{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la varicelle\n", "\n", "Le travail présenté ici est fortement inspiré du travail déjà réalisé sur l'incidence du syndrôme grippale." ] }, { "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\n", "import os" ] }, { "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/france/fr/?page=table). 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 1991 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.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": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1714 rows × 11 columns

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" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 \\\n", "0 0 202340 7 3128 1412 4844 5 \n", "1 1 202339 7 1717 611 2823 3 \n", "2 2 202338 7 1663 274 3052 3 \n", "3 3 202337 7 1122 223 2021 2 \n", "4 4 202336 7 726 10 1442 1 \n", "5 5 202335 7 961 96 1826 1 \n", "6 6 202334 7 1168 9 2327 2 \n", "7 7 202333 7 3308 1184 5432 5 \n", "8 8 202332 7 7996 1120 14872 12 \n", "9 9 202331 7 3318 1398 5238 5 \n", "10 10 202330 7 5821 3269 8373 9 \n", "11 11 202329 7 13558 8297 18819 20 \n", "12 12 202328 7 6700 4043 9357 10 \n", "13 13 202327 7 7253 4599 9907 11 \n", "14 14 202326 7 9192 6223 12161 14 \n", "15 15 202325 7 11498 8257 14739 17 \n", "16 16 202324 7 11115 7968 14262 17 \n", "17 17 202323 7 12563 6134 18992 19 \n", "18 18 202322 7 12184 8125 16243 18 \n", "19 19 202321 7 11349 7598 15100 17 \n", "20 20 202320 7 9000 4615 13385 14 \n", "21 21 202319 7 9344 6091 12597 14 \n", "22 22 202318 7 10671 7291 14051 16 \n", "23 23 202317 7 9184 6162 12206 14 \n", "24 24 202316 7 11387 8014 14760 17 \n", "25 25 202315 7 14040 7613 20467 21 \n", "26 26 202314 7 15247 11032 19462 23 \n", "27 27 202313 7 13322 9700 16944 20 \n", "28 28 202312 7 10374 7218 13530 16 \n", "29 29 202311 7 4919 2880 6958 7 \n", "... ... ... ... ... ... ... ... \n", "1684 1684 199126 7 17608 11304 23912 31 \n", "1685 1685 199125 7 16169 10700 21638 28 \n", "1686 1686 199124 7 16171 10071 22271 28 \n", "1687 1687 199123 7 11947 7671 16223 21 \n", "1688 1688 199122 7 15452 9953 20951 27 \n", "1689 1689 199121 7 14903 8975 20831 26 \n", "1690 1690 199120 7 19053 12742 25364 34 \n", "1691 1691 199119 7 16739 11246 22232 29 \n", "1692 1692 199118 7 21385 13882 28888 38 \n", "1693 1693 199117 7 13462 8877 18047 24 \n", "1694 1694 199116 7 14857 10068 19646 26 \n", "1695 1695 199115 7 13975 9781 18169 25 \n", "1696 1696 199114 7 12265 7684 16846 22 \n", "1697 1697 199113 7 9567 6041 13093 17 \n", "1698 1698 199112 7 10864 7331 14397 19 \n", "1699 1699 199111 7 15574 11184 19964 27 \n", "1700 1700 199110 7 16643 11372 21914 29 \n", "1701 1701 199109 7 13741 8780 18702 24 \n", "1702 1702 199108 7 13289 8813 17765 23 \n", "1703 1703 199107 7 12337 8077 16597 22 \n", "1704 1704 199106 7 10877 7013 14741 19 \n", "1705 1705 199105 7 10442 6544 14340 18 \n", "1706 1706 199104 7 7913 4563 11263 14 \n", "1707 1707 199103 7 15387 10484 20290 27 \n", "1708 1708 199102 7 16277 11046 21508 29 \n", "1709 1709 199101 7 15565 10271 20859 27 \n", "1710 1710 199052 7 19375 13295 25455 34 \n", "1711 1711 199051 7 19080 13807 24353 34 \n", "1712 1712 199050 7 11079 6660 15498 20 \n", "1713 1713 199049 7 1143 0 2610 2 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 2 8 FR France \n", "1 1 5 FR France \n", "2 1 5 FR France \n", "3 1 3 FR France \n", "4 0 2 FR France \n", "5 0 2 FR France \n", "6 0 4 FR France \n", "7 2 8 FR France \n", "8 2 22 FR France \n", "9 2 8 FR France \n", "10 5 13 FR France \n", "11 12 28 FR France \n", "12 6 14 FR France \n", "13 7 15 FR France \n", "14 10 18 FR France \n", "15 12 22 FR France \n", "16 12 22 FR France \n", "17 9 29 FR France \n", "18 12 24 FR France \n", "19 11 23 FR France \n", "20 7 21 FR