030.0FRFrance
262023303132058647.017763.02013.027.0FRFrance
272023293111227113.015131.01711.023.0FRFrance
28202328391795703.012655.0149.019.0FRFrance
29202327389995763.012235.0149.019.0FRFrance
.................................
201819852132609619621.032571.04735.059.0FRFrance
201919852032789620885.034907.05138.064.0FRFrance
202019851934315432821.053487.07859.097.0FRFrance
202119851834055529935.051175.07455.093.0FRFrance
202219851733405324366.043740.06244.080.0FRFrance
202319851635036236451.064273.09166.0116.0FRFrance
202419851536388145538.082224.011683.0149.0FRFrance
20251985143134545114400.0154690.0244207.0281.0FRFrance
20261985133197206176080.0218332.0357319.0395.0FRFrance
20271985123245240223304.0267176.0445405.0485.0FRFrance
20281985113276205252399.0300011.0501458.0544.0FRFrance
20291985103353231326279.0380183.0640591.0689.0FRFrance
20301985093369895341109.0398681.0670618.0722.0FRFrance
20311985083389886359529.0420243.0707652.0762.0FRFrance
20321985073471852432599.0511105.0855784.0926.0FRFrance
20331985063565825518011.0613639.01026939.01113.0FRFrance
20341985053637302592795.0681809.011551074.01236.0FRFrance
20351985043424937390794.0459080.0770708.0832.0FRFrance
20361985033213901174689.0253113.0388317.0459.0FRFrance
203719850239758680949.0114223.0177147.0207.0FRFrance
203819850138548965918.0105060.0155120.0190.0FRFrance
203919845238483060602.0109058.0154110.0198.0FRFrance
2040198451310172680242.0123210.0185146.0224.0FRFrance
20411984503123680101401.0145959.0225184.0266.0FRFrance
2042198449310107381684.0120462.0184149.0219.0FRFrance
204319844837862060634.096606.0143110.0176.0FRFrance
204419844737202954274.089784.013199.0163.0FRFrance
204519844638733067686.0106974.0159123.0195.0FRFrance
20461984453135223101414.0169032.0246184.0308.0FRFrance
204719844436842220056.0116788.012537.0213.0FRFrance
\n", "

2048 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202404 3 238623 222823.0 254423.0 358 334.0 \n", "1 202403 3 163970 152714.0 175226.0 246 229.0 \n", "2 202402 3 129436 119453.0 139419.0 194 179.0 \n", "3 202401 3 120769 109452.0 132086.0 181 164.0 \n", "4 202352 3 115446 103738.0 127154.0 174 156.0 \n", "5 202351 3 148755 136546.0 160964.0 224 206.0 \n", "6 202350 3 147971 136787.0 159155.0 223 206.0 \n", "7 202349 3 147552 136422.0 158682.0 222 205.0 \n", "8 202348 3 124204 113479.0 134929.0 187 171.0 \n", "9 202347 3 110910 100658.0 121162.0 167 152.0 \n", "10 202346 3 83853 75096.0 92610.0 126 113.0 \n", "11 202345 3 72003 63178.0 80828.0 108 95.0 \n", "12 202344 3 49952 42813.0 57091.0 75 64.0 \n", "13 202343 3 44982 38170.0 51794.0 68 58.0 \n", "14 202342 3 56842 49277.0 64407.0 86 75.0 \n", "15 202341 3 58357 51032.0 65682.0 88 77.0 \n", "16 202340 3 68894 60069.0 77719.0 104 91.0 \n", "17 202339 3 72003 63452.0 80554.0 108 95.0 \n", "18 202338 3 63218 55227.0 71209.0 95 83.0 \n", "19 202337 3 49085 42079.0 56091.0 74 63.0 \n", "20 202336 3 38247 32237.0 44257.0 58 49.0 \n", "21 202335 3 31695 26013.0 37377.0 48 39.0 \n", "22 202334 3 26663 21057.0 32269.0 40 32.0 \n", "23 202333 3 19144 13161.0 25127.0 29 20.0 \n", "24 202332 3 14641 10285.0 18997.0 22 15.0 \n", "25 202331 3 15286 10705.0 19867.0 23 16.0 \n", "26 202330 3 13205 8647.0 17763.0 20 13.0 \n", "27 202329 3 11122 7113.0 15131.0 17 11.0 \n", "28 202328 3 9179 5703.0 12655.0 14 9.0 \n", "29 202327 3 8999 5763.0 12235.0 14 9.0 \n", "... ... ... ... ... ... ... ... \n", "2018 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2019 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2020 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2021 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2022 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2023 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2024 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2025 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2026 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2027 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2028 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2029 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2030 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2031 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2032 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2033 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2034 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2035 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2036 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2037 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2038 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2039 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2040 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2041 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2042 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2043 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2044 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2045 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2046 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2047 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 382.0 FR France \n", "1 263.0 FR France \n", "2 209.0 FR France \n", "3 198.0 FR France \n", "4 192.0 FR France \n", "5 242.0 FR France \n", "6 240.0 FR France \n", "7 239.0 FR France \n", "8 203.0 FR France \n", "9 182.0 FR France \n", "10 139.0 FR France \n", "11 121.0 FR France \n", "12 86.0 FR France \n", "13 78.0 FR France \n", "14 97.0 FR France \n", "15 99.0 FR France \n", "16 117.0 FR France \n", "17 121.0 FR France \n", "18 107.0 FR France \n", "19 85.0 FR France \n", "20 67.0 FR France \n", "21 57.0 FR France \n", "22 48.0 FR France \n", "23 38.0 FR France \n", "24 29.0 FR France \n", "25 30.0 FR France \n", "26 27.0 FR France \n", "27 23.0 FR France \n", "28 19.0 FR France \n", "29 19.0 FR France \n", "... ... ... ... \n", "2018 59.0 FR France \n", "2019 64.0 FR France \n", "2020 97.0 FR France \n", "2021 93.0 FR France \n", "2022 80.0 FR France \n", "2023 116.0 FR France \n", "2024 149.0 FR France \n", "2025 281.0 FR France \n", "2026 395.0 FR France \n", "2027 485.0 FR France \n", "2028 544.0 FR France \n", "2029 689.0 FR France \n", "2030 722.0 FR France \n", "2031 762.0 FR France \n", "2032 926.0 FR France \n", "2033 1113.0 FR France \n", "2034 1236.0 FR France \n", "2035 832.0 FR France \n", "2036 459.0 FR France \n", "2037 207.0 FR France \n", "2038 190.0 FR France \n", "2039 198.0 FR France \n", "2040 224.0 FR France \n", "2041 266.0 FR France \n", "2042 219.0 FR France \n", "2043 176.0 FR France \n", "2044 163.0 FR France \n", "2045 195.0 FR France \n", "2046 308.0 FR France \n", "2047 213.0 FR France \n", "\n", "[2048 rows x 10 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, 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 }