{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la varicelle" ] }, { "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\n", "import urllib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Données" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true }, "source": [ "J'étudie les données sur l'incdence de la varicelle fournies par le [Réseau Seninelles](http://www.sentiweb.fr/). Les données sont récupérées dans un fichier CSV. Le fichier est téléchargé en local (téléchargement le 02/04/2021) et ne sera téléchargé à nouveau que si le fichier local n'est pas trouvé. Le fichier comprend toutes les données existantes c'est à dire depuis décembre 1990 et jusqu'à la dernière semaine renseignée.\n", "\n", "Chaque colonne du fichier correspond à une semaine. \n", "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", "Le chemin d'accès du fichier en local est :" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hideCode": true, "hideOutput": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_file = \"S:\\Formations\\Recherche reproductible\\incidence-PAY-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'adresse URL pour téléchager les données sur le site du Réseau Sentinelles est :" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si le fichier n'existe pas, on le télécharge à l'URL précédent. Sinon on utilise le fichier déjà téléchargé en local. \n", "La première ligne du fichier est un commentaire donc on ne l'inclut pas aux données (avec `skiprows = 1` )" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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220211079056645211660141018FRFrance
3202109710988793814038171222FRFrance
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52021077135611031516807211626FRFrance
6202106713401981016992201525FRFrance
7202105712210898815432181323FRFrance
8202104712026882615226181323FRFrance
92021037891363751145113917FRFrance
102021027779554301016012816FRFrance
11202101710525775013300161220FRFrance
12202053711978840615550181323FRFrance
13202052712012828515739181224FRFrance
14202051710564757413554161121FRFrance
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1820204774999296370358511FRFrance
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202020457369620165376639FRFrance
2120204474391237564077410FRFrance
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25202040720786753481315FRFrance
26202039710492371861213FRFrance
27202038722517813721315FRFrance
28202037715844052763204FRFrance
2920203679191001738102FRFrance
.................................
15521991267176081130423912312042FRFrance
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15601991187213851388228888382551FRFrance
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\n", "

1582 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202112 7 14023 9841 18205 21 15 \n", "1 202111 7 9501 6752 12250 14 10 \n", "2 202110 7 9056 6452 11660 14 10 \n", "3 202109 7 10988 7938 14038 17 12 \n", "4 202108 7 11281 8361 14201 17 13 \n", "5 202107 7 13561 10315 16807 21 16 \n", "6 202106 7 13401 9810 16992 20 15 \n", "7 202105 7 12210 8988 15432 18 13 \n", "8 202104 7 12026 8826 15226 18 13 \n", "9 202103 7 8913 6375 11451 13 9 \n", "10 202102 7 7795 5430 10160 12 8 \n", "11 202101 7 10525 7750 13300 16 12 \n", "12 202053 7 11978 8406 15550 18 13 \n", "13 202052 7 12012 8285 15739 18 12 \n", "14 202051 7 10564 7574 13554 16 11 \n", "15 202050 7 7063 4744 9382 11 7 \n", "16 202049 7 5026 3145 6907 8 5 \n", "17 202048 7 6683 4312 9054 10 6 \n", "18 202047 7 4999 2963 7035 8 5 \n", "19 202046 7 3752 1963 5541 6 3 \n", "20 202045 7 3696 2016 5376 6 3 \n", "21 202044 7 4391 2375 6407 7 4 \n", "22 202043 7 4376 2505 6247 7 4 \n", "23 202042 7 4000 1979 6021 6 3 \n", "24 202041 7 3961 2099 5823 6 3 \n", "25 202040 7 2078 675 3481 3 1 \n", "26 202039 7 1049 237 1861 2 1 \n", "27 202038 7 2251 781 3721 3 1 \n", "28 202037 7 1584 405 2763 2 0 \n", "29 202036 