France \n", "21 9 19 FR France \n", "22 11 21 FR France \n", "23 9 19 FR France \n", "24 12 22 FR France \n", "25 11 31 FR France \n", "26 17 29 FR France \n", "27 15 25 FR France \n", "28 11 21 FR France \n", "29 4 10 FR France \n", "... ... ... ... ... \n", "1684 20 42 FR France \n", "1685 18 38 FR France \n", "1686 17 39 FR France \n", "1687 13 29 FR France \n", "1688 17 37 FR France \n", "1689 16 36 FR France \n", "1690 23 45 FR France \n", "1691 19 39 FR France \n", "1692 25 51 FR France \n", "1693 16 32 FR France \n", "1694 18 34 FR France \n", "1695 18 32 FR France \n", "1696 14 30 FR France \n", "1697 11 23 FR France \n", "1698 13 25 FR France \n", "1699 19 35 FR France \n", "1700 20 38 FR France \n", "1701 15 33 FR France \n", "1702 15 31 FR France \n", "1703 15 29 FR France \n", "1704 12 26 FR France \n", "1705 11 25 FR France \n", "1706 8 20 FR France \n", "1707 18 36 FR France \n", "1708 20 38 FR France \n", "1709 18 36 FR France \n", "1710 23 45 FR France \n", "1711 25 43 FR France \n", "1712 12 28 FR France \n", "1713 0 5 FR France \n", "\n", "[1714 rows x 11 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We try to get the dataset from local file and download it if it doesn't already exists\n", "try:\n", " raw_data = pd.read_csv('incidence_varicelle.csv')\n", "except FileNotFoundError:\n", " raw_data = pd.read_csv(data_url, skiprows=1)\n", " raw_data.to_csv('incidence_varicelle.csv')\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Non, les données ont l'air correctes" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Unnamed: 0, week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will work on a clean copy of our data, in case we modify thing" ] }, { "cell_type": "code", "execution_count": 5, "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": 6, "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": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1990-12-03/1990-12-0917131990497114302610205FRFrance
1990-12-10/1990-12-161712199050711079666015498201228FRFrance
1990-12-17/1990-12-2317111990517190801380724353342543FRFrance
1990-12-24/1990-12-3017101990527193751329525455342345FRFrance
1990-12-31/1991-01-0617091991017155651027120859271836FRFrance
1991-01-07/1991-01-1317081991027162771104621508292038FRFrance
1991-01-14/1991-01-2017071991037153871048420290271836FRFrance
1991-01-21/1991-01-2717061991047791345631126314820FRFrance
1991-01-28/1991-02-031705199105710442654414340181125FRFrance
1991-02-04/1991-02-101704199106710877701314741191226FRFrance
1991-02-11/1991-02-171703199107712337807716597221529FRFrance
1991-02-18/1991-02-241702199108713289881317765231531FRFrance
1991-02-25/1991-03-031701199109713741878018702241533FRFrance
1991-03-04/1991-03-1017001991107166431137221914292038FRFrance
1991-03-11/1991-03-1716991991117155741118419964271935FRFrance
1991-03-18/1991-03-241698199112710864733114397191325FRFrance
1991-03-25/1991-03-31169719911379567604113093171123FRFrance
1991-04-01/1991-04-071696199114712265768416846221430FRFrance
1991-04-08/1991-04-141695199115713975978118169251832FRFrance
1991-04-15/1991-04-2116941991167148571006819646261834FRFrance
1991-04-22/1991-04-281693199117713462887718047241632FRFrance
1991-04-29/1991-05-0516921991187213851388228888382551FRFrance
1991-05-06/1991-05-1216911991197167391124622232291939FRFrance
1991-05-13/1991-05-1916901991207190531274225364342345FRFrance
1991-05-20/1991-05-261689199121714903897520831261636FRFrance
1991-05-27/1991-06-021688199122715452995320951271737FRFrance
1991-06-03/1991-06-091687199123711947767116223211329FRFrance
1991-06-10/1991-06-1616861991247161711007122271281739FRFrance
1991-06-17/1991-06-2316851991257161691070021638281838FRFrance
1991-06-24/1991-06-3016841991267176081130423912312042FRFrance
....................................
