{ "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.path\n", "import numpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n", "local_file = \"data.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "if not os.path.isfile(local_file):\n", " print(\"Téléchargement du fichier de données...\")\n", " pd.read_csv(data_url, skiprows=1).to_csv('data.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", "| Name | Type | Description |\n", "|:-------------|:--------|:--------------------------------------------------------------------------------------------------------------------------|\n", "| week PK | integer | ISO8601 Yearweek number as numeric (year*100 + week nubmer) |\n", "| geo_insee PK | string | Identifier of the geographic area, from INSEE https://www.insee.fr |\n", "| geo_name | string | Geographic label of the area, corresponding to INSEE code. This label is not an id and is only provided for human reading |\n", "| indicator PK | integer | Unique identifier of the indicator, see metadata document https://www.sentiweb.fr/meta.json |\n", "| inc | integer | Estimated incidence value for the time step, in the geographic level |\n", "| inc_low | integer | Lower bound of the estimated incidence 95% Confidence Interval |\n", "| inc_up | integer | Upper bound of the estimated incidence 95% Confidence Interval |\n", "| inc100 | integer | Estimated rate incidence per 100,000 inhabitants |\n", "| inc100_low | integer | Lower bound of the estimated incidence 95% Confidence Interval |\n", "| inc100_up | integer | Upper bound of the estimated rate incidence 95% Confidence Interval |" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1530 rows × 11 columns

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" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 \\\n", "0 0 202013 7 7371 5268 9474 11 \n", "1 1 202012 7 8123 5790 10456 12 \n", "2 2 202011 7 10198 7568 12828 15 \n", "3 3 202010 7 9011 6691 11331 14 \n", "4 4 202009 7 13631 10544 16718 21 \n", "5 5 202008 7 10424 7708 13140 16 \n", "6 6 202007 7 8959 6574 11344 14 \n", "7 7 202006 7 9264 6925 11603 14 \n", "8 8 202005 7 8505 6314 10696 13 \n", "9 9 202004 7 7991 5831 10151 12 \n", "10 10 202003 7 5968 4100 7836 9 \n", "11 11 202002 7 6534 4530 8538 10 \n", "12 12 202001 7 9835 7019 12651 15 \n", "13 13 201952 7 7941 5246 10636 12 \n", "14 14 201951 7 5823 3675 7971 9 \n", "15 15 201950 7 6424 4276 8572 10 \n", "16 16 201949 7 6621 4540 8702 10 \n", "17 17 201948 7 5542 3383 7701 8 \n", "18 18 201947 7 7536 5058 10014 11 \n", "19 19 201946 7 2638 1316 3960 4 \n", "20 20 201945 7 4492 2615 6369 7 \n", "21 21 201944 7 5728 3627 7829 9 \n", "22 22 201943 7 4834 2751 6917 7 \n", "23 23 201942 7 6279 3989 8569 10 \n", "24 24 201941 7 4130 2030 6230 6 \n", "25 25 201940 7 4211 2218 6204 6 \n", "26 26 201939 7 3137 1310 4964 5 \n", "27 27 201938 7 3078 1416 4740 5 \n", "28 28 201937 7 970 162 1778 1 \n", "29 29 201936 7 1277 263 2291 2 \n", "... ... ... ... ... ... ... ... \n", "1500 1500 199126 7 17608 11304 23912 31 \n", "1501 1501 199125 7 16169 10700 21638 28 \n", "1502 1502 199124 7 16171 10071 22271 28 \n", "1503 1503 199123 7 11947 7671 16223 21 \n", "1504 1504 199122 7 15452 9953 20951 27 \n", "1505 1505 199121 7 14903 8975 20831 26 \n", "1506 1506 199120 7 19053 12742 25364 34 \n", "1507 1507 199119 7 16739 11246 22232 29 \n", "1508 1508 199118 7 21385 13882 28888 38 \n", "1509 1509 199117 7 13462 8877 18047 24 \n", "1510 1510 199116 7 14857 10068 19646 26 \n", "1511 1511 199115 7 13975 9781 18169 25 \n", "1512 1512 199114 7 12265 7684 16846 22 \n", "1513 1513 