{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 17, "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": 18, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/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": 32, "metadata": {}, "outputs": [ { "ename": "EmptyDataError", "evalue": "No columns to parse from file", "output_type": "error", "traceback": [ 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\u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mEmptyDataError\u001b[0m: No columns to parse from file" ] } ], "source": [ "import os.path\n", "import requests\n", "\n", "filename = 'data_7.csv'\n", "\n", "if os.path.isfile(filename):\n", " print(f'{filename} already exists, download not necessary')\n", "else:\n", " r = requests.get(data_url)\n", " with open(filename, 'w') as f:\n", " f.write(str(r.content))\n", " print(str(r.content)\n", " \n", "raw_data = pd.read_csv(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": 33, "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
172419891930NaNNaN0NaNNaNFRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1724 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1724 FR France " ] }, "execution_count": 33, "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": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020222131753812255.022821.02618.034.0FRFrance
120222032041316271.024555.03125.037.0FRFrance
220221931787414068.021680.02721.033.0FRFrance
320221833035325089.035617.04638.054.0FRFrance
420221733600630373.041639.05446.062.0FRFrance
520221634994942836.057062.07564.086.0FRFrance
6202215310080690824.0110788.0152137.0167.0FRFrance
72022143155441143891.0166991.0234217.0251.0FRFrance
82022133191914179558.0204270.0289270.0308.0FRFrance
92022123166224155035.0177413.0251234.0268.0FRFrance
102022113122849113306.0132392.0185171.0199.0FRFrance
1120221038790479741.096067.0133121.0145.0FRFrance
1220220935018243958.056406.07667.085.0FRFrance
1320220833096325942.035984.04739.055.0FRFrance
1420220733488229446.040318.05345.061.0FRFrance
1520220634662340398.052848.07061.079.0FRFrance
1620220536297056043.069897.09585.0105.0FRFrance
1720220437220964804.079614.010998.0120.0FRFrance
1820220337461367144.082082.0113102.0124.0FRFrance
1920220235592049511.062329.08474.094.0FRFrance
2020220135762950699.064559.08777.097.0FRFrance
2120215235434947029.061669.08271.093.0FRFrance
2220215134169835359.048037.06353.073.0FRFrance
2320215033811732497.043737.05849.067.0FRFrance
2420214934016834716.045620.06153.069.0FRFrance
2520214834184236364.047320.06355.071.0FRFrance
2620214733659831338.041858.05547.063.0FRFrance
2720214633005925302.034816.04639.053.0FRFrance
2820214532036416564.024164.03125.037.0FRFrance
2920214431899915042.022956.02923.035.0FRFrance
.................................
193119852132609619621.032571.04735.059.0FRFrance
193219852032789620885.034907.05138.064.0FRFrance
193319851934315432821.053487.07859.097.0FRFrance
193419851834055529935.051175.07455.093.0FRFrance
193519851733405324366.043740.06244.080.0FRFrance
193619851635036236451.064273.09166.0116.0FRFrance
193719851536388145538.082224.011683.0149.0FRFrance
19381985143134545114400.0154690.0244207.0281.0FRFrance
19391985133197206176080.0218332.0357319.0395.0FRFrance
19401985123245240223304.0267176.0445405.0485.0FRFrance
19411985113276205252399.0300011.0501458.0544.0FRFrance
19421985103353231326279.0380183.0640591.0689.0FRFrance
19431985093369895341109.0398681.0670618.0722.0FRFrance
19441985083389886359529.0420243.0707652.0762.0FRFrance
19451985073471852432599.0511105.0855784.0926.0FRFrance
19461985063565825518011.0613639.01026939.01113.0FRFrance
19471985053637302592795.0681809.011551074.01236.0FRFrance
19481985043424937390794.0459080.0770708.0832.0FRFrance
19491985033213901174689.0253113.0388317.0459.0FRFrance
195019850239758680949.0114223.0177147.0207.0FRFrance
195119850138548965918.0105060.0155120.0190.0FRFrance
195219845238483060602.0109058.0154110.0198.0FRFrance
1953198451310172680242.0123210.0185146.0224.0FRFrance
19541984503123680101401.0145959.0225184.0266.0FRFrance
1955198449310107381684.0120462.0184149.0219.0FRFrance
195619844837862060634.096606.0143110.0176.0FRFrance
195719844737202954274.089784.013199.0163.0FRFrance
195819844638733067686.0106974.0159123.0195.0FRFrance
19591984453135223101414.0169032.0246184.0308.0FRFrance
196019844436842220056.0116788.012537.0213.0FRFrance
\n", "

1960 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202221 3 17538 12255.0 22821.0 26 18.0 \n", "1 202220 3 20413 16271.0 24555.0 31 25.0 \n", "2 202219 3 17874 14068.0 21680.0 27 21.0 \n", "3 202218 3 30353 25089.0 35617.0 46 38.0 \n", "4 202217 3 36006 30373.0 41639.0 54 46.0 \n", "5 202216 3 49949 42836.0 57062.0 75 64.0 \n", "6 202215 3 100806 90824.0 110788.0 152 137.0 \n", "7 202214 3 155441 143891.0 166991.0 234 217.0 \n", "8 202213 3 191914 179558.0 204270.0 289 270.0 \n", "9 202212 3 166224 155035.0 177413.0 251 234.0 \n", "10 202211 3 122849 113306.0 132392.0 185 171.0 \n", "11 202210 3 87904 79741.0 96067.0 133 121.0 \n", "12 202209 3 50182 43958.0 56406.0 76 67.0 \n", "13 202208 3 30963 25942.0 35984.0 47 39.0 \n", "14 202207 3 34882 29446.0 40318.0 53 45.0 \n", "15 202206 3 46623 40398.0 52848.0 70 61.0 \n", "16 202205 3 62970 56043.0 69897.0 95 85.0 \n", "17 202204 3 72209 64804.0 79614.0 109 98.0 \n", "18 202203 3 74613 67144.0 