2023-03-13/2023-03-192920231174919288069587410FRFrance
2023-03-20/2023-03-2628202312710374721813530161121FRFrance
2023-03-27/2023-04-0227202313713322970016944201525FRFrance
2023-04-03/2023-04-09262023147152471103219462231729FRFrance
2023-04-10/2023-04-1625202315714040761320467211131FRFrance
2023-04-17/2023-04-2324202316711387801414760171222FRFrance
2023-04-24/2023-04-30232023177918461621220614919FRFrance
2023-05-01/2023-05-0722202318710671729114051161121FRFrance
2023-05-08/2023-05-14212023197934460911259714919FRFrance
2023-05-15/2023-05-21202023207900046151338514721FRFrance
2023-05-22/2023-05-2819202321711349759815100171123FRFrance
2023-05-29/2023-06-0418202322712184812516243181224FRFrance
2023-06-05/2023-06-111720232371256361341899219929FRFrance
2023-06-12/2023-06-1816202324711115796814262171222FRFrance
2023-06-19/2023-06-2515202325711498825714739171222FRFrance
2023-06-26/2023-07-021420232679192622312161141018FRFrance
2023-07-03/2023-07-0913202327772534599990711715FRFrance
2023-07-10/2023-07-1612202328767004043935710614FRFrance
2023-07-17/2023-07-2311202329713558829718819201228FRFrance
2023-07-24/2023-07-301020233075821326983739513FRFrance
2023-07-31/2023-08-0692023317331813985238528FRFrance
2023-08-07/2023-08-1382023327799611201487212222FRFrance
2023-08-14/2023-08-2072023337330811845432528FRFrance
2023-08-21/2023-08-2762023347116892327204FRFrance
2023-08-28/2023-09-0352023357961961826102FRFrance
2023-09-04/2023-09-1042023367726101442102FRFrance
2023-09-11/2023-09-173202337711222232021213FRFrance
2023-09-18/2023-09-242202338716632743052315FRFrance
2023-09-25/2023-10-011202339717176112823315FRFrance
2023-10-02/2023-10-0802023407312814124844528FRFrance
\n", "

1714 rows × 11 columns

\n", "
" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up \\\n", "period \n", "1990-12-03/1990-12-09 1713 199049 7 1143 0 2610 \n", "1990-12-10/1990-12-16 1712 199050 7 11079 6660 15498 \n", "1990-12-17/1990-12-23 1711 199051 7 19080 13807 24353 \n", "1990-12-24/1990-12-30 1710 199052 7 19375 13295 25455 \n", "1990-12-31/1991-01-06 1709 199101 7 15565 10271 20859 \n", "1991-01-07/1991-01-13 1708 199102 7 16277 11046 21508 \n", "1991-01-14/1991-01-20 1707 199103 7 15387 10484 20290 \n", "1991-01-21/1991-01-27 1706 199104 7 7913 4563 11263 \n", "1991-01-28/1991-02-03 1705 199105 7 10442 6544 14340 \n", "1991-02-04/1991-02-10 1704 199106 7 10877 7013 14741 \n", "1991-02-11/1991-02-17 1703 199107 7 