7 919 100 1738 1 0 \n", "... ... ... ... ... ... ... ... \n", "1552 199126 7 17608 11304 23912 31 20 \n", "1553 199125 7 16169 10700 21638 28 18 \n", "1554 199124 7 16171 10071 22271 28 17 \n", "1555 199123 7 11947 7671 16223 21 13 \n", "1556 199122 7 15452 9953 20951 27 17 \n", "1557 199121 7 14903 8975 20831 26 16 \n", "1558 199120 7 19053 12742 25364 34 23 \n", "1559 199119 7 16739 11246 22232 29 19 \n", "1560 199118 7 21385 13882 28888 38 25 \n", "1561 199117 7 13462 8877 18047 24 16 \n", "1562 199116 7 14857 10068 19646 26 18 \n", "1563 199115 7 13975 9781 18169 25 18 \n", "1564 199114 7 12265 7684 16846 22 14 \n", "1565 199113 7 9567 6041 13093 17 11 \n", "1566 199112 7 10864 7331 14397 19 13 \n", "1567 199111 7 15574 11184 19964 27 19 \n", "1568 199110 7 16643 11372 21914 29 20 \n", "1569 199109 7 13741 8780 18702 24 15 \n", "1570 199108 7 13289 8813 17765 23 15 \n", "1571 199107 7 12337 8077 16597 22 15 \n", "1572 199106 7 10877 7013 14741 19 12 \n", "1573 199105 7 10442 6544 14340 18 11 \n", "1574 199104 7 7913 4563 11263 14 8 \n", "1575 199103 7 15387 10484 20290 27 18 \n", "1576 199102 7 16277 11046 21508 29 20 \n", "1577 199101 7 15565 10271 20859 27 18 \n", "1578 199052 7 19375 13295 25455 34 23 \n", "1579 199051 7 19080 13807 24353 34 25 \n", "1580 199050 7 11079 6660 15498 20 12 \n", "1581 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 27 FR France \n", "1 18 FR France \n", "2 18 FR France \n", "3 22 FR France \n", "4 21 FR France \n", "5 26 FR France \n", "6 25 FR France \n", "7 23 FR France \n", "8 23 FR France \n", "9 17 FR France \n", "10 16 FR France \n", "11 20 FR France \n", "12 23 FR France \n", "13 24 FR France \n", "14 21 FR France \n", "15 15 FR France \n", "16 11 FR France \n", "17 14 FR France \n", "18 11 FR France \n", "19 9 FR France \n", "20 9 FR France \n", "21 10 FR France \n", "22 10 FR France \n", "23 9 FR France \n", "24 9 FR France \n", "25 5 FR France \n", "26 3 FR France \n", "27 5 FR France \n", "28 4 FR France \n", "29 2 FR France \n", "... ... ... ... \n", "1552 42 FR France \n", "1553 38 FR France \n", "1554 39 FR France \n", "1555 29 FR France \n", "1556 37 FR France \n", "1557 36 FR France \n", "1558 45 FR France \n", "1559 39 FR France \n", "1560 51 FR France \n", "1561 32 FR France \n", "1562 34 FR France \n", "1563 32 FR France \n", "1564 30 FR France \n", "1565 23 FR France \n", "1566 25 FR France \n", "1567 35 FR France \n", "1568 38 FR France \n", "1569 33 FR France \n", "1570 31 FR France \n", "1571 29 FR France \n", "1572 26 FR France \n", "1573 25 FR France \n", "1574 20 FR France \n", "1575 36 FR France \n", "1576 38 FR France \n", "1577 36 FR France \n", "1578 45 FR France \n", "1579 43 FR France \n", "1580 28 FR France \n", "1581 5 FR France \n", "\n", "[1582 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)\n", "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tri des données et vérifications" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Après inspection visuelle des données, je n'ai pas repéré d'anomalie comme des lignes non renseignées.\n", "Je fais tout de même une vérification avec le code :" ] }, { "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
\n", "
" ], "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": [ "Il n'y a pas de ligne vide à retirer du jeu de données\n", "\n", "Il faut à présent modifier la façon d'écrire la date pour qu'elle soit lisible par pandas. On le fait dans une nouvelle colonne 'period' qui comprend la date de début et la date de fin de la semaine concernée, sous la forme AAAA-MM-JJ/AAAA-MM-JJ" ] }, { "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", "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Je définis la période d'observation comme nouvel index et je trie les données chronologiquement" ] }, { "cell_type": "code", "execution_count": 7, "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
.................................
