{ "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 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 1990 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", "\n", "| Nom de colonne | Libellé de colonne |\n", "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", "| week | Semaine calendaire (ISO 8601) |\n", "| indicator | Code de l'indicateur de surveillance |\n", "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", "\n", "Pour plus de reproductibilité, nous allons télécharger les données sous format csv d'abord puis nous allons lire ce fichier plutôt que l'url directement." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"varicelle.csv\"\n", "\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 première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `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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5202316711387801414760171222FRFrance
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1020231174919288069587410FRFrance
1120231074854273169777410FRFrance
12202309770044548946011715FRFrance
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14202307765953782940810614FRFrance
152023067959560171317314919FRFrance
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1720230476299397386259612FRFrance
1820230376063379883289612FRFrance
192023027657630601009210515FRFrance
202023017815354701083612816FRFrance
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2520224874985304369278511FRFrance
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272022467303313924674537FRFrance
282022457382717205934639FRFrance
292022447427122316311639FRFrance
.................................
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\n", "

1695 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202321 7 13155 7879 18431 20 12 \n", "1 202320 7 9078 4647 13509 14 7 \n", "2 202319 7 9344 6091 12597 14 9 \n", "3 202318 7 10671 7291 14051 16 11 \n", "4 202317 7 9184 6162 12206 14 9 \n", "5 202316 7 11387 8014 14760 17 12 \n", "6 202315 7 14040 7613 20467 21 11 \n", "7 202314 7 15247 11032 19462 23 17 \n", "8 202313 7 13322 9700 16944 20 15 \n", "9 202312 7 10374 7218 13530 16 11 \n", "10 202311 7 4919 2880 6958 7 4 \n", "11 202310 7 4854 2731 6977 7 4 \n", "12 202309 7 7004 4548 9460 11 7 \n", "13 202308 7 8175 5316 11034 12 8 \n", "14 202307 7 6595 3782 9408 10 6 \n", "15 202306 7 9595 6017 13173 14 9 \n", "16 202305 7 6237 3907 8567 9 5 \n", "17 202304 7 6299 3973 8625 9 6 \n", "18 202303 7 6063 3798 8328 9 6 \n", "19 202302 7 6576 3060 10092 10 5 \n", "20 202301 7 8153 5470 10836 12 8 \n", "21 202252 7 5171 2717 7625 8 4 \n", "22 202251 7 6226 3822 8630 9 5 \n", "23 202250 7 6590 3100 10080 10 5 \n", "24 202249 7 5095 3212 6978 8 5 \n", "25 202248 7 4985 3043 6927 8 5 \n", "26 202247 7 6087 3733 8441 9 5 \n", "27 202246 7 3033 1392 4674 5 3 \n", "28 202245 7 3827 1720 5934 6 3 \n", "29 202244 7 4271 2231 6311 6 3 \n", "... ... ... ... ... ... ... ... \n", "1665 199126 7 17608 11304 23912 31 20 \n", "1666 199125 7 16169 10700 21638 28 18 \n", "1667 199124 7 16171 10071 22271 28 17 \n", "1668 199123 7 11947 7671 16223 21 13 \n", "1669 199122 7 15452 9953 20951 27 17 \n", "1670 199121 7 14903 8975 20831 26 16 \n", "1671 199120 7 19053 12742 25364 34 23 \n", "1672 199119 7 16739 11246 22232 29 19 \n", "1673 199118 7 21385 13882 28888 38 25 \n", "1674 199117 7 13462 8877 18047 24 16 \n", "1675 199116 7 14857 10068 19646 26 18 \n", "1676 199115 7 13975 9781 18169 25 18 \n", "1677 199114 7 12265 7684 16846 22 14 \n", "1678 199113 7 9567 6041 13093 17 11 \n", "1679 199112 7 10864 7331 14397 19 13 \n", "1680 199111 7 15574 11184 19964 27 19 \n", "1681 199110 7 16643 11372 21914 29 20 \n", "1682 199109 7 13741 8780 18702 24 15 \n", "1683 199108 7 13289 8813 17765 23 15 \n", "1684 199107 7 12337 8077 16597 22 15 \n", "1685 199106 7 10877 7013 14741 19 12 \n", "1686 199105 7 10442 6544 14340 18 11 \n", "1687 199104 7 7913 4563 11263 14 8 \n", "1688 199103 7 15387 10484 20290 27 18 \n", "1689 199102 7 16277 11046 21508 