199113 7 9567 6041 13093 17 \n", "1514 1514 199112 7 10864 7331 14397 19 \n", "1515 1515 199111 7 15574 11184 19964 27 \n", "1516 1516 199110 7 16643 11372 21914 29 \n", "1517 1517 199109 7 13741 8780 18702 24 \n", "1518 1518 199108 7 13289 8813 17765 23 \n", "1519 1519 199107 7 12337 8077 16597 22 \n", "1520 1520 199106 7 10877 7013 14741 19 \n", "1521 1521 199105 7 10442 6544 14340 18 \n", "1522 1522 199104 7 7913 4563 11263 14 \n", "1523 1523 199103 7 15387 10484 20290 27 \n", "1524 1524 199102 7 16277 11046 21508 29 \n", "1525 1525 199101 7 15565 10271 20859 27 \n", "1526 1526 199052 7 19375 13295 25455 34 \n", "1527 1527 199051 7 19080 13807 24353 34 \n", "1528 1528 199050 7 11079 6660 15498 20 \n", "1529 1529 199049 7 1143 0 2610 2 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 8 14 FR France \n", "1 8 16 FR France \n", "2 11 19 FR France \n", "3 10 18 FR France \n", "4 16 26 FR France \n", "5 12 20 FR France \n", "6 10 18 FR France \n", "7 10 18 FR France \n", "8 10 16 FR France \n", "9 9 15 FR France \n", "10 6 12 FR France \n", "11 7 13 FR France \n", "12 11 19 FR France \n", "13 8 16 FR France \n", "14 6 12 FR France \n", "15 7 13 FR France \n", "16 7 13 FR France \n", "17 5 11 FR France \n", "18 7 15 FR France \n", "19 2 6 FR France \n", "20 4 10 FR France \n", "21 6 12 FR France \n", "22 4 10 FR France \n", "23 7 13 FR France \n", "24 3 9 FR France \n", "25 3 9 FR France \n", "26 2 8 FR France \n", "27 2 8 FR France \n", "28 0 2 FR France \n", "29 0 4 FR France \n", "... ... ... ... ... \n", "1500 20 42 FR France \n", "1501 18 38 FR France \n", "1502 17 39 FR France \n", "1503 13 29 FR France \n", "1504 17 37 FR France \n", "1505 16 36 FR France \n", "1506 23 45 FR France \n", "1507 19 39 FR France \n", "1508 25 51 FR France \n", "1509 16 32 FR France \n", "1510 18 34 FR France \n", "1511 18 32 FR France \n", "1512 14 30 FR France \n", "1513 11 23 FR France \n", "1514 13 25 FR France \n", "1515 19 35 FR France \n", "1516 20 38 FR France \n", "1517 15 33 FR France \n", "1518 15 31 FR France \n", "1519 15 29 FR France \n", "1520 12 26 FR France \n", "1521 11 25 FR France \n", "1522 8 20 FR France \n", "1523 18 36 FR France \n", "1524 20 38 FR France \n", "1525 18 36 FR France \n", "1526 23 45 FR France \n", "1527 25 43 FR France \n", "1528 12 28 FR France \n", "1529 0 5 FR France \n", "\n", "[1530 rows x 11 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(local_file)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(raw_data == '-').any(axis=1).any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il n'y a aucun point manquant dans ce jeu de données. Nous poursuivons donc l'analyse avec le jeu de données complet." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "data = raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de\n", "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", "de nombre entier. C'est comme ça que Pandas les interprète.\n", " \n", "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", "semaine. Il faut lui fournir les dates de début et de fin de\n", "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous\n", "écrivons une petite fonction Python pour cela. Ensuite, nous\n", "l'appliquons à tous les points de nos donnés. Les résultats vont\n", "dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il restent deux petites modifications à faire.\n", "\n", "Premièrement, nous définissons les périodes d'observation\n", "comme nouvel index de notre jeux de données. Ceci en fait\n", "une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans\n", "le sens chronologique." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1990-12-03/1990-12-0915291990497114302610205FRFrance