2020-08-31/2020-09-0620203679191001738102FRFrance
2020-09-07/2020-09-13202037715844052763204FRFrance
2020-09-14/2020-09-20202038722517813721315FRFrance
2020-09-21/2020-09-27202039710492371861213FRFrance
2020-09-28/2020-10-04202040720786753481315FRFrance
2020-10-05/2020-10-112020417396120995823639FRFrance
2020-10-12/2020-10-182020427400019796021639FRFrance
2020-10-19/2020-10-2520204374376250562477410FRFrance
2020-10-26/2020-11-0120204474391237564077410FRFrance
2020-11-02/2020-11-082020457369620165376639FRFrance
2020-11-09/2020-11-152020467375219635541639FRFrance
2020-11-16/2020-11-2220204774999296370358511FRFrance
2020-11-23/2020-11-29202048766834312905410614FRFrance
2020-11-30/2020-12-0620204975026314569078511FRFrance
2020-12-07/2020-12-13202050770634744938211715FRFrance
2020-12-14/2020-12-20202051710564757413554161121FRFrance
2020-12-21/2020-12-27202052712012828515739181224FRFrance
2020-12-28/2021-01-03202053711978840615550181323FRFrance
2021-01-04/2021-01-10202101710525775013300161220FRFrance
2021-01-11/2021-01-172021027779554301016012816FRFrance
2021-01-18/2021-01-242021037891363751145113917FRFrance
2021-01-25/2021-01-31202104712026882615226181323FRFrance
2021-02-01/2021-02-07202105712210898815432181323FRFrance
2021-02-08/2021-02-14202106713401981016992201525FRFrance
2021-02-15/2021-02-212021077135611031516807211626FRFrance
2021-02-22/2021-02-28202108711281836114201171321FRFrance
2021-03-01/2021-03-07202109710988793814038171222FRFrance
2021-03-08/2021-03-1420211079056645211660141018FRFrance
2021-03-15/2021-03-2120211179501675212250141018FRFrance
2021-03-22/2021-03-28202112714023984118205211527FRFrance
\n", "

1582 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", "2020-08-31/2020-09-06 202036 7 919 100 1738 1 \n", "2020-09-07/2020-09-13 202037 7 1584 405 2763 2 \n", "2020-09-14/2020-09-20 202038 7 2251 781 3721 3 \n", "2020-09-21/2020-09-27 202039 7 1049 237 1861 2 \n", "2020-09-28/2020-10-04 202040 7 2078 675 3481 3 \n", "2020-10-05/2020-10-11 202041 7 3961 2099 5823 6 \n", "2020-10-12/2020-10-18 202042 7 4000 1979 6021 6 \n", "2020-10-19/2020-10-25 202043 7 4376 2505 6247 7 \n", "2020-10-26/2020-11-01 202044 7 4391 2375 6407 7 \n", "2020-11-02/2020-11-08 202045 7 3696 2016 5376 6 \n", "2020-11-09/2020-11-15 202046 7 3752 1963 5541 6 \n", "2020-11-16/2020-11-22 202047 7 4999 2963 7035 8 \n", "2020-11-23/2020-11-29 202048 7 6683 4312 9054 10 \n", "2020-11-30/2020-12-06 202049 7 5026 3145 6907 8 \n", "2020-12-07/2020-12-13 202050 7 7063 4744 9382 11 \n", "2020-12-14/2020-12-20 202051 7 10564 7574 13554 16 \n", "2020-12-21/2020-12-27 202052 7 12012 8285 15739 18 \n", "2020-12-28/2021-01-03 