diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..6a6b3668f84955012bd12becc7f886070090d288 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2500 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence de la varicelle\n", + "\n", + "Pour réaliser ce document computationnel, j'ai pris comme base les notebooks sur l'analyse de l'incidence du syndrôme grippal disponible [ici](https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/blob/master/module3/ressources/analyse-syndrome-grippal-jupyter.ipynb) et [ici](https://app-learninglab.inria.fr/moocrr/jupyter/services/nbdime/difftool?base=https%3A%2F%2Fapp-learninglab.inria.fr%2Fmoocrr%2Fgitlab%2Flearning-lab%2Fmooc-rr-corrections%2Fraw%2Fmaster%2Fmodule3%2Fexo1%2Fanalyse-syndrome-grippal.ipynb&remote=https%3A%2F%2Fapp-learninglab.inria.fr%2Fmoocrr%2Fgitlab%2Fa12707740afaaa5fb65905502cc0ab13%2Fmooc-rr%2Fraw%2Fmaster%2Fmodule3%2Fexo1%2Fanalyse-syndrome-grippal.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "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 de la varicelle 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": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv?v=77dym\"" + ] + }, + { + "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 risqué 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": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_file = \"varicelle.csv\"\n", + "\n", + "import os\n", + "import urllib.request\n", + "if not os.path.exists(data_file):\n", + " urllib.request.urlretrieve(data_url, data_file)" + ] + }, + { + "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": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1716 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202342 7 5150 1291 9009 8 2 \n", + "1 202341 7 3404 1786 5022 5 3 \n", + "2 202340 7 2845 1410 4280 4 2 \n", + "3 202339 7 1739 629 2849 3 1 \n", + "4 202338 7 1663 274 3052 3 1 \n", + "5 202337 7 1122 223 2021 2 1 \n", + "6 202336 7 726 10 1442 1 0 \n", + "7 202335 7 961 96 1826 1 0 \n", + "8 202334 7 1168 9 2327 2 0 \n", + "9 202333 7 3308 1184 5432 5 2 \n", + "10 202332 7 7996 1120 14872 12 2 \n", + "11 202331 7 3318 1398 5238 5 2 \n", + "12 202330 7 5821 3269 8373 9 5 \n", + "13 202329 7 13558 8297 18819 20 12 \n", + "14 202328 7 6700 4043 9357 10 6 \n", + "15 202327 7 7253 4599 9907 11 7 \n", + "16 202326 7 9192 6223 12161 14 10 \n", + "17 202325 7 11498 8257 14739 17 12 \n", + "18 202324 7 11115 7968 14262 17 12 \n", + "19 202323 7 12563 6134 18992 19 9 \n", + "20 202322 7 12184 8125 16243 18 12 \n", + "21 202321 7 11349 7598 15100 17 11 \n", + "22 202320 7 9000 4615 13385 14 7 \n", + "23 202319 7 9344 6091 12597 14 9 \n", + "24 202318 7 10671 7291 14051 16 11 \n", + "25 202317 7 9184 6162 12206 14 9 \n", + "26 202316 7 11387 8014 14760 17 12 \n", + "27 202315 7 14040 7613 20467 21 11 \n", + "28 202314 7 15247 11032 19462 23 17 \n", + "29 202313 7 13322 9700 16944 20 15 \n", + "... ... ... ... ... ... ... ... \n", + "1686 199126 7 17608 11304 23912 31 20 \n", + "1687 199125 7 16169 10700 21638 28 18 \n", + "1688 199124 7 16171 10071 22271 28 17 \n", + "1689 199123 7 11947 7671 16223 21 13 \n", + "1690 199122 7 15452 9953 20951 27 17 \n", + "1691 199121 7 14903 8975 20831 26 16 \n", + "1692 199120 7 19053 12742 25364 34 23 \n", + "1693 199119 7 16739 11246 22232 29 19 \n", + "1694 199118 7 21385 13882 28888 38 25 \n", + "1695 199117 7 13462 8877 18047 24 16 \n", + "1696 199116 7 14857 10068 19646 26 18 \n", + "1697 199115 7 13975 9781 18169 25 18 \n", + "1698 199114 7 12265 7684 16846 22 14 \n", + "1699 199113 7 9567 6041 13093 17 11 \n", + "1700 199112 7 10864 7331 14397 19 13 \n", + "1701 199111 7 15574 11184 19964 27 19 \n", + "1702 199110 7 16643 11372 21914 29 20 \n", + "1703 199109 7 13741 8780 18702 24 15 \n", + "1704 199108 7 13289 8813 17765 23 15 \n", + "1705 199107 7 12337 8077 16597 22 15 \n", + "1706 199106 7 10877 7013 14741 19 12 \n", + "1707 199105 7 10442 6544 14340 18 11 \n", + "1708 199104 7 7913 4563 11263 14 8 \n", + "1709 199103 7 15387 10484 20290 27 18 \n", + "1710 