{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la varicelle" ] }, { "cell_type": "code", "execution_count": 2, "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 de 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.\n", "\n", "Il faut passer par les menus \"Surveillance continue\" - \"Base de données\" - \"Accès aux données\" et cliquer sur l'onglet \"Télécharger\", puis choisir les données au format CSV pour la France Métropolitaine." ] }, { "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": [ "Pour des raisons de reproductibilité, nous travaillons sur une copie locale de la base de données. Pour la télécharger, il faut passer par les menus \"Surveillance continue\" - \"Base de données\" - \"Accès aux données\" et cliquer sur l'onglet \"Télécharger\", puis choisir les données au format CSV pour la France Métropolitaine.\n", "\n", "Si la copie locale n'existe pas, nous téléchargeons la base depuis le site web du réseau sentinelle." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "local_data_file = \"LOCAL-incidence-varicelle.csv\"\n", "\n", "import os.path\n", "import urllib.request\n", "if not os.path.exists(local_data_file):\n", " urllib.request.urlretrieve(data_url, local_data_file)" ] }, { "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", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1202011710198756812828151119FRFrance
220201079011669111331141018FRFrance
32020097136311054416718211626FRFrance
4202008710424770813140161220FRFrance
520200778959657411344141018FRFrance
620200679264692511603141018FRFrance
720200578505631410696131016FRFrance
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920200375968410078369612FRFrance
10202002765344530853810713FRFrance
1120200179835701912651151119FRFrance
122019527794152461063612816FRFrance
1320195175823367579719612FRFrance
14201950764244276857210713FRFrance
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1620194875542338377018511FRFrance
172019477753650581001411715FRFrance
182019467263813163960426FRFrance
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242019407421122186204639FRFrance
252019397313713104964528FRFrance
262019387307814164740528FRFrance
2720193779701621778102FRFrance
28201936712772632291204FRFrance
29201935792201857102FRFrance
.................................
14991991267176081130423912312042FRFrance
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1504199121714903897520831261636FRFrance
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15071991187213851388228888382551FRFrance
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1527199050711079666015498201228FRFrance
15281990497114302610205FRFrance
\n", "

1529 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202012 7 8210 5823 10597 12 8 \n", "1 202011 7 10198 7568 12828 15 11 \n", "2 202010 7 9011 6691 11331 14 10 \n", "3 202009 7 13631 10544 16718 21 16 \n", "4 202008 7 10424 7708 13140 16 12 \n", "5 202007 7 8959 6574 11344 14 10 \n", "6 202006 7 9264 6925 11603 14 10 \n", "7 202005 7 8505 6314 10696 13 10 \n", "8 202004 7 7991 5831 10151 12 9 \n", "9 202003 7 5968 4100 7836 9 6 \n", "10 202002 7 6534 4530 8538 10 7 \n", "11 202001 7 9835 7019 12651 15 11 \n", "12 201952 7 7941 5246 10636 12 8 \n", "13 201951 7 5823 3675 7971 9 6 \n", "14 201950 7 6424 4276 8572 10 7 \n", "15 201949 7 6621 4540 8702 10 7 \n", "16 201948 7 5542 3383 7701 8 5 \n", "17 201947 7 7536 5058 10014 11 7 \n", "18 201946 7 2638 1316 3960 4 2 \n", "19 201945 7 4492 2615 6369 7 4 \n", "20 201944 7 5728 3627 7829 9 6 \n", "21 201943 7 4834 2751 6917 7 4 \n", "22 201942 7 6279 3989 8569 10 7 \n", "23 201941 7 4130 2030 6230 6 3 \n", "24 201940 7 4211 2218 6204 6 3 \n", "25 201939 7 3137 1310 4964 5 2 \n", "26 201938 7 3078 1416 4740 5 2 \n", "27 201937 7 970 162 1778 1 0 \n", "28 201936 7 1277 263 2291 2 0 \n", "29 201935 7 922 0 1857 1 0 \n", "... ... ... ... ... ... ... ... \n", "1499 199126 7 17608 11304 23912 31 20 \n", "1500 199125 7 16169 10700 21638 28 18 \n", "1501 199124 7 16171 10071 22271 28 17 \n", "1502 199123 7 11947 7671 16223 21 13 \n", "1503 199122 7 15452 9953 20951 27 17 \n", "1504 199121 7 14903 8975 20831 26 16 \n", "1505 199120 7 19053 12742 25364 34 23 \n", "1506 199119 7 16739 11246 22232 29 19 \n", "1507 199118 7 21385 13882 28888 38 25 \n", "1508 199117 7 13462 8877 18047 24 16 \n", "1509 199116 7 14857 10068 19646 26 18 \n", "1510 199115 7 13975 9781 18169 25 18 \n", "1511 199114 7 12265 7684 16846 22 14 \n", "1512 199113 