202053 7 11978 8406 15550 18 \n", "2021-01-04/2021-01-10 202101 7 10525 7750 13300 16 \n", "2021-01-11/2021-01-17 202102 7 7795 5430 10160 12 \n", "2021-01-18/2021-01-24 202103 7 8913 6375 11451 13 \n", "2021-01-25/2021-01-31 202104 7 12026 8826 15226 18 \n", "2021-02-01/2021-02-07 202105 7 12210 8988 15432 18 \n", "2021-02-08/2021-02-14 202106 7 13401 9810 16992 20 \n", "2021-02-15/2021-02-21 202107 7 13561 10315 16807 21 \n", "2021-02-22/2021-02-28 202108 7 11281 8361 14201 17 \n", "2021-03-01/2021-03-07 202109 7 10988 7938 14038 17 \n", "2021-03-08/2021-03-14 202110 7 9056 6452 11660 14 \n", "2021-03-15/2021-03-21 202111 7 9501 6752 12250 14 \n", "2021-03-22/2021-03-28 202112 7 14023 9841 18205 21 \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", "2020-08-31/2020-09-06 0 2 FR France \n", "2020-09-07/2020-09-13 0 4 FR France \n", "2020-09-14/2020-09-20 1 5 FR France \n", "2020-09-21/2020-09-27 1 3 FR France \n", "2020-09-28/2020-10-04 1 5 FR France \n", "2020-10-05/2020-10-11 3 9 FR France \n", "2020-10-12/2020-10-18 3 9 FR France \n", "2020-10-19/2020-10-25 4 10 FR France \n", "2020-10-26/2020-11-01 4 10 FR France \n", "2020-11-02/2020-11-08 3 9 FR France \n", "2020-11-09/2020-11-15 3 9 FR France \n", "2020-11-16/2020-11-22 5 11 FR France \n", "2020-11-23/2020-11-29 6 14 FR France \n", "2020-11-30/2020-12-06 5 11 FR France \n", "2020-12-07/2020-12-13 7 15 FR France \n", "2020-12-14/2020-12-20 11 21 FR France \n", "2020-12-21/2020-12-27 12 24 FR France \n", "2020-12-28/2021-01-03 13 23 FR France \n", "2021-01-04/2021-01-10 12 20 FR France \n", "2021-01-11/2021-01-17 8 16 FR France \n", "2021-01-18/2021-01-24 9 17 FR France \n", "2021-01-25/2021-01-31 13 23 FR France \n", "2021-02-01/2021-02-07 13 23 FR France \n", "2021-02-08/2021-02-14 15 25 FR France \n", "2021-02-15/2021-02-21 16 26 FR France \n", "2021-02-22/2021-02-28 13 21 FR France \n", "2021-03-01/2021-03-07 12 22 FR France \n", "2021-03-08/2021-03-14 10 18 FR France \n", "2021-03-15/2021-03-21 10 18 FR France \n", "2021-03-22/2021-03-28 15 27 FR France \n", "\n", "[1582 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = raw_data.set_index('period').sort_index()\n", "sorted_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une dernière vérification : est-ce que les données sont complètes, c'est à dire est-ce que toutes les semaines sont présentes ? Pour cela on regarde le dernier jour d'une période et le premier de la période suivante qui doivent se suivre et donc avoir un écart très faible. On teste avec un écart de 1 seconde." ] }, { "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": [ "Aucune date ne s'affiche à l'exécution du code précédent donc toutes les périodes de données sont cohérentes.