199102 7 16277 11046 21508 29 20 \n", + "1711 199101 7 15565 10271 20859 27 18 \n", + "1712 199052 7 19375 13295 25455 34 23 \n", + "1713 199051 7 19080 13807 24353 34 25 \n", + "1714 199050 7 11079 6660 15498 20 12 \n", + "1715 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 14 FR France \n", + "1 7 FR France \n", + "2 6 FR France \n", + "3 5 FR France \n", + "4 5 FR France \n", + "5 3 FR France \n", + "6 2 FR France \n", + "7 2 FR France \n", + "8 4 FR France \n", + "9 8 FR France \n", + "10 22 FR France \n", + "11 8 FR France \n", + "12 13 FR France \n", + "13 28 FR France \n", + "14 14 FR France \n", + "15 15 FR France \n", + "16 18 FR France \n", + "17 22 FR France \n", + "18 22 FR France \n", + "19 29 FR France \n", + "20 24 FR France \n", + "21 23 FR France \n", + "22 21 FR France \n", + "23 19 FR France \n", + "24 21 FR France \n", + "25 19 FR France \n", + "26 22 FR France \n", + "27 31 FR France \n", + "28 29 FR France \n", + "29 25 FR France \n", + "... ... ... ... \n", + "1686 42 FR France \n", + "1687 38 FR France \n", + "1688 39 FR France \n", + "1689 29 FR France \n", + "1690 37 FR France \n", + "1691 36 FR France \n", + "1692 45 FR France \n", + "1693 39 FR France \n", + "1694 51 FR France \n", + "1695 32 FR France \n", + "1696 34 FR France \n", + "1697 32 FR France \n", + "1698 30 FR France \n", + "1699 23 FR France \n", + "1700 25 FR France \n", + "1701 35 FR France \n", + "1702 38 FR France \n", + "1703 33 FR France \n", + "1704 31 FR France \n", + "1705 29 FR France \n", + "1706 26 FR France \n", + "1707 25 FR France \n", + "1708 20 FR France \n", + "1709 36 FR France \n", + "1710 38 FR France \n", + "1711 36 FR France \n", + "1712 45 FR France \n", + "1713 43 FR France \n", + "1714 28 FR France \n", + "1715 5 FR France \n", + "\n", + "[1716 rows x 10 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_file, encoding = 'iso-8859-1', skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", + "Index: []" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Non pas cette fois !" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "data = raw_data.copy()" + ] + }, + { + "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": 8, + "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": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1990-12-03/1990-12-091990497114302610205FRFrance
1990-12-10/1990-12-16199050711079666015498201228FRFrance
1990-12-17/1990-12-231990517190801380724353342543FRFrance
1990-12-24/1990-12-301990527193751329525455342345FRFrance
1990-12-31/1991-01-061991017155651027120859271836FRFrance
1991-01-07/1991-01-131991027162771104621508292038FRFrance
1991-01-14/1991-01-201991037153871048420290271836FRFrance
1991-01-21/1991-01-271991047791345631126314820FRFrance
1991-01-28/1991-02-03199105710442654414340181125FRFrance
1991-02-04/1991-02-10199106710877701314741191226FRFrance
1991-02-11/1991-02-17199107712337807716597221529FRFrance
1991-02-18/1991-02-24199108713289881317765231531FRFrance
1991-02-25/1991-03-03199109713741878018702241533FRFrance
1991-03-04/1991-03-101991107166431137221914292038FRFrance
1991-03-11/1991-03-171991117155741118419964271935FRFrance
1991-03-18/1991-03-24199112710864733114397191325FRFrance
1991-03-25/1991-03-3119911379567604113093171123FRFrance
1991-04-01/1991-04-07199114712265768416846221430FRFrance
1991-04-08/1991-04-14199115713975978118169251832FRFrance
1991-04-15/1991-04-211991167148571006819646261834FRFrance
1991-04-22/1991-04-28199117713462887718047241632FRFrance
1991-04-29/1991-05-051991187213851388228888382551FRFrance
1991-05-06/1991-05-121991197167391124622232291939FRFrance
1991-05-13/1991-05-191991207190531274225364342345FRFrance
1991-05-20/1991-05-26199121714903897520831261636FRFrance
1991-05-27/1991-06-02199122715452995320951271737FRFrance
1991-06-03/1991-06-09199123711947767116223211329FRFrance
1991-06-10/1991-06-161991247161711007122271281739FRFrance
1991-06-17/1991-06-231991257161691070021638281838FRFrance
1991-06-24/1991-06-301991267176081130423912312042FRFrance
.................................
