{ "cells": [ { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "# varicelle" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Les données de l'incidence la varicelle sont disponibles du site Web du Réseau Sentinelles. 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 1999 semaine 49 et se termine avec une semaine récente : 2022 semaine 6.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "raw", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "type(raw_data)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "1628" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(raw_data)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202206710414712813700161121FRFrance
1202205710866775813974161121FRFrance
220220479547672112373141018FRFrance
32022037139721068017264211626FRFrance
42022027849560261096413917FRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "0 202206 7 10414 7128 13700 16 11 21 \n", "1 202205 7 10866 7758 13974 16 11 21 \n", "2 202204 7 9547 6721 12373 14 10 18 \n", "3 202203 7 13972 10680 17264 21 16 26 \n", "4 202202 7 8495 6026 10964 13 9 17 \n", "\n", " geo_insee geo_name \n", "0 FR France \n", "1 FR France \n", "2 FR France \n", "3 FR France \n", "4 FR France " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[:5]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
16231991017155651027120859271836FRFrance
16241990527193751329525455342345FRFrance
16251990517190801380724353342543FRFrance
1626199050711079666015498201228FRFrance
16271990497114302610205FRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "1623 199101 7 15565 10271 20859 27 18 \n", "1624 199052 7 19375 13295 25455 34 23 \n", "1625 199051 7 19080 13807 24353 34 25 \n", "1626 199050 7 11079 6660 15498 20 12 \n", "1627 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "1623 36 FR France \n", "1624 45 FR France \n", "1625 43 FR France \n", "1626 28 FR France \n", "1627 5 FR France " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[-5:]" ] }, { "cell_type": "raw", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Non" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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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": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "raw", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "copie des donnés raw_data dans data, sans filtrage car il n'y a pas de données manquantes" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "data = raw_data.copy()" ] }, { "cell_type": "raw", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de semaine est collé à l'année, donnant l'impression qu'il s'agit 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 semaine. Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque isoweek.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'.\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "1628" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "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']]\n", "len(data)" ] }, { "cell_type": "raw", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "2 modifications à faire.\n", "\n", "Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans le sens chronologique.\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
0202206710414712813700161121FRFrance2022-02-07/2022-02-13
1202205710866775813974161121FRFrance2022-01-31/2022-02-06
220220479547672112373141018FRFrance2022-01-24/2022-01-30
32022037139721068017264211626FRFrance2022-01-17/2022-01-23
42022027849560261096413917FRFrance2022-01-10/2022-01-16
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "0 202206 7 10414 7128 13700 16 11 21 \n", "1 202205 7 10866 7758 13974 16 11 21 \n", "2 202204 7 9547 6721 12373 14 10 18 \n", "3 202203 7 13972 10680 17264 21 16 26 \n", "4 202202 7 8495 6026 10964 13 9 17 \n", "\n", " geo_insee geo_name period \n", "0 FR France 2022-02-07/2022-02-13 \n", "1 FR France 2022-01-31/2022-02-06 \n", "2 FR France 2022-01-24/2022-01-30 \n", "3 FR France 2022-01-17/2022-01-23 \n", "4 FR France 2022-01-10/2022-01-16 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[:5]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
16231991017155651027120859271836FRFrance1990-12-31/1991-01-06
16241990527193751329525455342345FRFrance1990-12-24/1990-12-30
16251990517190801380724353342543FRFrance1990-12-17/1990-12-23
1626199050711079666015498201228FRFrance1990-12-10/1990-12-16
16271990497114302610205FRFrance1990-12-03/1990-12-09
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "1623 199101 7 15565 10271 20859 27 18 \n", "1624 199052 7 19375 13295 25455 34 23 \n", "1625 199051 7 19080 13807 24353 34 25 \n", "1626 199050 7 11079 6660 15498 20 12 \n", "1627 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name period \n", "1623 36 FR France 1990-12-31/1991-01-06 \n", "1624 45 FR France 1990-12-24/1990-12-30 \n", "1625 43 FR France 1990-12-17/1990-12-23 \n", "1626 28 FR France 1990-12-10/1990-12-16 \n", "1627 5 FR France 1990-12-03/1990-12-09 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[-5:]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "hideCode": false, "hidePrompt": false }, "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
\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", "\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 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data[:5]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
2022-01-10/2022-01-162022027849560261096413917FRFrance
2022-01-17/2022-01-232022037139721068017264211626FRFrance
2022-01-24/2022-01-3020220479547672112373141018FRFrance
2022-01-31/2022-02-06202205710866775813974161121FRFrance
2022-02-07/2022-02-13202206710414712813700161121FRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 \\\n", "period \n", "2022-01-10/2022-01-16 202202 7 8495 6026 10964 13 \n", "2022-01-17/2022-01-23 202203 7 13972 10680 17264 21 \n", "2022-01-24/2022-01-30 202204 7 9547 6721 12373 14 \n", "2022-01-31/2022-02-06 202205 7 10866 7758 13974 16 \n", "2022-02-07/2022-02-13 202206 7 10414 7128 13700 16 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "period \n", "2022-01-10/2022-01-16 9 17 FR France \n", "2022-01-17/2022-01-23 16 26 FR France \n", "2022-01-24/2022-01-30 10 18 FR France \n", "2022-01-31/2022-02-06 11 21 FR France \n", "2022-02-07/2022-02-13 11 21 FR France " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data[-5:]" ] }, { "cell_type": "raw", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Nous vérifions la cohérence des données. Entre la fin d'une période et le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n", "Normalement il n'y aura rien." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cpt = 0\n" ] } ], "source": [ "periods = sorted_data.index\n", "cpt = 0\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)\n", "print(f'cpt = {cpt}')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "Period('2022-01-31/2022-02-06', 'W-SUN')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p1" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "premier plot" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "hideCode": false, "hideOutput": true, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "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": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "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", "\n", "Etant donné que le pic de l'épidémie se situe en hiver, à cheval entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er septembre de l'année 𝑁\n", "au 1er août de l'année 𝑁+1\n", "\n", ".\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Comme l'incidence de la varicelle est très faible en été, cette modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore 2 petit détails: les données commencent en décembre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991 et la période 01/09/2021 - 13/02/2022 ne constitue pas une année complète non plus." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "len(first_sept_week)=33\n" ] } ], "source": [ "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(sorted_data.index[0].year,\n", " sorted_data.index[-1].year+1)]\n", "print(f'len(first_sept_week)={len(first_sept_week)}')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Period('1990-08-27/1990-09-02', 'W-SUN'),\n", " 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')]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_sept_week[:5]" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[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'),\n", " Period('2021-08-30/2021-09-05', 'W-SUN'),\n", " Period('2022-08-29/2022-09-04', 'W-SUN')]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_sept_week[-5:]" ] }, { "cell_type": "raw", "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.\n" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1990-08-27/1990-09-02 1991-08-26/1991-09-01 38 is not complete\n", "1991-08-26/1991-09-01 1992-08-31/1992-09-06 53 is complete\n", "1992-08-31/1992-09-06 1993-08-30/1993-09-05 52 is complete\n", "1993-08-30/1993-09-05 1994-08-29/1994-09-04 52 is complete\n", "1994-08-29/1994-09-04 1995-08-28/1995-09-03 52 is complete\n", "1995-08-28/1995-09-03 1996-08-26/1996-09-01 52 is complete\n", "1996-08-26/1996-09-01 1997-09-01/1997-09-07 53 is complete\n", "1997-09-01/1997-09-07 1998-08-31/1998-09-06 52 is complete\n", "1998-08-31/1998-09-06 1999-08-30/1999-09-05 52 is complete\n", "1999-08-30/1999-09-05 2000-08-28/2000-09-03 52 is complete\n", "2000-08-28/2000-09-03 2001-08-27/2001-09-02 52 is complete\n", "2001-08-27/2001-09-02 2002-08-26/2002-09-01 52 is complete\n", "2002-08-26/2002-09-01 2003-09-01/2003-09-07 53 is complete\n", "2003-09-01/2003-09-07 2004-08-30/2004-09-05 52 is complete\n", "2004-08-30/2004-09-05 2005-08-29/2005-09-04 52 is complete\n", "2005-08-29/2005-09-04 2006-08-28/2006-09-03 52 is complete\n", "2006-08-28/2006-09-03 2007-08-27/2007-09-02 52 is complete\n", "2007-08-27/2007-09-02 2008-09-01/2008-09-07 53 is complete\n", "2008-09-01/2008-09-07 2009-08-31/2009-09-06 52 is complete\n", "2009-08-31/2009-09-06 2010-08-30/2010-09-05 52 is complete\n", "2010-08-30/2010-09-05 2011-08-29/2011-09-04 52 is complete\n", "2011-08-29/2011-09-04 2012-08-27/2012-09-02 52 is complete\n", "2012-08-27/2012-09-02 2013-08-26/2013-09-01 52 is complete\n", "2013-08-26/2013-09-01 2014-09-01/2014-09-07 53 is complete\n", "2014-09-01/2014-09-07 2015-08-31/2015-09-06 52 is complete\n", "2015-08-31/2015-09-06 2016-08-29/2016-09-04 52 is complete\n", "2016-08-29/2016-09-04 2017-08-28/2017-09-03 52 is complete\n", "2017-08-28/2017-09-03 2018-08-27/2018-09-02 52 is complete\n", "2018-08-27/2018-09-02 2019-08-26/2019-09-01 52 is complete\n", "2019-08-26/2019-09-01 2020-08-31/2020-09-06 53 is complete\n", "2020-08-31/2020-09-06 2021-08-30/2021-09-05 52 is complete\n", "2021-08-30/2021-09-05 2022-08-29/2022-09-04 24 is not complete\n" ] } ], "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", " if abs(len(one_year)-52) < 2 :\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)\n", " print(week1, week2, len(one_year), \"is complete\")\n", " else:\n", " print(week1, week2, len(one_year), \"is not complete\")\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "30" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(yearly_incidence)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "graphique des incidences annuelles" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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