\n", "\n", "On peut donc tester d'afficher les données en graphique" ] }, { "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": [ "Je remarque que les pics d'incidence sont moins fins que pour le cas des syndromes grippaux.\n", "JE zoome sur les dernières années" ] }, { "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-250:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Je note qu'il y a tout de même un creux chaque année autour de la semaine 36, ce qui correspond au début du mois de septembre. \n", "\n", "__Remarque 1 :__ je suis tentée de relier cette dynamique aux vacances estivales pendant lesquelles les enfants ont moins de contacts entre eux et se contaminent donc moins, l'épidémie reprenant au mois de septembre avec la rentrée scolaire. \n", "\n", "__Remarque 2 :__ l'année 2020 a un pic épidémique plus faible que d'habitude, je relie cette observation à la fermeture des écoles qui a eu lieu de mars à juin 2020 due à l'épidémie de Covid-19, ainsi qu'aux mesures d'hygiène et de distanciation renforcées dans les écoles, ce qui a fait baisser les contaminations à la varicelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle de la varicelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On a pu noter précédemment que le creux de l'épidémie de varicelle se situe à la fin de l'été, je veux donc prendre une période de référence allant du __1er septembre__ de l'année $N$ au 1er septembre de l'année $N+1$.\n", "Pour simplifier et s'adapter à nos données (fournies pour 1 semaine) on prendra comme premier jour de chaque période __le premier jour de la semaine qui contient le 1er septembre__.\n", "Je suis consciente qu'en procédent comme cela les périodes de temps étudiées ne seront pas tout à fait égales et pourront comporter 51 ou 52 semaines. Cependant comme l'incidence est faible début septembre il y aura peu de consequences sur nos résultats.\n", "\n", "Les données commencent en décembre 1990 donc notre première période de référence n'est pas complète. On ne prendra en compte que les données à partir du 1er septembre 1991.\n", "\n", "On crée une liste des semaines qui contiennet un 1er septembre dans nos données :" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Period('1991-08-26/1991-09-01', 'W-SUN'),\n", " Period('1992-08-31/1992-09-06', 'W-SUN'),\n", " Period('1993-08-30/1993-09-05', 'W-SUN'),\n", " Period('1994-08-29/1994-09-04', 'W-SUN'),\n", " Period('1995-08-28/1995-09-03', 'W-SUN'),\n", " Period('1996-08-26/1996-09-01', 'W-SUN'),\n", " Period('1997-09-01/1997-09-07', 