2023-03-27/2023-04-02202313713322970016944201525FRFrance
2023-04-03/2023-04-092023147152471103219462231729FRFrance
2023-04-10/2023-04-16202315714040761320467211131FRFrance
2023-04-17/2023-04-23202316711387801414760171222FRFrance
2023-04-24/2023-04-302023177918461621220614919FRFrance
2023-05-01/2023-05-07202318710671729114051161121FRFrance
2023-05-08/2023-05-142023197934460911259714919FRFrance
2023-05-15/2023-05-212023207900046151338514721FRFrance
2023-05-22/2023-05-28202321711349759815100171123FRFrance
2023-05-29/2023-06-04202322712184812516243181224FRFrance
2023-06-05/2023-06-1120232371256361341899219929FRFrance
2023-06-12/2023-06-18202324711115796814262171222FRFrance
2023-06-19/2023-06-25202325711498825714739171222FRFrance
2023-06-26/2023-07-0220232679192622312161141018FRFrance
2023-07-03/2023-07-09202327772534599990711715FRFrance
2023-07-10/2023-07-16202328767004043935710614FRFrance
2023-07-17/2023-07-23202329713558829718819201228FRFrance
2023-07-24/2023-07-3020233075821326983739513FRFrance
2023-07-31/2023-08-062023317331813985238528FRFrance
2023-08-07/2023-08-132023327799611201487212222FRFrance
2023-08-14/2023-08-202023337330811845432528FRFrance
2023-08-21/2023-08-272023347116892327204FRFrance
2023-08-28/2023-09-032023357961961826102FRFrance
2023-09-04/2023-09-102023367726101442102FRFrance
2023-09-11/2023-09-17202337711222232021213FRFrance
2023-09-18/2023-09-24202338716632743052315FRFrance
2023-09-25/2023-10-01202339717396292849315FRFrance
2023-10-02/2023-10-082023407284514104280426FRFrance
2023-10-09/2023-10-152023417340417865022537FRFrance
2023-10-16/2023-10-2220234275150129190098214FRFrance
\n", + "

1716 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 \\\n", + "period \n", + "1990-12-03/1990-12-09 199049 7 1143 0 2610 2 \n", + "1990-12-10/1990-12-16 199050 7 11079 6660 15498 20 \n", + "1990-12-17/1990-12-23 199051 7 19080 13807 24353 34 \n", + "1990-12-24/1990-12-30 199052 7 19375 13295 25455 34 \n", + "1990-12-31/1991-01-06 199101 7 15565 10271 20859 27 \n", + "1991-01-07/1991-01-13 199102 7 16277 11046 21508 29 \n", + "1991-01-14/1991-01-20 199103 7 15387 10484 20290 27 \n", + "1991-01-21/1991-01-27 199104 7 7913 4563 11263 14 \n", + "1991-01-28/1991-02-03 199105 7 10442 6544 14340 18 \n", + "1991-02-04/1991-02-10 199106 7 10877 7013 14741 19 \n", + "1991-02-11/1991-02-17 199107 7 12337 8077 16597 22 \n", + "1991-02-18/1991-02-24 199108 7 13289 8813 17765 23 \n", + "1991-02-25/1991-03-03 199109 7 13741 8780 18702 24 \n", + "1991-03-04/1991-03-10 199110 7 16643 11372 21914 29 \n", + "1991-03-11/1991-03-17 199111 7 15574 11184 19964 27 \n", + "1991-03-18/1991-03-24 199112 7 10864 7331 14397 19 \n", + "1991-03-25/1991-03-31 199113 7 9567 6041 13093 17 \n", + "1991-04-01/1991-04-07 199114 7 12265 7684 16846 22 \n", + "1991-04-08/1991-04-14 199115 7 13975 9781 18169 25 \n", + "1991-04-15/1991-04-21 199116 7 14857 10068 19646 26 \n", + "1991-04-22/1991-04-28 199117 7 13462 8877 18047 24 \n", + "1991-04-29/1991-05-05 199118 7 21385 13882 28888 38 \n", + "1991-05-06/1991-05-12 199119 7 16739 11246 22232 29 \n", + "1991-05-13/1991-05-19 199120 7 19053 12742 25364 34 \n", + "1991-05-20/1991-05-26 199121 7 14903 8975 20831 26 \n", + "1991-05-27/1991-06-02 199122 7 15452 9953 20951 27 \n", + "1991-06-03/1991-06-09 199123 7 11947 7671 16223 21 \n", + "1991-06-10/1991-06-16 199124 7 16171 10071 22271 28 \n", + "1991-06-17/1991-06-23 199125 7 16169 10700 21638 28 \n", + "1991-06-24/1991-06-30 199126 7 17608 11304 23912 31 \n", + "... ... ... ... ... ... ... \n", + "2023-03-27/2023-04-02 202313 7 13322 9700 16944 20 \n", + "2023-04-03/2023-04-09 202314 7 15247 11032 19462 23 \n", + "2023-04-10/2023-04-16 