{ "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 de la varicelle 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. Les données vont de 1990 à 2020.\n", "\n", "Afin d'éviter que les modifications du jeu de données d'oorigine n'affecte notre étude, nous allons télécharger les données et les enregistrer sur un fichier local. Ne voulant pas que le téléchargement du fihchier local soit effectué à chaque éxecution du code et écrase le fichier précédemment téléchargé, une vérification de l'existence du fichier sera effectuée avant que la décision de télécharger le fichier ne soit prise." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_file=\"varicelle.csv\"\n", "data_url=\"http://www.sentiweb.fr/datasets/incidence-PAY-7.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": [ "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": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202034725072594755417FRFrance
1202033713201772463204FRFrance
2202032726506894611417FRFrance
3202031713031002506204FRFrance
420203071385752695204FRFrance
52020297841101672102FRFrance
6202028772801515102FRFrance
720202779861491823102FRFrance
8202026769401454102FRFrance
920202572280597001FRFrance
1020202473880959102FRFrance
11202023755811115102FRFrance
1220202272770633001FRFrance
132020217602361168102FRFrance
142020207824201628102FRFrance
1520201973100753001FRFrance
162020187849981600102FRFrance
1720201772720658001FRFrance
182020167758781438102FRFrance
19202015719186753161315FRFrance
202020147387922275531639FRFrance
21202013773265236941611814FRFrance
222020127812357901045612816FRFrance
23202011710198756812828151119FRFrance
2420201079011669111331141018FRFrance
252020097136311054416718211626FRFrance
26202008710424770813140161220FRFrance
2720200778959657411344141018FRFrance
2820200679264692511603141018FRFrance
2920200578505631410696131016FRFrance
.................................
15211991267176081130423912312042FRFrance
15221991257161691070021638281838FRFrance
15231991247161711007122271281739FRFrance
1524199123711947767116223211329FRFrance
1525199122715452995320951271737FRFrance
1526199121714903897520831261636FRFrance
15271991207190531274225364342345FRFrance
15281991197167391124622232291939FRFrance
15291991187213851388228888382551FRFrance
1530199117713462887718047241632FRFrance
15311991167148571006819646261834FRFrance
1532199115713975978118169251832FRFrance
1533199114712265768416846221430FRFrance
153419911379567604113093171123FRFrance
1535199112710864733114397191325FRFrance
15361991117155741118419964271935FRFrance
15371991107166431137221914292038FRFrance
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15481990517190801380724353342543FRFrance
1549199050711079666015498201228FRFrance
15501990497114302610205FRFrance
\n", "

1551 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202034 7 2507 259 4755 4 1 \n", "1 202033 7 1320 177 2463 2 0 \n", "2 202032 7 2650 689 4611 4 1 \n", "3 202031 7 1303 100 2506 2 0 \n", "4 202030 7 1385 75 2695 2 0 \n", "5 202029 7 841 10 1672 1 0 \n", "6 202028 7 728 0 1515 1 0 \n", "7 202027 7 986 149 1823 1 0 \n", "8 202026 7 694 0 1454 1 0 \n", "9 202025 7 228 0 597 0 0 \n", "10 202024 7 388 0 959 1 0 \n", "11 202023 7 558 1 1115 1 0 \n", "12 202022 7 277 0 633 0 0 \n", "13 202021 7 602 36 1168 1 0 \n", "14 202020 7 824 20 1628 1 0 \n", "15 202019 7 310 0 753 0 0 \n", "16 202018 7 849 98 1600 1 0 \n", "17 202017 7 272 0 658 0 0 \n", "18 202016 7 758 78 1438 1 0 \n", "19 202015 7 1918 675 3161 3 1 \n", "20 202014 7 3879 2227 5531 6 3 \n", "21 202013 7 7326 5236 9416 11 8 \n", "22 202012 7 8123 5790 10456 12 8 \n", "23 202011 7 10198 7568 12828 15 11 \n", "24 202010 7 9011 6691 11331 14 10 \n", "25 202009 7 13631 10544 16718 21 16 \n", "26 202008 7 10424 7708 13140 16 12 \n", "27 202007 7 8959 6574 11344 14 10 \n", "28 202006 7 9264 6925 11603 14 10 \n", "29 202005 