7 9567 6041 13093 17 11 \n", "1513 199112 7 10864 7331 14397 19 13 \n", "1514 199111 7 15574 11184 19964 27 19 \n", "1515 199110 7 16643 11372 21914 29 20 \n", "1516 199109 7 13741 8780 18702 24 15 \n", "1517 199108 7 13289 8813 17765 23 15 \n", "1518 199107 7 12337 8077 16597 22 15 \n", "1519 199106 7 10877 7013 14741 19 12 \n", "1520 199105 7 10442 6544 14340 18 11 \n", "1521 199104 7 7913 4563 11263 14 8 \n", "1522 199103 7 15387 10484 20290 27 18 \n", "1523 199102 7 16277 11046 21508 29 20 \n", "1524 199101 7 15565 10271 20859 27 18 \n", "1525 199052 7 19375 13295 25455 34 23 \n", "1526 199051 7 19080 13807 24353 34 25 \n", "1527 199050 7 11079 6660 15498 20 12 \n", "1528 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 16 FR France \n", "1 19 FR France \n", "2 18 FR France \n", "3 26 FR France \n", "4 20 FR France \n", "5 18 FR France \n", "6 18 FR France \n", "7 16 FR France \n", "8 15 FR France \n", "9 12 FR France \n", "10 13 FR France \n", "11 19 FR France \n", "12 16 FR France \n", "13 12 FR France \n", "14 13 FR France \n", "15 13 FR France \n", "16 11 FR France \n", "17 15 FR France \n", "18 6 FR France \n", "19 10 FR France \n", "20 12 FR France \n", "21 10 FR France \n", "22 13 FR France \n", "23 9 FR France \n", "24 9 FR France \n", "25 8 FR France \n", "26 8 FR France \n", "27 2 FR France \n", "28 4 FR France \n", "29 2 FR France \n", "... ... ... ... \n", "1499 42 FR France \n", "1500 38 FR France \n", "1501 39 FR France \n", "1502 29 FR France \n", "1503 37 FR France \n", "1504 36 FR France \n", "1505 45 FR France \n", "1506 39 FR France \n", "1507 51 FR France \n", "1508 32 FR France \n", "1509 34 FR France \n", "1510 32 FR France \n", "1511 30 FR France \n", "1512 23 FR France \n", "1513 25 FR France \n", "1514 35 FR France \n", "1515 38 FR France \n", "1516 33 FR France \n", "1517 31 FR France \n", "1518 29 FR France \n", "1519 26 FR France \n", "1520 25 FR France \n", "1521 20 FR France \n", "1522 36 FR France \n", "1523 38 FR France \n", "1524 36 FR France \n", "1525 45 FR France \n", "1526 43 FR France \n", "1527 28 FR France \n", "1528 5 FR France \n", "\n", "[1529 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(local_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." ] }, { "cell_type": "code", "execution_count": 7, "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": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "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'" ] }, { "cell_type": "code", "execution_count": 9, "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": [ "Il restent deux petites 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." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "sorted_data = raw_data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "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", "\n", "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives entre lesquelles il manque une semaine.\n", "\n", "Nous reconnaissons ces dates: c'est la semaine sans observations que nous avions supprimées !" ] }, { "cell_type": "code", "execution_count": 12, "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": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "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 des pics en hiver. Le creux des incidences se trouve en été." ] }, { "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", "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 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 août de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent en novembre 1990, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "first_septembre_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 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": 16, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_septembre_week[:-1],\n", " first_septembre_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": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { 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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 les plus élevées (à la fin)." ] }, { "cell_type": "code", "execution_count": 19, "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": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population française, sont assez rares: il y en eu trois au cours des 35 dernières années." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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