202315 7 14040 7613 20467 21 \n", + "2023-04-17/2023-04-23 202316 7 11387 8014 14760 17 \n", + "2023-04-24/2023-04-30 202317 7 9184 6162 12206 14 \n", + "2023-05-01/2023-05-07 202318 7 10671 7291 14051 16 \n", + "2023-05-08/2023-05-14 202319 7 9344 6091 12597 14 \n", + "2023-05-15/2023-05-21 202320 7 9000 4615 13385 14 \n", + "2023-05-22/2023-05-28 202321 7 11349 7598 15100 17 \n", + "2023-05-29/2023-06-04 202322 7 12184 8125 16243 18 \n", + "2023-06-05/2023-06-11 202323 7 12563 6134 18992 19 \n", + "2023-06-12/2023-06-18 202324 7 11115 7968 14262 17 \n", + "2023-06-19/2023-06-25 202325 7 11498 8257 14739 17 \n", + "2023-06-26/2023-07-02 202326 7 9192 6223 12161 14 \n", + "2023-07-03/2023-07-09 202327 7 7253 4599 9907 11 \n", + "2023-07-10/2023-07-16 202328 7 6700 4043 9357 10 \n", + "2023-07-17/2023-07-23 202329 7 13558 8297 18819 20 \n", + "2023-07-24/2023-07-30 202330 7 5821 3269 8373 9 \n", + "2023-07-31/2023-08-06 202331 7 3318 1398 5238 5 \n", + "2023-08-07/2023-08-13 202332 7 7996 1120 14872 12 \n", + "2023-08-14/2023-08-20 202333 7 3308 1184 5432 5 \n", + "2023-08-21/2023-08-27 202334 7 1168 9 2327 2 \n", + "2023-08-28/2023-09-03 202335 7 961 96 1826 1 \n", + "2023-09-04/2023-09-10 202336 7 726 10 1442 1 \n", + "2023-09-11/2023-09-17 202337 7 1122 223 2021 2 \n", + "2023-09-18/2023-09-24 202338 7 1663 274 3052 3 \n", + "2023-09-25/2023-10-01 202339 7 1739 629 2849 3 \n", + "2023-10-02/2023-10-08 202340 7 2845 1410 4280 4 \n", + "2023-10-09/2023-10-15 202341 7 3404 1786 5022 5 \n", + "2023-10-16/2023-10-22 202342 7 5150 1291 9009 8 \n", + "\n", + " inc100_low inc100_up geo_insee geo_name \n", + "period \n", + "1990-12-03/1990-12-09 0 5 FR France \n", + "1990-12-10/1990-12-16 12 28 FR France \n", + "1990-12-17/1990-12-23 25 43 FR France \n", + "1990-12-24/1990-12-30 23 45 FR France \n", + "1990-12-31/1991-01-06 18 36 FR France \n", + "1991-01-07/1991-01-13 20 38 FR France \n", + "1991-01-14/1991-01-20 18 36 FR France \n", + "1991-01-21/1991-01-27 8 20 FR France \n", + "1991-01-28/1991-02-03 11 25 FR France \n", + "1991-02-04/1991-02-10 12 26 FR France \n", + "1991-02-11/1991-02-17 15 29 FR France \n", + "1991-02-18/1991-02-24 15 31 FR France \n", + "1991-02-25/1991-03-03 15 33 FR France \n", + "1991-03-04/1991-03-10 20 38 FR France \n", + "1991-03-11/1991-03-17 19 35 FR France \n", + "1991-03-18/1991-03-24 13 25 FR France \n", + "1991-03-25/1991-03-31 11 23 FR France \n", + "1991-04-01/1991-04-07 14 30 FR France \n", + "1991-04-08/1991-04-14 18 32 FR France \n", + "1991-04-15/1991-04-21 18 34 FR France \n", + "1991-04-22/1991-04-28 16 32 FR France \n", + "1991-04-29/1991-05-05 25 51 FR France \n", + "1991-05-06/1991-05-12 19 39 FR France \n", + "1991-05-13/1991-05-19 23 45 FR France \n", + "1991-05-20/1991-05-26 16 36 FR France \n", + "1991-05-27/1991-06-02 17 37 FR France \n", + "1991-06-03/1991-06-09 13 29 FR France \n", + "1991-06-10/1991-06-16 17 39 FR France \n", + "1991-06-17/1991-06-23 18 38 FR France \n", + "1991-06-24/1991-06-30 20 42 FR France \n", + "... ... ... ... ... \n", + "2023-03-27/2023-04-02 15 25 FR France \n", + "2023-04-03/2023-04-09 17 29 FR France \n", + "2023-04-10/2023-04-16 11 31 FR France \n", + "2023-04-17/2023-04-23 12 22 FR France \n", + "2023-04-24/2023-04-30 9 19 FR France \n", + "2023-05-01/2023-05-07 11 21 FR France \n", + "2023-05-08/2023-05-14 9 19 FR France \n", + "2023-05-15/2023-05-21 7 21 FR France \n", + "2023-05-22/2023-05-28 11 23 FR France \n", + "2023-05-29/2023-06-04 12 24 FR France \n", + "2023-06-05/2023-06-11 9 29 FR France \n", + "2023-06-12/2023-06-18 12 22 FR France \n", + "2023-06-19/2023-06-25 12 22 FR France \n", + "2023-06-26/2023-07-02 10 18 FR