'W-SUN'),\n", " Period('1998-08-31/1998-09-06', 'W-SUN'),\n", " Period('1999-08-30/1999-09-05', 'W-SUN'),\n", " Period('2000-08-28/2000-09-03', 'W-SUN'),\n", " Period('2001-08-27/2001-09-02', 'W-SUN'),\n", " Period('2002-08-26/2002-09-01', 'W-SUN'),\n", " Period('2003-09-01/2003-09-07', 'W-SUN'),\n", " Period('2004-08-30/2004-09-05', 'W-SUN'),\n", " Period('2005-08-29/2005-09-04', 'W-SUN'),\n", " Period('2006-08-28/2006-09-03', 'W-SUN'),\n", " Period('2007-08-27/2007-09-02', 'W-SUN'),\n", " Period('2008-09-01/2008-09-07', 'W-SUN'),\n", " Period('2009-08-31/2009-09-06', 'W-SUN'),\n", " Period('2010-08-30/2010-09-05', 'W-SUN'),\n", " Period('2011-08-29/2011-09-04', 'W-SUN'),\n", " Period('2012-08-27/2012-09-02', 'W-SUN'),\n", " Period('2013-08-26/2013-09-01', 'W-SUN'),\n", " Period('2014-09-01/2014-09-07', 'W-SUN'),\n", " Period('2015-08-31/2015-09-06', 'W-SUN'),\n", " Period('2016-08-29/2016-09-04', 'W-SUN'),\n", " Period('2017-08-28/2017-09-03', 'W-SUN'),\n", " Period('2018-08-27/2018-09-02', 'W-SUN'),\n", " Period('2019-08-26/2019-09-01', 'W-SUN'),\n", " Period('2020-08-31/2020-09-06', 'W-SUN')]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]\n", "first_september_week" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Je vérifie que les périodes de référence font bien 51 ou 52 semaines. Je somme les incidences hebdomadaires sur chaque période de référence." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1992 832939\n", "1993 643387\n", "1994 661409\n", "1995 652478\n", "1996 564901\n", "1997 683434\n", "1998 677775\n", "1999 756456\n", "2000 617597\n", "2001 619041\n", "2002 516689\n", "2003 758363\n", "2004 777388\n", "2005 628464\n", "2006 632833\n", "2007 717352\n", "2008 749478\n", "2009 842373\n", "2010 829911\n", "2011 642368\n", "2012 624573\n", "2013 698332\n", "2014 685769\n", "2015 604382\n", "2016 782114\n", "2017 551041\n", "2018 542312\n", "2019 584066\n", "2020 221186\n", "dtype: int64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_september_week[:-1], first_september_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)\n", "yearly_incidence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On trace les incidences annuelles :" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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eG8d5WwPw8vBmtZHb0gjgLyUdlNSRYtdHxGmA9L4wxRcD75aVPZlii9P28PiQMhFxCTgHLKhwrCEkdUjqldTb39+fWSUzM6tWbkvjlyLilKSFQLek71bYVyPEokJ8rGUuByI6gU4o3RFe4dzMzGwcsloaEXEqvZ8BvklpfOE9SYsA0vuZtPtJ4Iay4kuAUym+ZIT4kDKSZgFNwECFY5mZWQ2MmjQkXSvpo4PbwFrgTWAPMDibqR14MW3vAdrSjKgbKQ14H0hdWOclrU7jFXcNKzN4rDuAV6K0KNbLwFpJ89IA+NoUMzOzGsjpnroe+GaaHTsL+EZE/E9JrwG7JG0CTgB3AkTEYUm7gLeAS8C9EfFBOtY9wNPAbEoD4HtT/Eng2TRoPkBp9hURMSDpQeC1tN8Dg4PiZmY29bzKrZmZeZVbMzObeE4aZmaWzUnDzMyyOWmYmVk2Jw0zM8vmpGFmZtmcNMzMLJuThpmZZXPSMDOzbE4aZmaWzUnDzMyyOWmYmVk2Jw0zM8vmpGFmZtmcNMzMLJuThpmZZXPSMDOzbE4aZmaWzUnDzMyyOWmYmVk2Jw0zM8vmpGFmZtmyk4akmZJel/Tn6ef5krolHU3v88r23SKpT9IRSevK4islHUq/2yFJKX61pBdSfL+klrIy7ekzjkpqn4hKm5nZ2FTT0vgy8HbZz5uBnohYDvSkn5G0AmgDbgbWA49LmpnKPAF0AMvTa32KbwLORsRNwCPAw+lY84FtwG3AKmBbeXIyM7OplZU0JC0BfgP4ell4A7Azbe8Ebi+LPx8RFyPiHaAPWCVpETA3IvZFRADPDCszeKzdwJrUClkHdEfEQEScBbq5nGjMzGyK5bY0HgX+HfBhWez6iDgNkN4Xpvhi4N2y/U6m2OK0PTw+pExEXALOAQsqHMvMzGpg1KQh6TeBMxFxMPOYGiEWFeJjLVN+jh2SeiX19vf3Z56mmZlVK6el8UvAFyQdA54HPifpT4D3UpcT6f1M2v8kcENZ+SXAqRRfMkJ8SBlJs4AmYKDCsYaIiM6IaI2I1ubm5owqmZnZWIyaNCJiS0QsiYgWSgPcr0TE7wJ7gMHZTO3Ai2l7D9CWZkTdSGnA+0DqwjovaXUar7hrWJnBY92RPiOAl4G1kualAfC1KWZmZjUwaxxlHwJ2SdoEnADuBIiIw5J2AW8Bl4B7I+KDVOYe4GlgNrA3vQCeBJ6V1EephdGWjjUg6UHgtbTfAxExMI5zNjOzcVDpC33jaG1tjd7e3lqfhplZoUg6GBGto+3nO8LNzCybk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZNicNMzPL5qRhZmbZnDTMzCybk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZNicNMzPL5qRhZmbZnDTMzCybk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZNicNMzPLNmrSkHSNpAOSviPpsKSvpfh8Sd2Sjqb3eWVltkjqk3RE0rqy+EpJh9LvdkhSil8t6YUU3y+ppaxMe/qMo5LaJ7LyZmZWnZyWxkXgcxHxC8CtwHpJq4HNQE9ELAd60s9IWgG0ATcD64HHJc1Mx3oC6ACWp9f6FN8EnI2Im4BHgIfTseYD24DbgFXAtvLkZGZmU2vUpBElP0w/XpVeAWwAdqb4TuD2tL0BeD4iLkbEO0AfsErSImBuROyLiACeGVZm8Fi7gTWpFbIO6I6IgYg4C3RzOdGYmdkUyxrTkDRT0hvAGUp/xPcD10fEaYD0vjDtvhh4t6z4yRRbnLaHx4eUiYhLwDlgQYVjmZlZDWQljYj4ICJuBZZQajV8ssLuGukQFeJjLXP5A6UOSb2Sevv7+yucmpmZjUdVs6ci4h+BVyl1Eb2XupxI72fSbieBG8qKLQFOpfiSEeJDykiaBTQBAxWONfy8OiOiNSJam5ubq6mSmZlVIWf2VLOkj6Xt2cCvAN8F9gCDs5nagRfT9h6gLc2IupHSgPeB1IV1XtLqNF5x17Ayg8e6A3gljXu8DKyVNC8NgK9NMTMzq4FZGfssAnamGVAzgF0R8eeS9gG7JG0CTgB3AkTEYUm7gLeAS8C9EfFBOtY9wNPAbGBvegE8CTwrqY9SC6MtHWtA0oPAa2m/ByJiYDwVNjOzsVPpC33jaG1tjd7e3lqfhplZoUg6GBGto+3nO8LNzCybk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZNicNMzPL5qRhZmbZnDTMzCybk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZNicNMzPL5qRhZmbZnDTMzCybk4aZWcF1Heqi5dEWZnxtBi2PttB1qGvSPivnGeFmZlanug510fFSBxfevwDA8XPH6XipA4CNt2yc8M9zS8PMrMC29mz9ScIYdOH9C2zt2Topn+ekYWZWYCfOnagqPl5OGmZmBba0aWlV8fFy0jAzK7Dta7Yz56o5Q2JzrprD9jXbJ+XzRk0akm6Q9L8kvS3psKQvp/h8Sd2Sjqb3eWVltkjqk3RE0rqy+EpJh9LvdkhSil8t6YUU3y+ppaxMe/qMo5LaJ7LyZmZFt/GWjXR+vpNlTcsQYlnTMjo/3zkpg+AAiojKO0iLgEUR8W1JHwUOArcDvw8MRMRDkjYD8yLifkkrgOeAVcDHgW8BPxcRH0g6AHwZ+FvgL4AdEbFX0h8APx8RX5TUBvxWRPyOpPlAL9AKRPrslRFx9krn29raGr29vWP/L2JmNg1JOhgRraPtN2pLIyJOR8S30/Z54G1gMbAB2Jl220kpkZDiz0fExYh4B+gDVqXkMzci9kUpUz0zrMzgsXYDa1IrZB3QHREDKVF0A+tHO2czM5scVY1ppG6jTwH7gesj4jSUEguwMO22GHi3rNjJFFuctofHh5SJiEvAOWBBhWOZmVkNZCcNST8D/Hfgvoj4p0q7jhCLCvGxlik/tw5JvZJ6+/v7K5yamZmNR1bSkHQVpYTRFRF/lsLvpS6nwXGPMyl+ErihrPgS4FSKLxkhPqSMpFlAEzBQ4VhDRERnRLRGRGtzc3NOlczMbAxyZk8JeBJ4OyL+qOxXe4DB2UztwItl8bY0I+pGYDlwIHVhnZe0Oh3zrmFlBo91B/BKGvd4GVgraV6anbU2xczMrAZyZk99Bvhr4BDwYQp/hdK4xi5gKXACuDMiBlKZrcDdwCVK3Vl7U7wVeBqYDewFvhQRIeka4FlK4yUDQFtEfD+VuTt9HsD2iHhqlPPtB45n1r8eXQf8Q61PYpI0at1cr+Jp1LqNp17LImLUrppRk4ZNLUm9OdPeiqhR6+Z6FU+j1m0q6uU7ws3MLJuThpmZZXPSqD+dtT6BSdSodXO9iqdR6zbp9fKYhpmZZXNLw8zMsjlpTAFJfyzpjKQ3y2K/IGlfWvX3JUlzU/wjkp5K8e9I+mxZmVfTysFvpNfCET5uykzFCsi1MMH1qptrVm29JC1I+/9Q0mPDjlU31yudz0TWrcjX7FclHUzX5qCkz5Uda2KuWUT4Nckv4JeBTwNvlsVeA/5F2r4beDBt3ws8lbYXUlrZd0b6+VWgtdb1KavDIuDTafujwN8DK4D/BGxO8c3Aw2l7BfAd4GrgRuB7wMz0uwPAL1JaOmYv8GsNUq+6uWZjqNe1wGeALwKPDTtW3VyvSahbka/Zp4CPp+1PAj+Y6GvmlsYUiIi/onTTYrlPAH+VtruB307bK4CeVO4M8I+UloavOzE1KyBPuYmq19Se9eiqrVdE/Cgi/jfw/8qPU2/XCyaubvVmDPV6PSIGl1o6DFyj0uocE3bNnDRq503gC2n7Ti6vsfUdYIOkWSotw7KSoetvPZWazP+h1l0C5TR5KyDX1DjrNajurllmva6kbq8XjLtugxrhmv028HpEXGQCr5mTRu3cDdwr6SClZuePU/yPKV3QXuBR4P9QWo4FYGNE3AL88/T6vSk94yvQ5K6AXDMTUC+ow2tWRb2ueIgRYjW/XjAhdYMGuGaSbgYeBv7NYGiE3cZ0zZw0aiQivhsRayNiJaUnHX4vxS9FxB9GxK0RsQH4GHA0/e4H6f088A3qoAtEk78Cck1MUL3q7ppVWa8rqbvrBRNWt8JfM0lLgG8Cd0XE91J4wq6Zk0aNDM7IkDQD+PfAf0s/z5F0bdr+VeBSRLyVuquuS/GrgN+k1MVVM6nZPtkrIE+5iapXvV2zMdRrRPV2vWDi6lb0aybpY8D/ALZExN8M7jyh12wqZgBM9xellsRp4H1KGX8TpWel/316PcTlGy1bgCOUBry+RWnlSSjN9jgI/B2lAa7/QpqhU8N6fYZSE/fvgDfS69cpPXWxh1ILqQeYX1ZmK6VW1RHKZm9QGux/M/3uscH/HkWuV71dszHW6xilSRw/TP/vrqi36zWRdSv6NaP0BfRHZfu+ASycyGvmO8LNzCybu6fMzCybk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZNicNMzPL5qRhZmbZ/j+lI7m0y1Wd4QAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='o' + 'g')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Je vois clairement que l'année 2020 a été une année de faible incidence de la varicelle, le point se détache clairement du reste des données.\n", "\n", "Je trie les données d'incidence annuelle selon leur valeur :" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\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", "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": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin on peut montrer les résultats dans un histogramme :" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 51, "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.hist(xrot=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dans le cas de la varicelle, l'année 2020 est inédite. Les fortes épidémies sont courantes. " ] } ], "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": 2 }