{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 6, "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": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", "\n", "# check if file exists\n", "filepath = \"./incidence-PAY-3.csv\"\n", "isExist = os.path.exists(filepath)\n", "\n", "if not isExist:\n", " urllib.request.urlretrieve(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`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02022493164351152570.0176132.0248230.0266.0FRFrance
12022483121884111932.0131836.0184169.0199.0FRFrance
220224739644787259.0105635.0145131.0159.0FRFrance
320224636773560075.075395.010290.0114.0FRFrance
420224534530638909.051703.06858.078.0FRFrance
520224433471328880.040546.05243.061.0FRFrance
620224334476936884.052654.06856.080.0FRFrance
720224234746240773.054151.07262.082.0FRFrance
820224134858342388.054778.07364.082.0FRFrance
920224034192736115.047739.06354.072.0FRFrance
1020223933990234168.045636.06051.069.0FRFrance
1120223832878123733.033829.04335.051.0FRFrance
1220223732139517076.025714.03225.039.0FRFrance
1320223631412010487.017753.02116.026.0FRFrance
14202235392836485.012081.01410.018.0FRFrance
15202234374984731.010265.0117.015.0FRFrance
16202233375864442.010730.0116.016.0FRFrance
172022323122227749.016695.01811.025.0FRFrance
182022313132578905.017609.02013.027.0FRFrance
1920223031500610738.019274.02317.029.0FRFrance
2020222932080115829.025773.03124.038.0FRFrance
2120222832338717970.028804.03527.043.0FRFrance
2220222733601529709.042321.05444.064.0FRFrance
2320222632942124314.034528.04436.052.0FRFrance
2420222532288718582.027192.03529.041.0FRFrance
2520222431929415406.023182.02923.035.0FRFrance
2620222331715913450.020868.02620.032.0FRFrance
2720222231423910930.017548.02116.026.0FRFrance
282022213118048686.014922.01813.023.0FRFrance
2920222031735513600.021110.02620.032.0FRFrance
.................................
195919852132609619621.032571.04735.059.0FRFrance
196019852032789620885.034907.05138.064.0FRFrance
196119851934315432821.053487.07859.097.0FRFrance
196219851834055529935.051175.07455.093.0FRFrance
196319851733405324366.043740.06244.080.0FRFrance
196419851635036236451.064273.09166.0116.0FRFrance
196519851536388145538.082224.011683.0149.0FRFrance
19661985143134545114400.0154690.0244207.0281.0FRFrance
19671985133197206176080.0218332.0357319.0395.0FRFrance
19681985123245240223304.0267176.0445405.0485.0FRFrance
19691985113276205252399.0300011.0501458.0544.0FRFrance
19701985103353231326279.0380183.0640591.0689.0FRFrance
19711985093369895341109.0398681.0670618.0722.0FRFrance
19721985083389886359529.0420243.0707652.0762.0FRFrance
19731985073471852432599.0511105.0855784.0926.0FRFrance
19741985063565825518011.0613639.01026939.01113.0FRFrance
19751985053637302592795.0681809.011551074.01236.0FRFrance
19761985043424937390794.0459080.0770708.0832.0FRFrance
19771985033213901174689.0253113.0388317.0459.0FRFrance
197819850239758680949.0114223.0177147.0207.0FRFrance
197919850138548965918.0105060.0155120.0190.0FRFrance
198019845238483060602.0109058.0154110.0198.0FRFrance
1981198451310172680242.0123210.0185146.0224.0FRFrance
19821984503123680101401.0145959.0225184.0266.0FRFrance
1983198449310107381684.0120462.0184149.0219.0FRFrance
198419844837862060634.096606.0143110.0176.0FRFrance
198519844737202954274.089784.013199.0163.0FRFrance
198619844638733067686.0106974.0159123.0195.0FRFrance
19871984453135223101414.0169032.0246184.0308.0FRFrance
198819844436842220056.0116788.012537.0213.0FRFrance
\n", "

1989 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202249 3 164351 152570.0 176132.0 248 230.0 \n", "1 202248 3 121884 111932.0 131836.0 184 169.0 \n", "2 202247 3 96447 87259.0 105635.0 145 131.0 \n", "3 202246 3 67735 60075.0 75395.0 102 90.0 \n", "4 202245 3 45306 38909.0 51703.0 68 58.0 \n", "5 202244 3 34713 28880.0 40546.0 52 43.0 \n", "6 202243 3 44769 36884.0 52654.0 68 56.0 \n", "7 202242 3 47462 40773.0 54151.0 72 62.0 \n", "8 202241 3 48583 42388.0 54778.0 73 64.0 \n", "9 202240 3 41927 36115.0 47739.0 63 54.0 \n", "10 202239 3 39902 34168.0 45636.0 60 51.0 \n", "11 202238 3 28781 23733.0 33829.0 43 35.0 \n", "12 202237 3 21395 17076.0 25714.0 32 25.0 \n", "13 202236 3 14120 10487.0 17753.0 21 16.0 \n", "14 