{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercice 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": [ "Télécharger la base de données pour la varicelle à partir du réseau Sentinelles. Les données sont disponibles depuis la semaine 49 en 1990." ] }, { "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": [ "Descriptif des variables sour le [fichier d'origine](https://www.sentiweb.fr/france/fr/?page=table&maladie=7)\n", "\n", "Schema\n", "Name\tType\tLabel\tDescription\n", "week PK\tinteger\t\tISO8601 Yearweek number as numeric (year*100 + week nubmer)\n", "geo_insee PK\tstring\t\tIdentifier of the geographic area, from INSEE https://www.insee.fr\n", "geo_name\tstring\t\tGeographic label of the area, corresponding to INSEE code. This label is not an id and is only provided for human reading\n", "indicator PK\tinteger\t\tUnique identifier of the indicator, see metadata document https://www.sentiweb.fr/meta.json\n", "inc\tinteger\t\tEstimated incidence value for the time step, in the geographic level\n", "inc_low\tinteger\t\tLower bound of the estimated incidence 95% Confidence Interval\n", "inc_up\tinteger\t\tUpper bound of the estimated incidence 95% Confidence Interval\n", "inc100\tinteger\t\tEstimated rate incidence per 100,000 inhabitants\n", "inc100_low\tinteger\t\tLower bound of the estimated incidence 95% Confidence Interval\n", "inc100_up\tinteger\t\tUpper bound of the estimated rate incidence 95% Confidence Interval\n", "Missing value : -\n", "\n", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant skiprows=1." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1202105712379910715651191424FRFrance
2202104712026882615226181323FRFrance
32021037891363751145113917FRFrance
42021027779554301016012816FRFrance
5202101710525775013300161220FRFrance
6202053711978840615550181323FRFrance
7202052712012828515739181224FRFrance
8202051710564757413554161121FRFrance
9202050770634744938211715FRFrance
1020204975026314569078511FRFrance
11202048766834312905410614FRFrance
1220204774999296370358511FRFrance
132020467375219635541639FRFrance
142020457369620165376639FRFrance
1520204474391237564077410FRFrance
1620204374376250562477410FRFrance
172020427400019796021639FRFrance
182020417396120995823639FRFrance
19202040720786753481315FRFrance
20202039710492371861213FRFrance
21202038722537823724315FRFrance
22202037715844052763204FRFrance
2320203679191001738102FRFrance
24202035782801694102FRFrance
25202034722723714173306FRFrance
26202033712841772391204FRFrance
27202032726506894611417FRFrance
28202031713031002506204FRFrance
2920203071385752695204FRFrance
.................................
15461991267176081130423912312042FRFrance
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1550199122715452995320951271737FRFrance
1551199121714903897520831261636FRFrance
15521991207190531274225364342345FRFrance
15531991197167391124622232291939FRFrance
15541991187213851388228888382551FRFrance
1555199117713462887718047241632FRFrance
15561991167148571006819646261834FRFrance
1557199115713975978118169251832FRFrance
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1574199050711079666015498201228FRFrance
15751990497114302610205FRFrance
\n", "

1576 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202106 7 14719 10462 18976 22 16 \n", "1 202105 7 12379 9107 15651 19 14 \n", "2 202104 7 12026 8826 15226 18 13 \n", "3 202103 7 8913 6375 11451 13 9 \n", "4 202102 7 7795 5430 10160 12 8 \n", "5 202101 7 10525 7750 13300 16 12 \n", "6 202053 7 11978 8406 15550 18 13 \n", "7 202052 7 12012 8285 15739 18 12 \n", "8 202051 7 10564 7574 13554 16 11 \n", "9 202050 7 7063 4744 9382 11 7 \n", "10 202049 7 5026 3145 6907 8 5 \n", "11 202048 7 6683 4312 9054 10 6 \n", "12 202047 7 4999 2963 7035 8 5 \n", "13 202046 7 3752 1963 5541 6 3 \n", "14 202045 7 3696 2016 5376 6 3 \n", "15 202044 7 4391 2375 6407 7 4 \n", "16 202043 7 4376 2505 6247 7 4 \n", "17 202042 7 4000 1979 6021 6 3 \n", "18 202041 7 3961 2099 5823 6 3 \n", "19 202040 7 2078 675 3481 3 1 \n", "20 202039 7 1049 237 1861 2 1 \n", "21 202038 7 2253 782 3724 3 1 \n", "22 202037 7 1584 405 2763 2 0 \n", "23 202036 7 919 100 1738 1 0 \n", "24 202035 7 828 0 1694 1 0 \n", "25 202034 7 2272 371 4173 3 0 \n", "26 202033 7 1284 177 2391 2 0 \n", "27 202032 7 2650 689 4611 4 1 \n", "28 202031 7 1303 100 2506 2 0 \n", "29 202030 7 1385 75 2695 2 0 \n", "... ... ... ... ... ... ... ... \n", "1546 199126 7 17608 11304 23912 31 20 \n", "1547 199125 7 16169 10700 21638 28 18 \n", "1548 199124 7 16171 10071 22271 28 17 \n", "1549 199123 7 11947 7671 16223 21 13 \n", "1550 199122 7 15452 9953 20951 27 17 \n", "1551 199121 7 14903 8975 20831 26 16 \n", "1552 199120 7 19053 12742 25364 34 23 \n", "1553 199119 7 16739 11246 22232 29 19 \n", "1554 199118 7 21385 13882 28888 38 25 \n", "1555 199117 7 13462 8877 18047 24 16 \n", "1556 199116 7 14857 10068 19646 26 18 \n", "1557 199115 7 13975 9781 18169 25 18 \n", "1558 199114 7 12265 7684 16846 22 14 \n", "1559 199113 7 9567 6041 13093 