12337 8077 16597 \n", "1991-02-18/1991-02-24 1702 199108 7 13289 8813 17765 \n", "1991-02-25/1991-03-03 1701 199109 7 13741 8780 18702 \n", "1991-03-04/1991-03-10 1700 199110 7 16643 11372 21914 \n", "1991-03-11/1991-03-17 1699 199111 7 15574 11184 19964 \n", "1991-03-18/1991-03-24 1698 199112 7 10864 7331 14397 \n", "1991-03-25/1991-03-31 1697 199113 7 9567 6041 13093 \n", "1991-04-01/1991-04-07 1696 199114 7 12265 7684 16846 \n", "1991-04-08/1991-04-14 1695 199115 7 13975 9781 18169 \n", "1991-04-15/1991-04-21 1694 199116 7 14857 10068 19646 \n", "1991-04-22/1991-04-28 1693 199117 7 13462 8877 18047 \n", "1991-04-29/1991-05-05 1692 199118 7 21385 13882 28888 \n", "1991-05-06/1991-05-12 1691 199119 7 16739 11246 22232 \n", "1991-05-13/1991-05-19 1690 199120 7 19053 12742 25364 \n", "1991-05-20/1991-05-26 1689 199121 7 14903 8975 20831 \n", "1991-05-27/1991-06-02 1688 199122 7 15452 9953 20951 \n", "1991-06-03/1991-06-09 1687 199123 7 11947 7671 16223 \n", "1991-06-10/1991-06-16 1686 199124 7 16171 10071 22271 \n", "1991-06-17/1991-06-23 1685 199125 7 16169 10700 21638 \n", "1991-06-24/1991-06-30 1684 199126 7 17608 11304 23912 \n", "... ... ... ... ... ... ... \n", "2023-03-13/2023-03-19 29 202311 7 4919 2880 6958 \n", "2023-03-20/2023-03-26 28 202312 7 10374 7218 13530 \n", "2023-03-27/2023-04-02 27 202313 7 13322 9700 16944 \n", "2023-04-03/2023-04-09 26 202314 7 15247 11032 19462 \n", "2023-04-10/2023-04-16 25 202315 7 14040 7613 20467 \n", "2023-04-17/2023-04-23 24 202316 7 11387 8014 14760 \n", "2023-04-24/2023-04-30 23 202317 7 9184 6162 12206 \n", "2023-05-01/2023-05-07 22 202318 7 10671 7291 14051 \n", "2023-05-08/2023-05-14 21 202319 7 9344 6091 12597 \n", "2023-05-15/2023-05-21 20 202320 7 9000 4615 13385 \n", "2023-05-22/2023-05-28 19 202321 7 11349 7598 15100 \n", "2023-05-29/2023-06-04 18 202322 7 12184 8125 16243 \n", "2023-06-05/2023-06-11 17 202323 7 12563 6134 18992 \n", "2023-06-12/2023-06-18 16 202324 7 11115 7968 14262 \n", "2023-06-19/2023-06-25 15 202325 7 11498 8257 14739 \n", "2023-06-26/2023-07-02 14 202326 7 9192 6223 12161 \n", "2023-07-03/2023-07-09 13 202327 7 7253 4599 9907 \n", "2023-07-10/2023-07-16 12 202328 7 6700 4043 9357 \n", "2023-07-17/2023-07-23 11 202329 7 13558 8297 18819 \n", "2023-07-24/2023-07-30 10 202330 7 5821 3269 8373 \n", "2023-07-31/2023-08-06 9 202331 7 3318 1398 5238 \n", "2023-08-07/2023-08-13 8 202332 7 7996 1120 14872 \n", "2023-08-14/2023-08-20 7 202333 7 3308 1184 5432 \n", "2023-08-21/2023-08-27 6 202334 7 1168 9 2327 \n", "2023-08-28/2023-09-03 5 202335 7 961 96 1826 \n", "2023-09-04/2023-09-10 4 202336 7 726 10 1442 \n", "2023-09-11/2023-09-17 3 202337 7 1122 223 2021 \n", "2023-09-18/2023-09-24 2 202338 7 1663 274 3052 \n", "2023-09-25/2023-10-01 1 202339 7 1717 611 2823 \n", "2023-10-02/2023-10-08 0 202340 7 3128 1412 4844 \n", "\n", " inc100 inc100_low inc100_up geo_insee geo_name \n", "period \n", "1990-12-03/1990-12-09 2 0 5 FR France \n", "1990-12-10/1990-12-16 20 12 28 FR France \n", "1990-12-17/1990-12-23 34 25 43 FR France \n", "1990-12-24/1990-12-30 34 23 45 FR France \n", "1990-12-31/1991-01-06 27 18 36 FR France \n", "1991-01-07/1991-01-13 29 20 38 FR France \n", "1991-01-14/1991-01-20 27 18 36 FR France \n", "1991-01-21/1991-01-27 14 8 20 FR France \n", "1991-01-28/1991-02-03 18 11 25 FR France \n", "1991-02-04/1991-02-10 19 12 26 FR France \n", "1991-02-11/1991-02-17 22 15 29 FR France \n", "1991-02-18/1991-02-24 23 15 31 FR France \n", "1991-02-25/1991-03-03 24 15 33 FR France \n", "1991-03-04/1991-03-10 29 20 38 FR France \n", "1991-03-11/1991-03-17 27 19 35 FR France \n", "1991-03-18/1991-03-24 19 13 25 FR France \n", "1991-03-25/1991-03-31 17 11 23 FR France \n", "1991-04-01/1991-04-07 22 14 30 FR France \n", "1991-04-08/1991-04-14 25 18 32 FR France \n", "1991-04-15/1991-04-21 26 18 34 FR France \n", "1991-04-22/1991-04-28 24 16 32 FR France \n", "1991-04-29/1991-05-05 38 25 51 FR France \n", "1991-05-06/1991-05-12 29 19 39 FR France \n", "1991-05-13/1991-05-19 34 23 45 FR France \n", "1991-05-20/1991-05-26 26 16 36 FR France \n", "1991-05-27/1991-06-02 27 17 37 FR France \n", "1991-06-03/1991-06-09 21 13 29 FR France \n", "1991-06-10/1991-06-16 28 17 39 FR France \n", "1991-06-17/1991-06-23 28 18 38 FR France \n", "1991-06-24/1991-06-30 31 20 42 FR France \n", "... ... ... ... ... ... \n", "2023-03-13/2023-03-19 7 4 10 FR France \n", "2023-03-20/2023-03-26 16 11 21 FR France \n", "2023-03-27/2023-04-02 20 15 25 FR France \n", "2023-04-03/2023-04-09 23 17 29 FR France \n", "2023-04-10/2023-04-16 21 11 31 FR France \n", "2023-04-17/2023-04-23 17 12 22 FR France \n", "2023-04-24/2023-04-30 14 9 19 FR France \n", "2023-05-01/2023-05-07 16 11 21 FR France \n", "2023-05-08/2023-05-14 14 9 19 FR France \n", "2023-05-15/2023-05-21 14 7 21 FR France \n", "2023-05-22/2023-05-28 17 