29 20 \n", "1690 199101 7 15565 10271 20859 27 18 \n", "1691 199052 7 19375 13295 25455 34 23 \n", "1692 199051 7 19080 13807 24353 34 25 \n", "1693 199050 7 11079 6660 15498 20 12 \n", "1694 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 28 FR France \n", "1 21 FR France \n", "2 19 FR France \n", "3 21 FR France \n", "4 19 FR France \n", "5 22 FR France \n", "6 31 FR France \n", "7 29 FR France \n", "8 25 FR France \n", "9 21 FR France \n", "10 10 FR France \n", "11 10 FR France \n", "12 15 FR France \n", "13 16 FR France \n", "14 14 FR France \n", "15 19 FR France \n", "16 13 FR France \n", "17 12 FR France \n", "18 12 FR France \n", "19 15 FR France \n", "20 16 FR France \n", "21 12 FR France \n", "22 13 FR France \n", "23 15 FR France \n", "24 11 FR France \n", "25 11 FR France \n", "26 13 FR France \n", "27 7 FR France \n", "28 9 FR France \n", "29 9 FR France \n", "... ... ... ... \n", "1665 42 FR France \n", "1666 38 FR France \n", "1667 39 FR France \n", "1668 29 FR France \n", "1669 37 FR France \n", "1670 36 FR France \n", "1671 45 FR France \n", "1672 39 FR France \n", "1673 51 FR France \n", "1674 32 FR France \n", "1675 34 FR France \n", "1676 32 FR France \n", "1677 30 FR France \n", "1678 23 FR France \n", "1679 25 FR France \n", "1680 35 FR France \n", "1681 38 FR France \n", "1682 33 FR France \n", "1683 31 FR France \n", "1684 29 FR France \n", "1685 26 FR France \n", "1686 25 FR France \n", "1687 20 FR France \n", "1688 36 FR France \n", "1689 38 FR France \n", "1690 36 FR France \n", "1691 45 FR France \n", "1692 43 FR France \n", "1693 28 FR France \n", "1694 5 FR France \n", "\n", "[1695 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Non, il n'y a donc rien à enlever pour ça. Nous allons néanmoins créer une copie de données données brutes avant de les modifier/travailler dessus." ] }, { "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": "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": [ "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": 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.\n", "\n", "Ceci s'avère tout à fait juste." ] }, { "cell_type": "code", "execution_count": 11, "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": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "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'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en fin d'été." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].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 août de l'année $N$ au\n", "1er août 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 varicelle est très faible en fin d'été, 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": 20, "metadata": {}, "outputs": [], "source": [ "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er 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": 21, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_sept_week[:-1],\n", " first_sept_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " assert abs(len(one_year)-52) < 2\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "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 repérer les valeurs les plus élevées (à la fin)." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "2021 376290\n", "2002 516689\n", "2018 542312\n", "2017 551041\n", "1996 564901\n", "2019 584066\n", "2015 604382\n", "2000 617597\n", "2001 619041\n", "2012 624573\n", "2005 628464\n", "2006 632833\n", "2022 641397\n", "2011 642368\n", "1993 643387\n", "1995 652478\n", "1994 661409\n", "1998 677775\n", "1997 683434\n", "2014 685769\n", "2013 698332\n", "2007 717352\n", "2008 749478\n", "1999 756456\n", "2003 758363\n", "2004 777388\n", "2016 782114\n", "2010 829911\n", "1992 832939\n", "2009 842373\n", "dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre que les épidémies ont une incidence autour de 600 000 et 700 000. Quelques rares épidémies ont une incidence beaucoup plus faible." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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