1990-12-10/1990-12-161528199050711079666015498201228FRFrance
1990-12-17/1990-12-2315271990517190801380724353342543FRFrance
1990-12-24/1990-12-3015261990527193751329525455342345FRFrance
1990-12-31/1991-01-0615251991017155651027120859271836FRFrance
1991-01-07/1991-01-1315241991027162771104621508292038FRFrance
1991-01-14/1991-01-2015231991037153871048420290271836FRFrance
1991-01-21/1991-01-2715221991047791345631126314820FRFrance
1991-01-28/1991-02-031521199105710442654414340181125FRFrance
1991-02-04/1991-02-101520199106710877701314741191226FRFrance
1991-02-11/1991-02-171519199107712337807716597221529FRFrance
1991-02-18/1991-02-241518199108713289881317765231531FRFrance
1991-02-25/1991-03-031517199109713741878018702241533FRFrance
1991-03-04/1991-03-1015161991107166431137221914292038FRFrance
1991-03-11/1991-03-1715151991117155741118419964271935FRFrance
1991-03-18/1991-03-241514199112710864733114397191325FRFrance
1991-03-25/1991-03-31151319911379567604113093171123FRFrance
1991-04-01/1991-04-071512199114712265768416846221430FRFrance
1991-04-08/1991-04-141511199115713975978118169251832FRFrance
1991-04-15/1991-04-2115101991167148571006819646261834FRFrance
1991-04-22/1991-04-281509199117713462887718047241632FRFrance
1991-04-29/1991-05-0515081991187213851388228888382551FRFrance
1991-05-06/1991-05-1215071991197167391124622232291939FRFrance
1991-05-13/1991-05-1915061991207190531274225364342345FRFrance
1991-05-20/1991-05-261505199121714903897520831261636FRFrance
1991-05-27/1991-06-021504199122715452995320951271737FRFrance
1991-06-03/1991-06-091503199123711947767116223211329FRFrance
1991-06-10/1991-06-1615021991247161711007122271281739FRFrance
1991-06-17/1991-06-2315011991257161691070021638281838FRFrance
1991-06-24/1991-06-3015001991267176081130423912312042FRFrance
....................................
2019-09-02/2019-09-0829201936712772632291204FRFrance
2019-09-09/2019-09-152820193779701621778102FRFrance
2019-09-16/2019-09-22272019387307814164740528FRFrance
2019-09-23/2019-09-29262019397313713104964528FRFrance
2019-09-30/2019-10-06252019407421122186204639FRFrance
2019-10-07/2019-10-13242019417413020306230639FRFrance
2019-10-14/2019-10-2023201942762793989856910713FRFrance
2019-10-21/2019-10-272220194374834275169177410FRFrance
2019-10-28/2019-11-032120194475728362778299612FRFrance
2019-11-04/2019-11-102020194574492261563697410FRFrance
2019-11-11/2019-11-17192019467263813163960426FRFrance
2019-11-18/2019-11-24182019477753650581001411715FRFrance
2019-11-25/2019-12-011720194875542338377018511FRFrance
2019-12-02/2019-12-0816201949766214540870210713FRFrance
2019-12-09/2019-12-1515201950764244276857210713FRFrance
2019-12-16/2019-12-221420195175823367579719612FRFrance
2019-12-23/2019-12-29132019527794152461063612816FRFrance
2019-12-30/2020-01-051220200179835701912651151119FRFrance
2020-01-06/2020-01-1211202002765344530853810713FRFrance
2020-01-13/2020-01-191020200375968410078369612FRFrance
2020-01-20/2020-01-2692020047799158311015112915FRFrance
2020-01-27/2020-02-02820200578505631410696131016FRFrance
2020-02-03/2020-02-09720200679264692511603141018FRFrance
2020-02-10/2020-02-16620200778959657411344141018FRFrance
2020-02-17/2020-02-235202008710424770813140161220FRFrance
2020-02-24/2020-03-0142020097136311054416718211626FRFrance
2020-03-02/2020-03-08320201079011669111331141018FRFrance
2020-03-09/2020-03-152202011710198756812828151119FRFrance
2020-03-16/2020-03-2212020127812357901045612816FRFrance
2020-03-23/2020-03-290202013773715268947411814FRFrance