202235 3 9283 6485.0 12081.0 14 10.0 \n", "15 202234 3 7498 4731.0 10265.0 11 7.0 \n", "16 202233 3 7586 4442.0 10730.0 11 6.0 \n", "17 202232 3 12222 7749.0 16695.0 18 11.0 \n", "18 202231 3 13257 8905.0 17609.0 20 13.0 \n", "19 202230 3 15006 10738.0 19274.0 23 17.0 \n", "20 202229 3 20801 15829.0 25773.0 31 24.0 \n", "21 202228 3 23387 17970.0 28804.0 35 27.0 \n", "22 202227 3 36015 29709.0 42321.0 54 44.0 \n", "23 202226 3 29421 24314.0 34528.0 44 36.0 \n", "24 202225 3 22887 18582.0 27192.0 35 29.0 \n", "25 202224 3 19294 15406.0 23182.0 29 23.0 \n", "26 202223 3 17159 13450.0 20868.0 26 20.0 \n", "27 202222 3 14239 10930.0 17548.0 21 16.0 \n", "28 202221 3 11804 8686.0 14922.0 18 13.0 \n", "29 202220 3 17355 13600.0 21110.0 26 20.0 \n", "... ... ... ... ... ... ... ... \n", "1959 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1960 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1961 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1962 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1963 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1964 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1965 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1966 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1967 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1968 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1969 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1970 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1971 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1972 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1973 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1974 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1975 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1976 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1977 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1978 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1979 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1980 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1981 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1982 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1983 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1984 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1985 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1986 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1987 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1988 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 266.0 FR France \n", "1 199.0 FR France \n", "2 159.0 FR France \n", "3 114.0 FR France \n", "4 78.0 FR France \n", "5 61.0 FR France \n", "6 80.0 FR France \n", "7 82.0 FR France \n", "8 82.0 FR France \n", "9 72.0 FR France \n", "10 69.0 FR France \n", "11 51.0 FR France \n", "12 39.0 FR France \n", "13 26.0 FR France \n", "14 18.0 FR France \n", "15 15.0 FR France \n", "16 16.0 FR France \n", "17 25.0 FR France \n", "18 27.0 FR France \n", "19 29.0 FR France \n", "20 38.0 FR France \n", "21 43.0 FR France \n", "22 64.0 FR France \n", "23 52.0 FR France \n", "24 41.0 FR France \n", "25 35.0 FR France \n", "26 32.0 FR France \n", "27 26.0 FR France \n", "28 23.0 FR France \n", "29 32.0 FR France \n", "... ... ... ... \n", "1959 59.0 FR France \n", "1960 64.0 FR France \n", "1961 97.0 FR France \n", "1962 93.0 FR France \n", "1963 80.0 FR France \n", "1964 116.0 FR France \n", "1965 149.0 FR France \n", "1966 281.0 FR France \n", "1967 395.0 FR France \n", "1968 485.0 FR France \n", "1969 544.0 FR France \n", "1970 689.0 FR France \n", "1971 722.0 FR France \n", "1972 762.0 FR France \n", "1973 926.0 FR France \n", "1974 1113.0 FR France \n", "1975 1236.0 FR France \n", "1976 832.0 FR France \n", "1977 459.0 FR France \n", "1978 207.0 FR France \n", "1979 190.0 FR France \n", "1980 198.0 FR France \n", "1981 224.0 FR France \n", "1982 266.0 FR France \n", "1983 219.0 FR France \n", "1984 176.0 FR France \n", "1985 163.0 FR France \n", "1986 195.0 FR France \n", "1987 308.0 FR France \n", "1988 213.0 FR France \n", "\n", "[1989 rows x 10 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data = pd.read_csv(filepath, 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 }