France \n", + "2023-07-03/2023-07-09 7 15 FR France \n", + "2023-07-10/2023-07-16 6 14 FR France \n", + "2023-07-17/2023-07-23 12 28 FR France \n", + "2023-07-24/2023-07-30 5 13 FR France \n", + "2023-07-31/2023-08-06 2 8 FR France \n", + "2023-08-07/2023-08-13 2 22 FR France \n", + "2023-08-14/2023-08-20 2 8 FR France \n", + "2023-08-21/2023-08-27 0 4 FR France \n", + "2023-08-28/2023-09-03 0 2 FR France \n", + "2023-09-04/2023-09-10 0 2 FR France \n", + "2023-09-11/2023-09-17 1 3 FR France \n", + "2023-09-18/2023-09-24 1 5 FR France \n", + "2023-09-25/2023-10-01 1 5 FR France \n", + "2023-10-02/2023-10-08 2 6 FR France \n", + "2023-10-09/2023-10-15 3 7 FR France \n", + "2023-10-16/2023-10-22 2 14 FR France \n", + "\n", + "[1716 rows x 10 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data = data.set_index('period').sort_index()\n", + "sorted_data" + ] + }, + { + "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": 10, + "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": 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'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les résultats sont beaucoup moins lisibles que ceux de la grippe puisque contrairement à cette dernière, la varicelle n'a, *a priori*, pas de saison privilégiée. Faisons un zoom sur les dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "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'][-400:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En zoomant sur les 8 dernières années, il semble quand même qu'il y ait une légère période de creux qui se répète au fil des ans : entre juin et septembre. En 2020 cette période commence dès le deuxième trimestre mais le contexte du confinement y est peut-être pour quelque chose (moins de déclaration ?). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous définissons la période de référence du 1er septembre de l'année $N$ au 1er septembre de l'année $N+1$ comme demandé dans les instructions.\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 septembre.\n", + "\n", + "Encore un petit détail: les données commencent en décembre 1990, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", + " for y in range(1991,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En partant de cette liste des semaines qui contiennent un 1er septembre, 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": 16, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_sept_week[:-1],\n", + " first_sept_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": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\n", + "2021 376290\n", + "2002 516689\n", + "2018 542312\n", + "2017 551041\n", + "1996 564901\n", + "2019 584066\n", + "2015 604382\n", + "2000 617597\n", + "2001 619041\n", + "2012 624573\n", + "2005 628464\n", + "2006 632833\n", + "2022 641397\n", + "2011 642368\n", + "1993 643387\n", + "1995 652478\n", + "1994 661409\n", + "1998 677775\n", + "1997 683434\n", + "2014 685769\n", + "2013 698332\n", + "2007 717352\n", + "2008 749478\n", + "1999 756456\n", + "2003 758363\n", + "2004 777388\n", + "2016 782114\n", + "2010 829911\n", + "1992 832939\n", + "2009 842373\n", + "dtype: int64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, un histogramme montre bien que le nombre de cas de varicelle déclarés en France varie plus ou moins fortement selon les années. En très grande majorité (31 années sur 35 analysées), le nombre annuel d'incidence est compris entre 550000 et 850000 cas. En 2020 et 2021, celui-ci est exceptionnellement faible avec, respectivement 221186 et 376290 cas." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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