17 11 \n", "1560 199112 7 10864 7331 14397 19 13 \n", "1561 199111 7 15574 11184 19964 27 19 \n", "1562 199110 7 16643 11372 21914 29 20 \n", "1563 199109 7 13741 8780 18702 24 15 \n", "1564 199108 7 13289 8813 17765 23 15 \n", "1565 199107 7 12337 8077 16597 22 15 \n", "1566 199106 7 10877 7013 14741 19 12 \n", "1567 199105 7 10442 6544 14340 18 11 \n", "1568 199104 7 7913 4563 11263 14 8 \n", "1569 199103 7 15387 10484 20290 27 18 \n", "1570 199102 7 16277 11046 21508 29 20 \n", "1571 199101 7 15565 10271 20859 27 18 \n", "1572 199052 7 19375 13295 25455 34 23 \n", "1573 199051 7 19080 13807 24353 34 25 \n", "1574 199050 7 11079 6660 15498 20 12 \n", "1575 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 28 FR France \n", "1 24 FR France \n", "2 23 FR France \n", "3 17 FR France \n", "4 16 FR France \n", "5 20 FR France \n", "6 23 FR France \n", "7 24 FR France \n", "8 21 FR France \n", "9 15 FR France \n", "10 11 FR France \n", "11 14 FR France \n", "12 11 FR France \n", "13 9 FR France \n", "14 9 FR France \n", "15 10 FR France \n", "16 10 FR France \n", "17 9 FR France \n", "18 9 FR France \n", "19 5 FR France \n", "20 3 FR France \n", "21 5 FR France \n", "22 4 FR France \n", "23 2 FR France \n", "24 2 FR France \n", "25 6 FR France \n", "26 4 FR France \n", "27 7 FR France \n", "28 4 FR France \n", "29 4 FR France \n", "... ... ... ... \n", "1546 42 FR France \n", "1547 38 FR France \n", "1548 39 FR France \n", "1549 29 FR France \n", "1550 37 FR France \n", "1551 36 FR France \n", "1552 45 FR France \n", "1553 39 FR France \n", "1554 51 FR France \n", "1555 32 FR France \n", "1556 34 FR France \n", "1557 32 FR France \n", "1558 30 FR France \n", "1559 23 FR France \n", "1560 25 FR France \n", "1561 35 FR France \n", "1562 38 FR France \n", "1563 33 FR France \n", "1564 31 FR France \n", "1565 29 FR France \n", "1566 26 FR France \n", "1567 25 FR France \n", "1568 20 FR France \n", "1569 36 FR France \n", "1570 38 FR France \n", "1571 36 FR France \n", "1572 45 FR France \n", "1573 43 FR France \n", "1574 28 FR France \n", "1575 5 FR France \n", "\n", "[1576 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "On recherche des points manquants dans ce jeu de données." ] }, { "cell_type": "code", "execution_count": 4, "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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il n'y a pas de données manquantes. On peut continuer.\n", "Il faut maintenant faire comprendre que la variable **week** est en fait comprise de l'année suivie du numéro de semaine. \n", "Nous utilisons donc le code python pour créer une nouvelle variable **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", "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__*Attention*__ : comme il n'y a pas eu de nettoyage de la base, elle s'appelle toujours **raw_data**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On considère que la période est un index du jeu de données et on trie par ordre croissant." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "sorted_data = raw_data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions maintenant qu'il n'y a pas de saut dans le jeu de données, c'est à dire que la fin d'une période est directement suivie du début de la suivante. " ] }, { "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": [ "Il n'y a pas d'erreur. On peut continuer et visualiser 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il y a beaucoup d'information. Regardons une période plus courte." ] }, { "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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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le pic d'incidence est beaucoup plus large que pour la grippe. On détermine le creux de chaque vaguer au 1er septembre. Il faut donc recoder les périodes/années du 1er septembre de l'année N-1 au 31 aout de l'année N. Comme la première donnée est en novembre 1990, cette année est incomplète, on commence donc le 1er septembre 1991." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "first_september_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": [ "On vérifie que les années créées ont bien entre 51 et 52 semaines." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "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)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut maintenant visualiser les données par année." ] }, { "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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour répondre à la question de l'exercice, on peut trier ces données dans une liste. Les valeurs les plus faibles sont en début, les plus fortes en fin de 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'année 2020 n'est pas encore terminée (elle dure jusuq'au 31 aout 2021). L'année avec l'incidence la plus faible est donc **2002** et celle avec l'incidence la plus forte est **2009**." ] } ], "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": 2 }