{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la varicelle\n", "\n", "Adaptation du notebook sur les syndrômes grippauxau cas 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." ] }, { "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", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_path = r\"C:\\Users\\alecouffe\\Desktop\\incidence-PAY-7.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_path):\n", " urllib.request.urlretrieve(data_url, data_path)" ] }, { "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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120214074299233262667410FRFrance
22021397229110563526315FRFrance
320213874325226763837410FRFrance
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52021367344117305152528FRFrance
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7202134714293782480204FRFrance
82021337382918305828639FRFrance
92021327410818956321639FRFrance
1020213174793230172857311FRFrance
112021307719041911018911616FRFrance
12202129768004109949110614FRFrance
132021287973402173115033FRFrance
142021277902643161373614721FRFrance
152021267728441081046011616FRFrance
1620212579351654012162141018FRFrance
17202124712034893715131181323FRFrance
1820212379116642011812141018FRFrance
1920212274817275268827410FRFrance
2020212176092345887269513FRFrance
212021207748546011036911715FRFrance
22202119766544370893810713FRFrance
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2520211674780289166697410FRFrance
26202115711215762714803171222FRFrance
27202114711197799414400171222FRFrance
2820211379714628913139151020FRFrance
29202112711520841514625171222FRFrance
.................................
15811991267176081130423912312042FRFrance
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15831991247161711007122271281739FRFrance
1584199123711947767116223211329FRFrance
1585199122715452995320951271737FRFrance
1586199121714903897520831261636FRFrance
15871991207190531274225364342345FRFrance
15881991197167391124622232291939FRFrance
15891991187213851388228888382551FRFrance
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\n", "

1611 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202141 7 3716 1661 5771 6 3 \n", "1 202140 7 4299 2332 6266 7 4 \n", "2 202139 7 2291 1056 3526 3 1 \n", "3 202138 7 4325 2267 6383 7 4 \n", "4 202137 7 1964 754 3174 3 1 \n", "5 202136 7 3441 1730 5152 5 2 \n", "6 202135 7 2562 1107 4017 4 2 \n", "7 202134 7 1429 378 2480 2 0 \n", "8 202133 7 3829 1830 5828 6 3 \n", "9 202132 7 4108 1895 6321 6 3 \n", "10 202131 7 4793 2301 7285 7 3 \n", "11 202130 7 7190 4191 10189 11 6 \n", "12 202129 7 6800 4109 9491 10 6 \n", "13 202128 7 9734 0 21731 15 0 \n", "14 202127 7 9026 4316 13736 14 7 \n", "15 202126 7 7284 4108 10460 11 6 \n", "16 202125 7 9351 6540 12162 14 10 \n", "17 202124 7 12034 8937 15131 18 13 \n", "18 202123 7 9116 6420 11812 14 10 \n", "19 202122 7 4817 2752 6882 7 4 \n", "20 202121 7 6092 3458 8726 9 5 \n", "21 202120 7 7485 4601 10369 11 7 \n", "22 202119 7 6654 4370 8938 10 7 \n", "23 202118 7 3912 2110 5714 6 3 \n", "24 202117 7 4686 2878 6494 7 4 \n", "25 202116 7 4780 2891 6669 7 4 \n", "26 202115 7 11215 7627 14803 17 12 \n", "27 202114 7 11197 7994 14400 17 12 \n", "28 202113 7 9714 6289 13139 15 10 \n", "29 202112 7 11520 8415 14625 17 12 \n", "... ... ... ... ... ... ... ... \n", "1581 199126 7 17608 11304 23912 31 20 \n", "1582 199125 7 16169 10700 21638 28 18 \n", "1583 199124 7 16171 10071 22271 28 17 \n", "1584 199123 7 11947 7671 16223 21 13 \n", "1585 199122 7 15452 9953 20951 27 17 \n", "1586 199121 7 14903 8975 20831 26 16 \n", "1587 199120 7 19053 12742 25364 34 23 \n", "1588 199119 7 16739 11246 22232 29 19 \n", "1589 199118 7 21385 13882 28888 38 25 \n", "1590 199117 7 13462 8877 18047 24 16 \n", "1591 199116 7 14857 10068 19646 26 18 \n", "1592 199115 7 13975 9781 18169 25 18 \n", "1593 199114 7 12265 7684 16846 22 14 \n", "1594 199113 7 9567 6041 13093 17 11 \n", "1595 199112 7 10864 7331 14397 19 13 \n", "1596 199111 7 15574 11184 19964 27 19 \n", "1597 199110 7 16643 11372 21914 29 20 \n", "1598 199109 7 13741 8780 18702 24 15 \n", "1599 199108 7 13289 8813 17765 23 15 \n", "1600 199107 7 12337 8077 16597 22 15 \n", "1601 199106 7 10877 7013 14741 19 12 \n", "1602 199105 7 10442 6544 14340 18 11 \n", "1603 199104 7 7913 4563 11263 14 8 \n", "1604 199103 7 15387 10484 20290 27 18 \n", "1605 199102 7 16277 11046 21508 29 20 \n", "1606 199101 7 15565 10271 20859 27 18 \n", "1607 199052 7 19375 13295 25455 34 23 \n", "1608 199051 7 19080 13807 24353 34 25 \n", "1609 199050 7 11079 6660 15498 20 12 \n", "1610 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 9 FR France \n", "1 10 FR France \n", "2 5 FR France \n", "3 10 FR France \n", "4 5 FR France \n", "5 8 FR France \n", "6 6 FR France \n", "7 4 FR France \n", "8 9 FR France \n", "9 9 FR France \n", "10 11 FR France \n", "11 16 FR France \n", "12 14 FR France \n", "13 33 FR France \n", "14 21 FR France \n", "15 16 FR France \n", "16 18 FR France \n", "17 23 FR France \n", "18 18 FR France \n", "19 10 FR France \n", "20 13 FR France \n", "21 15 FR France \n", "22 13 FR France \n", "23 9 FR France \n", "24 10 FR France \n", "25 10 FR France \n", "26 22 FR France \n", "27 22 FR France \n", "28 20 FR France \n", "29 22 FR France \n", "... ... ... ... \n", "1581 42 FR France \n", "1582 38 FR France \n", "1583 39 FR France \n", "1584 29 FR France \n", "1585 37 FR France \n", "1586 36 FR France \n", "1587 45 FR France \n", "1588 39 FR France \n", "1589 51 FR France \n", "1590 32 FR France \n", "1591 34 FR France \n", "1592 32 FR France \n", "1593 30 FR France \n", "1594 23 FR France \n", "1595 25 FR France \n", "1596 35 FR France \n", "1597 38 FR France \n", "1598 33 FR France \n", "1599 31 FR France \n", "1600 29 FR France \n", "1601 26 FR France \n", "1602 25 FR France \n", "1603 20 FR France \n", "1604 36 FR France \n", "1605 38 FR France \n", "1606 36 FR France \n", "1607 45 FR France \n", "1608 43 FR France \n", "1609 28 FR France \n", "1610 5 FR France \n", "\n", "[1611 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(data_path, skiprows=1)\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La base de donnée commence fin décembre 1990 et va jusqu'à la 14e semaine de 2021.\n", "Une première vérification à l'oeil de la base de donnée ne donne pas d'anomalies.\n", "Quand même qu'aucune ligne ne contient des données manquantes." ] }, { "cell_type": "code", "execution_count": 5, "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Effectivement, aucune donnée ne semble manquer.\n", "\n", "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": 6, "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": 7, "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 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." ] }, { "cell_type": "code", "execution_count": 8, "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 avons fait les vérifications les plus évidentes. Regardons-les !" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "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": [ "Zoom sur les dernières années." ] }, { "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": [ "Nous définissons la période de référence le 1er septembre.\n", "\n", "Les données commencent en fin de l'année 1990. Nous commencerons l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 12, "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": [ "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": 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 donc tracer 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": [ "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." ] }, { "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": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "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.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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 }