82082.0 113 102.0 \n", "19 202202 3 55920 49511.0 62329.0 84 74.0 \n", "20 202201 3 57629 50699.0 64559.0 87 77.0 \n", "21 202152 3 54349 47029.0 61669.0 82 71.0 \n", "22 202151 3 41698 35359.0 48037.0 63 53.0 \n", "23 202150 3 38117 32497.0 43737.0 58 49.0 \n", "24 202149 3 40168 34716.0 45620.0 61 53.0 \n", "25 202148 3 41842 36364.0 47320.0 63 55.0 \n", "26 202147 3 36598 31338.0 41858.0 55 47.0 \n", "27 202146 3 30059 25302.0 34816.0 46 39.0 \n", "28 202145 3 20364 16564.0 24164.0 31 25.0 \n", "29 202144 3 18999 15042.0 22956.0 29 23.0 \n", "... ... ... ... ... ... ... ... \n", "1931 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1932 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1933 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1934 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1935 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1936 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1937 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1938 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1939 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1940 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1941 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1942 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1943 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1944 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1945 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1946 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1947 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1948 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1949 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1950 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1951 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1952 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1953 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1954 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1955 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1956 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1957 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1958 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1959 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1960 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 34.0 FR France \n", "1 37.0 FR France \n", "2 33.0 FR France \n", "3 54.0 FR France \n", "4 62.0 FR France \n", "5 86.0 FR France \n", "6 167.0 FR France \n", "7 251.0 FR France \n", "8 308.0 FR France \n", "9 268.0 FR France \n", "10 199.0 FR France \n", "11 145.0 FR France \n", "12 85.0 FR France \n", "13 55.0 FR France \n", "14 61.0 FR France \n", "15 79.0 FR France \n", "16 105.0 FR France \n", "17 120.0 FR France \n", "18 124.0 FR France \n", "19 94.0 FR France \n", "20 97.0 FR France \n", "21 93.0 FR France \n", "22 73.0 FR France \n", "23 67.0 FR France \n", "24 69.0 FR France \n", "25 71.0 FR France \n", "26 63.0 FR France \n", "27 53.0 FR France \n", "28 37.0 FR France \n", "29 35.0 FR France \n", "... ... ... ... \n", "1931 59.0 FR France \n", "1932 64.0 FR France \n", "1933 97.0 FR France \n", "1934 93.0 FR France \n", "1935 80.0 FR France \n", "1936 116.0 FR France \n", "1937 149.0 FR France \n", "1938 281.0 FR France \n", "1939 395.0 FR France \n", "1940 485.0 FR France \n", "1941 544.0 FR France \n", "1942 689.0 FR France \n", "1943 722.0 FR France \n", "1944 762.0 FR France \n", "1945 926.0 FR France \n", "1946 1113.0 FR France \n", "1947 1236.0 FR France \n", "1948 832.0 FR France \n", "1949 459.0 FR France \n", "1950 207.0 FR France \n", "1951 190.0 FR France \n", "1952 198.0 FR France \n", "1953 224.0 FR France \n", "1954 266.0 FR France \n", "1955 219.0 FR France \n", "1956 176.0 FR France \n", "1957 163.0 FR France \n", "1958 195.0 FR France \n", "1959 308.0 FR France \n", "1960 213.0 FR France \n", "\n", "[1960 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "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": 7, "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": 8, "metadata": {}, "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": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" ] } ], "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": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "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": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "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": 12, "metadata": {}, "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": 13, "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2021 938731\n", "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2053781\n", "2012 2175217\n", "2003 2234584\n", "2019 2254386\n", "2006 2307352\n", "2017 2321583\n", "2001 2529279\n", "1992 2574578\n", "1993 2703886\n", "2018 2705325\n", "1988 2765617\n", "2007 2780164\n", "1987 2855570\n", "2016 2856393\n", "2011 2857040\n", "2008 2973918\n", "1998 3034904\n", "2002 3125418\n", "2009 3444020\n", "1994 3514763\n", "1996 3539413\n", "2004 3567744\n", "1997 3620066\n", "2015 3654892\n", "2000 3826372\n", "2005 3835025\n", "1999 3908112\n", "2010 4111392\n", "2013 4182691\n", "1986 5115251\n", "1990 5235827\n", "1989 5466192\n", "dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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