11 23 FR France \n", "2023-05-29/2023-06-04 18 12 24 FR France \n", "2023-06-05/2023-06-11 19 9 29 FR France \n", "2023-06-12/2023-06-18 17 12 22 FR France \n", "2023-06-19/2023-06-25 17 12 22 FR France \n", "2023-06-26/2023-07-02 14 10 18 FR France \n", "2023-07-03/2023-07-09 11 7 15 FR France \n", "2023-07-10/2023-07-16 10 6 14 FR France \n", "2023-07-17/2023-07-23 20 12 28 FR France \n", "2023-07-24/2023-07-30 9 5 13 FR France \n", "2023-07-31/2023-08-06 5 2 8 FR France \n", "2023-08-07/2023-08-13 12 2 22 FR France \n", "2023-08-14/2023-08-20 5 2 8 FR France \n", "2023-08-21/2023-08-27 2 0 4 FR France \n", "2023-08-28/2023-09-03 1 0 2 FR France \n", "2023-09-04/2023-09-10 1 0 2 FR France \n", "2023-09-11/2023-09-17 2 1 3 FR France \n", "2023-09-18/2023-09-24 3 1 5 FR France \n", "2023-09-25/2023-10-01 3 1 5 FR France \n", "2023-10-02/2023-10-08 5 2 8 FR France \n", "\n", "[1714 rows x 11 columns]" ] }, "execution_count": 7, "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." ] }, { "cell_type": "code", "execution_count": 8, "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": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "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 au printemps. Le creux des incidences se trouve en automne." ] }, { "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'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle\n", "Etant donné que le pic de l'épidémie se situe au printemps, nous définissons la période de référence\n", "entre deux minima de l'incidence, du 1er septembre de l'année $N$ au\n", "1er septembre 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 septembre de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Comme l'incidence de la varicelle est très faible fin été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent en 1990 par une année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 11, "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": 12, "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": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8XZD3aEqbk46Hpg/KExH9wHvAxUXKGkTSekkdkjp6enoyNsnMzEqVdcnt5yPimKSZQLuknxY5V8OkRZH0seY5mxCxBdgC+eGpInUzM7NzkKmnERHH0nM38Bz5+Yp30pAT6bk7nX4UmFeQfS5wLKXPHSZ9UB5JDcBFQG+RsszMrAJGDRqSLpQ0beAYWAm8DuwABlYztQHPp+MdQGtaEbUAWAjsS0NYJyUtT/MVNw/JM1DWDcDLad7jRWClpOa0OmtlSjMzswrIMjx1KfBcWh3bADwVEf9T0n5gm6R1wBHgRoCIOChpG/AG0A/cHhGnU1m3Ao8B5wM70wPgEeAJSV3kexitqaxeSfcD+9N590VE7zm018zMzoG3RjczM2+NbmZm489Bw8zMMnPQMDOzzBw0zMwsMwcNMzPLzEHDzMwyc9AwM7PMHDTMzCwzBw0zM8vMQcPMzDJz0DAzs8wcNMzMLDMHDTMzy8xBw8zMMnPQMDOzzBw0zMwsMwcNMzPLzEHDzMwyc9AwM7PMHDTMzCwzBw0zM8vMQcPMzDLLHDQknSfpFUl/l17PkNQuqTM9Nxecu1FSl6RDklYVpC+R9Fp6b7MkpfRGSc+m9L2S5hfkaUs/o1NS23g02szMxqaUnsYdwJsFrzcAuyJiIbArvUbSIqAVuBJYDXxX0nkpz0PAemBheqxO6euAExHxSeDbwIOprBnAvcAyYClwb2FwMjOz8soUNCTNBb4EfK8geQ2wNR1vBdYWpD8TEX0R8RbQBSyVNAuYHhF7IiKAx4fkGShrO7Ai9UJWAe0R0RsRJ4B2zgYaMzMrs6w9jb8C7gTOFKRdGhHHAdLzzJQ+B3i74LyjKW1OOh6aPihPRPQD7wEXFynLzMwqYNSgIemPgO6IOJCxTA2TFkXSx5qnsI7rJXVI6ujp6clYTTMzK1WWnsbngeskHQaeAb4o6UngnTTkRHruTucfBeYV5J8LHEvpc4dJH5RHUgNwEdBbpKxBImJLROQiItfS0pKhSWZmNhajBo2I2BgRcyNiPvkJ7pcj4k+AHcDAaqY24Pl0vANoTSuiFpCf8N6XhrBOSlqe5ituHpJnoKwb0s8I4EVgpaTmNAG+MqWZmVkFNJxD3geAbZLWAUeAGwEi4qCkbcAbQD9we0ScTnluBR4Dzgd2pgfAI8ATkrrI9zBaU1m9ku4H9qfz7ouI3nOos5mZnQPlP9DXj1wuFx0dHZWuhplZTZF0ICJyo53nO8LNzKpY9/un+PLDe+g+earSVQEcNMzMqtrmXZ3sP9zL5pc6K10V4NzmNMzMbIJccc9O+vrP3hr35N4jPLn3CI0NUzi06dqK1cs9DTOzKrT7zmu4bvFsmqbm/5tumjqFNYtns/uuaypaLwcNM7MqNHN6E9MaG+jrP0NjwxT6+s8wrbGBmdOaKlovD0+ZmVWpdz/o46Zll/PVpZfx1L4j9FTBZLiX3JqZmZfcmpnZ+HPQMDOzzBw0zMwsMwcNMzPLzEHDzMwyc9AwM7PMHDTMzCwzBw0zM8vMQcPMzDJz0DAzs8wcNMzMLDMHDTMzy8xBw8zMMnPQMDOzzBw0zMwsMwcNMzPLbNSgIalJ0j5JP5F0UNJ/TOkzJLVL6kzPzQV5NkrqknRI0qqC9CWSXkvvbZaklN4o6dmUvlfS/II8belndEpqG8/Gm5lZabL0NPqAL0bE7wCLgdWSlgMbgF0RsRDYlV4jaRHQClwJrAa+K+m8VNZDwHpgYXqsTunrgBMR8Ung28CDqawZwL3AMmApcG9hcDIzs/IaNWhE3gfp5dT0CGANsDWlbwXWpuM1wDMR0RcRbwFdwFJJs4DpEbEn8t8x+/iQPANlbQdWpF7IKqA9Inoj4gTQztlAY2ZmZZZpTkPSeZJeBbrJ/ye+F7g0Io4DpOeZ6fQ5wNsF2Y+mtDnpeGj6oDwR0Q+8B1xcpCwzM6uATEEjIk5HxGJgLvlew1VFTtdwRRRJH2uesz9QWi+pQ1JHT09PkaqZmdm5KGn1VET8Gvgh+SGid9KQE+m5O512FJhXkG0ucCylzx0mfVAeSQ3ARUBvkbKG1mtLROQiItfS0lJKk8zMrARZVk+1SPp4Oj4f+APgp8AOYGA1UxvwfDreAbSmFVELyE9470tDWCclLU/zFTcPyTNQ1g3Ay2ne40VgpaTmNAG+MqWZmVkFNGQ4ZxawNa2AmgJsi4i/k7QH2CZpHXAEuBEgIg5K2ga8AfQDt0fE6VTWrcBjwPnAzvQAeAR4QlIX+R5GayqrV9L9wP503n0R0XsuDTYzs7FT/gN9/cjlctHR0VHpapiZ1RRJByIiN9p5viPczMwyc9AwM7PMHDTMzCwzBw0zM8vMQcPMzDJz0DAzs8wcNMzMLDMHDTMzy8xBw8zMMnPQMDOzzBw0zMwsMwcNMzPLzEHDzMwyc9AwM7PMHDTMzCwzBw0zM8vMQcPMzDJz0DAzq4Du90/x5Yf30H3yVKWrUhIHDTOzCti8q5P9h3vZ/FJnpatSkoZKV8DMbDK54p6d9PWf+efXT+49wpN7j9DYMIVDm66tYM2ycU/DzKyMdt95Ddctnk3T1Px/v01Tp7Bm8Wx233VNhWuWjYOGmVkZzZzexLTGBvr6z9DYMIW+/jNMa2xg5rSmSlctEw9PmZmV2bsf9HHTssv56tLLeGrfEXpqaDJcEVH8BGke8DjwCeAMsCUi/lrSDOBZYD5wGPhyRJxIeTYC64DTwJ9FxIspfQnwGHA+8AJwR0SEpMb0M5YA/w/4SkQcTnnagHtSdTZFxNZi9c3lctHR0ZH9X8DMzJB0ICJyo52XZXiqH/j3EfFpYDlwu6RFwAZgV0QsBHal16T3WoErgdXAdyWdl8p6CFgPLEyP1Sl9HXAiIj4JfBt4MJU1A7gXWAYsBe6V1JyhzmZmNgFGDRoRcTwifpyOTwJvAnOANcDAp/6twNp0vAZ4JiL6IuItoAtYKmkWMD0i9kS+e/P4kDwDZW0HVkgSsApoj4je1Itp52ygMTOzMitpIlzSfOBqYC9waUQch3xgAWam0+YAbxdkO5rS5qTjoemD8kREP/AecHGRsszMrAIyBw1JvwX8N+CbEfF+sVOHSYsi6WPNU1i39ZI6JHX09PQUqZqZmZ2LTEFD0lTyAeP7EfG3KfmdNOREeu5O6UeBeQXZ5wLHUvrcYdIH5ZHUAFwE9BYpa5CI2BIRuYjItbS0ZGmSmZmNwahBI80tPAK8GRH/ueCtHUBbOm4Dni9Ib5XUKGkB+QnvfWkI66Sk5anMm4fkGSjrBuDlNO/xIrBSUnOaAF+Z0szMrAKyLLn9ArAbeI38kluAb5Gf19gGXAYcAW6MiN6U527gFvIrr74ZETtTeo6zS253At9IS26bgCfIz5f0Aq0R8fOU55b08wD+IiIeHaW+PcAvMra/mlwCvFvpSowTt6X61Es7wG2ZKJdHxKhDNaMGDSsPSR1Z1kjXArel+tRLO8BtqTRvI2JmZpk5aJiZWWYOGtVjS6UrMI7clupTL+0At6WiPKdhZmaZuadhZmaZOWhMEEl/I6lb0usFab8jaY+k1yT9d0nTU/rHJD2a0n8i6fcL8vxQ0iFJr6bHzGF+3ES3ZZ6k/yXpTUkHJd2R0mdIapfUmZ6bC/JslNSV6r6qIH1JameXpM3pnp1abUvFrk2p7ZB0cTr/A0nfGVJWTV2TUdpS0b+XMbTlDyUdSP/+ByR9saCsil6XEUWEHxPwAH4P+BzwekHafuBfpeNbgPvT8e3Ao+l4JnAAmJJe/xDIVbgts4DPpeNpwD8Ci4C/BDak9A3Ag+l4EfAToBFYAPwMOC+9tw/4XfJbxOwErq3htlTs2oyhHRcCXwC+DnxnSFm1dk2KtaWify9jaMvVwOx0fBXwy2q5LiM93NOYIBHx9+RvVCx0BfD36bgd+ON0vIj89vJERDfwa6Bq1m5HeXY6Lovxaks56zycUtsREb+JiP8NDPq2n1q8JiO1pRqMoS2vRMTA1kgHgSbld9Oo+HUZiYNGeb0OXJeOb+Tsvlo/AdZIalB+65UlDN5z69HU1f4Ple6iauJ2Oi67c2zLgIpfm4ztGEktXpPRVPyawJja8sfAKxHRR5Vdl0IOGuV1C/kvsTpAvuv6Tyn9b8j/UnQAfwX8X/JbsADcFBGfAf5lenytrDUuoInd6bisxqEtUAXXpoR2jFjEMGnVfk2Kqfg1gdLbIulK8l8+96cDScOcVhVLXR00yigifhoRKyNiCfA0+fFxIqI/Iv48IhZHxBrg40Bneu+X6fkk8BQVGhrRxO90XDbj1JaKX5sS2zGSWrwmI6r0NYHS2yJpLvAccHNE/CwlV8V1GY6DRhkNrOSQNIX8957/l/T6AkkXpuM/BPoj4o00XHVJSp8K/BH5Ia5y17scOx2XxXi1pdLXZgztGFaNXpORyqn430upbZH0ceB/ABsj4v8MnFwN12VElZ6Jr9cH+Z7EceBD8p8a1gF3kF9N8Y/AA5y9uXI+cIj8pNlL5HebhPwqkQPAP5CfJPtr0sqdMrflC+S7xv8AvJoe/5r8tyvuIt8r2gXMKMhzN/me1CEKVn2Qn+B/Pb33nYF/g1prS6WvzRjbcZj84owP0u/kohq+Jh9pS6WvyVjaQv7D428Kzn0VmFkN12Wkh+8INzOzzDw8ZWZmmTlomJlZZg4aZmaWmYOGmZll5qBhZmaZOWiYmVlmDhpmZpaZg4aZmWX2/wFIULHVnmtEQwAAAABJRU5ErkJggg==\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": 14, "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": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre bien que les épidémies sont assez constantes en amplitudes, entre 500000 et 800000 cas par ans. Les années sortant de ces valeurs ne sont qu'au nombre de 2 depuis 1991." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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