7 8505 6314 10696 13 10 \n", "... ... ... ... ... ... ... ... \n", "1521 199126 7 17608 11304 23912 31 20 \n", "1522 199125 7 16169 10700 21638 28 18 \n", "1523 199124 7 16171 10071 22271 28 17 \n", "1524 199123 7 11947 7671 16223 21 13 \n", "1525 199122 7 15452 9953 20951 27 17 \n", "1526 199121 7 14903 8975 20831 26 16 \n", "1527 199120 7 19053 12742 25364 34 23 \n", "1528 199119 7 16739 11246 22232 29 19 \n", "1529 199118 7 21385 13882 28888 38 25 \n", "1530 199117 7 13462 8877 18047 24 16 \n", "1531 199116 7 14857 10068 19646 26 18 \n", "1532 199115 7 13975 9781 18169 25 18 \n", "1533 199114 7 12265 7684 16846 22 14 \n", "1534 199113 7 9567 6041 13093 17 11 \n", "1535 199112 7 10864 7331 14397 19 13 \n", "1536 199111 7 15574 11184 19964 27 19 \n", "1537 199110 7 16643 11372 21914 29 20 \n", "1538 199109 7 13741 8780 18702 24 15 \n", "1539 199108 7 13289 8813 17765 23 15 \n", "1540 199107 7 12337 8077 16597 22 15 \n", "1541 199106 7 10877 7013 14741 19 12 \n", "1542 199105 7 10442 6544 14340 18 11 \n", "1543 199104 7 7913 4563 11263 14 8 \n", "1544 199103 7 15387 10484 20290 27 18 \n", "1545 199102 7 16277 11046 21508 29 20 \n", "1546 199101 7 15565 10271 20859 27 18 \n", "1547 199052 7 19375 13295 25455 34 23 \n", "1548 199051 7 19080 13807 24353 34 25 \n", "1549 199050 7 11079 6660 15498 20 12 \n", "1550 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 7 FR France \n", "1 4 FR France \n", "2 7 FR France \n", "3 4 FR France \n", "4 4 FR France \n", "5 2 FR France \n", "6 2 FR France \n", "7 2 FR France \n", "8 2 FR France \n", "9 1 FR France \n", "10 2 FR France \n", "11 2 FR France \n", "12 1 FR France \n", "13 2 FR France \n", "14 2 FR France \n", "15 1 FR France \n", "16 2 FR France \n", "17 1 FR France \n", "18 2 FR France \n", "19 5 FR France \n", "20 9 FR France \n", "21 14 FR France \n", "22 16 FR France \n", "23 19 FR France \n", "24 18 FR France \n", "25 26 FR France \n", "26 20 FR France \n", "27 18 FR France \n", "28 18 FR France \n", "29 16 FR France \n", "... ... ... ... \n", "1521 42 FR France \n", "1522 38 FR France \n", "1523 39 FR France \n", "1524 29 FR France \n", "1525 37 FR France \n", "1526 36 FR France \n", "1527 45 FR France \n", "1528 39 FR France \n", "1529 51 FR France \n", "1530 32 FR France \n", "1531 34 FR France \n", "1532 32 FR France \n", "1533 30 FR France \n", "1534 23 FR France \n", "1535 25 FR France \n", "1536 35 FR France \n", "1537 38 FR France \n", "1538 33 FR France \n", "1539 31 FR France \n", "1540 29 FR France \n", "1541 26 FR France \n", "1542 25 FR France \n", "1543 20 FR France \n", "1544 36 FR France \n", "1545 38 FR France \n", "1546 36 FR France \n", "1547 45 FR France \n", "1548 43 FR France \n", "1549 28 FR France \n", "1550 5 FR France \n", "\n", "[1551 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous n'observons pas de données mmanquantes sur ce document.\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": 10, "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 reste 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.\n" ] }, { "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", "Comme précédemment observé, il n'y a pas de données manquantes.\n" ] }, { "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": "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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle\n", "\n", "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 est très faible en septembre, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent en Décembre 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_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.\n" ] }, { "cell_type": "code", "execution_count": 17, "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": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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