\n", "

1530 rows × 11 columns

\n", "
" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up \\\n", "period \n", "1990-12-03/1990-12-09 1529 199049 7 1143 0 2610 \n", "1990-12-10/1990-12-16 1528 199050 7 11079 6660 15498 \n", "1990-12-17/1990-12-23 1527 199051 7 19080 13807 24353 \n", "1990-12-24/1990-12-30 1526 199052 7 19375 13295 25455 \n", "1990-12-31/1991-01-06 1525 199101 7 15565 10271 20859 \n", "1991-01-07/1991-01-13 1524 199102 7 16277 11046 21508 \n", "1991-01-14/1991-01-20 1523 199103 7 15387 10484 20290 \n", "1991-01-21/1991-01-27 1522 199104 7 7913 4563 11263 \n", "1991-01-28/1991-02-03 1521 199105 7 10442 6544 14340 \n", "1991-02-04/1991-02-10 1520 199106 7 10877 7013 14741 \n", "1991-02-11/1991-02-17 1519 199107 7 12337 8077 16597 \n", "1991-02-18/1991-02-24 1518 199108 7 13289 8813 17765 \n", "1991-02-25/1991-03-03 1517 199109 7 13741 8780 18702 \n", "1991-03-04/1991-03-10 1516 199110 7 16643 11372 21914 \n", "1991-03-11/1991-03-17 1515 199111 7 15574 11184 19964 \n", "1991-03-18/1991-03-24 1514 199112 7 10864 7331 14397 \n", "1991-03-25/1991-03-31 1513 199113 7 9567 6041 13093 \n", "1991-04-01/1991-04-07 1512 199114 7 12265 7684 16846 \n", "1991-04-08/1991-04-14 1511 199115 7 13975 9781 18169 \n", "1991-04-15/1991-04-21 1510 199116 7 14857 10068 19646 \n", "1991-04-22/1991-04-28 1509 199117 7 13462 8877 18047 \n", "1991-04-29/1991-05-05 1508 199118 7 21385 13882 28888 \n", "1991-05-06/1991-05-12 1507 199119 7 16739 11246 22232 \n", "1991-05-13/1991-05-19 1506 199120 7 19053 12742 25364 \n", "1991-05-20/1991-05-26 1505 199121 7 14903 8975 20831 \n", "1991-05-27/1991-06-02 1504 199122 7 15452 9953 20951 \n", "1991-06-03/1991-06-09 1503 199123 7 11947 7671 16223 \n", "1991-06-10/1991-06-16 1502 199124 7 16171 10071 22271 \n", "1991-06-17/1991-06-23 1501 199125 7 16169 10700 21638 \n", "1991-06-24/1991-06-30 1500 199126 7 17608 11304 23912 \n", "... ... ... ... ... ... ... \n", "2019-09-02/2019-09-08 29 201936 7 1277 263 2291 \n", "2019-09-09/2019-09-15 28 201937 7 970 162 1778 \n", "2019-09-16/2019-09-22 27 201938 7 3078 1416 4740 \n", "2019-09-23/2019-09-29 26 201939 7 3137 1310 4964 \n", "2019-09-30/2019-10-06 25 201940 7 4211 2218 6204 \n", "2019-10-07/2019-10-13 24 201941 7 4130 2030 6230 \n", "2019-10-14/2019-10-20 23 201942 7 6279 3989 8569 \n", "2019-10-21/2019-10-27 22 201943 7 4834 2751 6917 \n", "2019-10-28/2019-11-03 21 201944 7 5728 3627 7829 \n", "2019-11-04/2019-11-10 20 201945 7 4492 2615 6369 \n", "2019-11-11/2019-11-17 19 201946 7 2638 1316 3960 \n", "2019-11-18/2019-11-24 18 201947 7 7536 5058 10014 \n", "2019-11-25/2019-12-01 17 201948 7 5542 3383 7701 \n", "2019-12-02/2019-12-08 16 201949 7 6621 4540 8702 \n", "2019-12-09/2019-12-15 15 201950 7 6424 4276 8572 \n", "2019-12-16/2019-12-22 14 201951 7 5823 3675 7971 \n", "2019-12-23/2019-12-29 13 201952 7 7941 5246 10636 \n", "2019-12-30/2020-01-05 12 202001 7 9835 7019 12651 \n", "2020-01-06/2020-01-12 11 202002 7 6534 4530 8538 \n", "2020-01-13/2020-01-19 10 202003 7 5968 4100 7836 \n", "2020-01-20/2020-01-26 9 202004 7 7991 5831 10151 \n", "2020-01-27/2020-02-02 8 202005 7 8505 6314 10696 \n", "2020-02-03/2020-02-09 7 202006 7 9264 6925 11603 \n", "2020-02-10/2020-02-16 6 202007 7 8959 6574 11344 \n", "2020-02-17/2020-02-23 5 202008 7 10424 7708 13140 \n", "2020-02-24/2020-03-01 4 202009 7 13631 10544 16718 \n", "2020-03-02/2020-03-08 3 202010 7 9011 6691 11331 \n", "2020-03-09/2020-03-15 2 202011 7 10198 7568 12828 \n", "2020-03-16/2020-03-22 1 202012 7 8123 5790 10456 \n", "2020-03-23/2020-03-29 0 202013 7 7371 5268 9474 \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", "2019-09-02/2019-09-08 2 0 4 FR France \n", "2019-09-09/2019-09-15 1 0 2 FR France \n", "2019-09-16/2019-09-22 5 2 8 FR France \n", "2019-09-23/2019-09-29 5 2 8 FR France \n", "2019-09-30/2019-10-06 6 3 9 FR France \n", "2019-10-07/2019-10-13 6 3 9 FR France \n", "2019-10-14/2019-10-20 10 7 13 FR France \n", "2019-10-21/2019-10-27 7 4 10 FR France \n", "2019-10-28/2019-11-03 9 6 12 FR France \n", "2019-11-04/2019-11-10 7 4 10 FR France \n", "2019-11-11/2019-11-17 4 2 6 FR France \n", "2019-11-18/2019-11-24 11 7 15 FR France \n", "2019-11-25/2019-12-01 8 5 11 FR France \n", "2019-12-02/2019-12-08 10 7 13 FR France \n", "2019-12-09/2019-12-15 10 7 13 FR France \n", "2019-12-16/2019-12-22 9 6 12 FR France \n", "2019-12-23/2019-12-29 12 8 16 FR France \n", "2019-12-30/2020-01-05 15 11 19 FR France \n", "2020-01-06/2020-01-12 10 7 13 FR France \n", "2020-01-13/2020-01-19 9 6 12 FR France \n", "2020-01-20/2020-01-26 12 9 15 FR France \n", "2020-01-27/2020-02-02 13 10 16 FR France \n", "2020-02-03/2020-02-09 14 10 18 FR France \n", "2020-02-10/2020-02-16 14 10 18 FR France \n", "2020-02-17/2020-02-23 16 12 20 FR France \n", "2020-02-24/2020-03-01 21 16 26 FR France \n", "2020-03-02/2020-03-08 14 10 18 FR France \n", "2020-03-09/2020-03-15 15 11 19 FR France \n", "2020-03-16/2020-03-22 12 8 16 FR France \n", "2020-03-23/2020-03-29 11 8 14 FR France \n", "\n", "[1530 rows x 11 columns]" ] }, "execution_count": 8, "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 permet de vérifier qu'il n'y a pas de \"trou\" dans les données :" ] }, { "cell_type": "code", "execution_count": 9, "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": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre mieux la situation, avec un creux des incidences chaque année en septembre." ] }, { "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'][-100:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", "entre deux années civiles, nous définissons la période de référence\n", "entre deux minima de l'incidence, du 1er 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 août.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en septembre, on limite ainsi le risque de fausser nos conclusions.\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. L'année en cours, incomplète également, est aussi éliminée." ] }, { "cell_type": "code", "execution_count": 12, "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')]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]\n", "first_week" ] }, { "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": 13, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_week[:-1],\n", " first_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " assert abs(len(one_year)-52) < 2\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une liste triée permet de plus facilement répérer les valeurs extrêmes." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "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": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre une queue de distribution assez épaisse : les épidémies fortes sont assez fréquentes ; d'un autre côté, elles ne sont pas beaucoup plus fortes qu'une épidémie moyenne." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 28.000000\n", "mean 674186.607143\n", "std 90193.127425\n", "min 516689.000000\n", "25% 618680.000000\n", "50% 656943.500000\n", "75% 751222.500000\n", "max 842373.000000\n", "dtype: float64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.describe()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }