{ "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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données proviennent du site du [réseau Sentinelle](https://www.sentiweb.fr/france/fr/?page=table). Nous les téléchargeons au format CSV." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = 'https://www.sentiweb.fr/datasets/incidence-PAY-7.csv'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour importer les données, on vérifie si le fichier CSV a une copie locale. Si ce n'est pas le cas, on télécharge les données depuis l'url indiqué:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"incidence-varicelle.csv\"\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La structure des données est la suivante, elle n'est pas indiquée comme indiqué sur le [site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json) mais c'est bien celle du fichier CSV\n", "\n", "| Nom de colonne | Libellé de colonne |\n", "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", "| week | Semaine calendaire (ISO 8601) |\n", "| indicator | Unique identifier of the indicator, see [metadata document](https://www.sentiweb.fr/meta.json) |\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", " |\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", " |\n", "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/\n", " |\n", "| geo_name | Libellé de la zone géographique\n", " |\n", "\n", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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11202109710988793814038171222FRFrance
12202108711281836114201171321FRFrance
132021077135611031516807211626FRFrance
14202106713401981016992201525FRFrance
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182021027779554301016012816FRFrance
19202101710525775013300161220FRFrance
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272020467375219635541639FRFrance
282020457369620165376639FRFrance
2920204474391237564077410FRFrance
.................................
15601991267176081130423912312042FRFrance
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\n", "

1590 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202120 7 8003 4590 11416 12 7 \n", "1 202119 7 6654 4370 8938 10 7 \n", "2 202118 7 3912 2110 5714 6 3 \n", "3 202117 7 4686 2878 6494 7 4 \n", "4 202116 7 4780 2891 6669 7 4 \n", "5 202115 7 11215 7627 14803 17 12 \n", "6 202114 7 11197 7994 14400 17 12 \n", "7 202113 7 9714 6289 13139 15 10 \n", "8 202112 7 11520 8415 14625 17 12 \n", "9 202111 7 9386 6678 12094 14 10 \n", "10 202110 7 9056 6452 11660 14 10 \n", "11 202109 7 10988 7938 14038 17 12 \n", "12 202108 7 11281 8361 14201 17 13 \n", "13 202107 7 13561 10315 16807 21 16 \n", "14 202106 7 13401 9810 16992 20 15 \n", "15 202105 7 12210 8988 15432 18 13 \n", "16 202104 7 12026 8826 15226 18 13 \n", "17 202103 7 8913 6375 11451 13 9 \n", "18 202102 7 7795 5430 10160 12 8 \n", "19 202101 7 10525 7750 13300 16 12 \n", "20 202053 7 11978 8406 15550 18 13 \n", "21 202052 7 12012 8285 15739 18 12 \n", "22 202051 7 10564 7574 13554 16 11 \n", "23 202050 7 7063 4744 9382 11 7 \n", "24 202049 7 5026 3145 6907 8 5 \n", "25 202048 7 6683 4312 9054 10 6 \n", "26 202047 7 4999 2963 7035 8 5 \n", "27 202046 7 3752 1963 5541 6 3 \n", "28 202045 7 3696 2016 5376 6 3 \n", "29 202044 7 4391 2375 6407 7 4 \n", "... ... ... ... ... ... ... ... \n", "1560 199126 7 17608 11304 23912 31 20 \n", "1561 199125 7 16169 10700 21638 28 18 \n", "1562 199124 7 16171 10071 22271 28 17 \n", "1563 199123 7 11947 7671 16223 21 13 \n", "1564 199122 7 15452 9953 20951 27 17 \n", "1565 199121 7 14903 8975 20831 26 16 \n", "1566 199120 7 19053 12742 25364 34 23 \n", "1567 199119 7 16739 11246 22232 29 19 \n", "1568 199118 7 21385 13882 28888 38 25 \n", "1569 199117 7 13462 8877 18047 24 16 \n", "1570 199116 7 14857 10068 19646 26 18 \n", "1571 199115 7 13975 9781 18169 25 18 \n", "1572 199114 7 12265 7684 16846 22 14 \n", "1573 199113 7 9567 6041 13093 17 11 \n", "1574 199112 7 10864 7331 14397 19 13 \n", "1575 199111 7 15574 11184 19964 27 19 \n", "1576 199110 7 16643 11372 21914 29 20 \n", "1577 199109 7 13741 8780 18702 24 15 \n", "1578 199108 7 13289 8813 17765 23 15 \n", "1579 199107 7 12337 8077 16597 22 15 \n", "1580 199106 7 10877 7013 14741 19 12 \n", "1581 199105 7 10442 6544 14340 18 11 \n", "1582 199104 7 7913 4563 11263 14 8 \n", "1583 199103 7 15387 10484 20290 27 18 \n", "1584 199102 7 16277 11046 21508 29 20 \n", "1585 199101 7 15565 10271 20859 27 18 \n", "1586 199052 7 19375 13295 25455 34 23 \n", "1587 199051 7 19080 13807 24353 34 25 \n", "1588 199050 7 11079 6660 15498 20 12 \n", "1589 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 17 FR France \n", "1 13 FR France \n", "2 9 FR France \n", "3 10 FR France \n", "4 10 FR France \n", "5 22 FR France \n", "6 22 FR France \n", "7 20 FR France \n", "8 22 FR France \n", "9 18 FR France \n", "10 18 FR France \n", "11 22 FR France \n", "12 21 FR France \n", "13 26 FR France \n", "14 25 FR France \n", "15 23 FR France \n", "16 23 FR France \n", "17 17 FR France \n", "18 16 FR France \n", "19 20 FR France \n", "20 23 FR France \n", "21 24 FR France \n", "22 21 FR France \n", "23 15 FR France \n", "24 11 FR France \n", "25 14 FR France \n", "26 11 FR France \n", "27 9 FR France \n", "28 9 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1560 42 FR France \n", "1561 38 FR France \n", "1562 39 FR France \n", "1563 29 FR France \n", "1564 37 FR France \n", "1565 36 FR France \n", "1566 45 FR France \n", "1567 39 FR France \n", "1568 51 FR France \n", "1569 32 FR France \n", "1570 34 FR France \n", "1571 32 FR France \n", "1572 30 FR France \n", "1573 23 FR France \n", "1574 25 FR France \n", "1575 35 FR France \n", "1576 38 FR France \n", "1577 33 FR France \n", "1578 31 FR France \n", "1579 29 FR France \n", "1580 26 FR France \n", "1581 25 FR France \n", "1582 20 FR France \n", "1583 36 FR France \n", "1584 38 FR France \n", "1585 36 FR France \n", "1586 45 FR France \n", "1587 43 FR France \n", "1588 28 FR France \n", "1589 5 FR France \n", "\n", "[1590 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une analyse visuelle des données ne montre pas de données manquantes mais nous vérifions tout de même:" ] }, { "cell_type": "code", "execution_count": 6, "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": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous gardons toutes les données en effectuant tout de même une copie:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data = raw_data.copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de\n", "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", "de nombre entier. C'est comme ça que Pandas les interprète.\n", " \n", "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", "semaine. Il faut lui fournir les dates de début et de fin de\n", "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous\n", "écrivons une petite fonction Python pour cela. Ensuite, nous\n", "l'appliquons à tous les points de nos donnés. Les résultats vont\n", "dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous définissons ensuite 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": 9, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "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." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous visualisons une première fois nos données:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "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 septembre.\n", "\n", "Comme l'incidence de la varicelle semble minimale à cette période de l'année, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent à la fin de l'année 1990, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 13, "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": 13, "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": [ "En partant de cette liste des semaines qui contiennent un 1er août, 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": 14, "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": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_september_week[:-1],\n", " 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": [ "Ci-dessous les incidences annuelles:" ] }, { "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour repérer les incidences maximales, on trie la liste:" ] }, { "cell_type": "code", "execution_count": 16, "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": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'épidémie la plus forte a eu lieu en 2009 et la plus faible en 2020. Cependant, l'année 2020 semble corrompue, peut-être du fait de la situation sanitaire. Nous prenons donc 2002 comme année de l'épidémie la plus faible.\n", "\n", "Pour repérer si les épidémies les plus fortes sont courantes ou rares, on représente les données en histogramme:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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