{ "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\n", "from os import path as pth" ] }, { "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/). Par soucis d'efficacité et de pérennité, nous utilisons une copie locale de ces données. Dans le cas où ce fichier n'existerait pas ou plus, nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Ces données sont ensuite sauvegardées à l'emplacement défini pour pouvoir être réutilisées par la suite." ] }, { "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": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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.................................
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\n", "

1590 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202120 7 8003 4590 11416 12 7 \n", "1 202119 7 6654 4370 8938 10 7 \n", "2 202118 7 3912 2110 5714 6 3 \n", "3 202117 7 4686 2878 6494 7 4 \n", "4 202116 7 4780 2891 6669 7 4 \n", "5 202115 7 11215 7627 14803 17 12 \n", "6 202114 7 11197 7994 14400 17 12 \n", "7 202113 7 9714 6289 13139 15 10 \n", "8 202112 7 11520 8415 14625 17 12 \n", "9 202111 7 9386 6678 12094 14 10 \n", "10 202110 7 9056 6452 11660 14 10 \n", "11 202109 7 10988 7938 14038 17 12 \n", "12 202108 7 11281 8361 14201 17 13 \n", "13 202107 7 13561 10315 16807 21 16 \n", "14 202106 7 13401 9810 16992 20 15 \n", "15 202105 7 12210 8988 15432 18 13 \n", "16 202104 7 12026 8826 15226 18 13 \n", "17 202103 7 8913 6375 11451 13 9 \n", "18 202102 7 7795 5430 10160 12 8 \n", "19 202101 7 10525 7750 13300 16 12 \n", "20 202053 7 11978 8406 15550 18 13 \n", "21 202052 7 12012 8285 15739 18 12 \n", "22 202051 7 10564 7574 13554 16 11 \n", "23 202050 7 7063 4744 9382 11 7 \n", "24 202049 7 5026 3145 6907 8 5 \n", "25 202048 7 6683 4312 9054 10 6 \n", "26 202047 7 4999 2963 7035 8 5 \n", "27 202046 7 3752 1963 5541 6 3 \n", "28 202045 7 3696 2016 5376 6 3 \n", "29 202044 7 4391 2375 6407 7 4 \n", "... ... ... ... ... ... ... ... \n", "1560 199126 7 17608 11304 23912 31 20 \n", "1561 199125 7 16169 10700 21638 28 18 \n", "1562 199124 7 16171 10071 22271 28 17 \n", "1563 199123 7 11947 7671 16223 21 13 \n", "1564 199122 7 15452 9953 20951 27 17 \n", "1565 199121 7 14903 8975 20831 26 16 \n", "1566 199120 7 19053 12742 25364 34 23 \n", "1567 199119 7 16739 11246 22232 29 19 \n", "1568 199118 7 21385 13882 28888 38 25 \n", "1569 199117 7 13462 8877 18047 24 16 \n", "1570 199116 7 14857 10068 19646 26 18 \n", "1571 199115 7 13975 9781 18169 25 18 \n", "1572 199114 7 12265 7684 16846 22 14 \n", "1573 199113 7 9567 6041 13093 17 11 \n", "1574 199112 7 10864 7331 14397 19 13 \n", "1575 199111 7 15574 11184 19964 27 19 \n", "1576 199110 7 16643 11372 21914 29 20 \n", "1577 199109 7 13741 8780 18702 24 15 \n", "1578 199108 7 13289 8813 17765 23 15 \n", "1579 199107 7 12337 8077 16597 22 15 \n", "1580 199106 7 10877 7013 14741 19 12 \n", "1581 199105 7 10442 6544 14340 18 11 \n", "1582 199104 7 7913 4563 11263 14 8 \n", "1583 199103 7 15387 10484 20290 27 18 \n", "1584 199102 7 16277 11046 21508 29 20 \n", "1585 199101 7 15565 10271 20859 27 18 \n", "1586 199052 7 19375 13295 25455 34 23 \n", "1587 199051 7 19080 13807 24353 34 25 \n", "1588 199050 7 11079 6660 15498 20 12 \n", "1589 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 17 FR France \n", "1 13 FR France \n", "2 9 FR France \n", "3 10 FR France \n", "4 10 FR France \n", "5 22 FR France \n", "6 22 FR France \n", "7 20 FR France \n", "8 22 FR France \n", "9 18 FR France \n", "10 18 FR France \n", "11 22 FR France \n", "12 21 FR France \n", "13 26 FR France \n", "14 25 FR France \n", "15 23 FR France \n", "16 23 FR France \n", "17 17 FR France \n", "18 16 FR France \n", "19 20 FR France \n", "20 23 FR France \n", "21 24 FR France \n", "22 21 FR France \n", "23 15 FR France \n", "24 11 FR France \n", "25 14 FR France \n", "26 11 FR France \n", "27 9 FR France \n", "28 9 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1560 42 FR France \n", "1561 38 FR France \n", "1562 39 FR France \n", "1563 29 FR France \n", "1564 37 FR France \n", "1565 36 FR France \n", "1566 45 FR France \n", "1567 39 FR France \n", "1568 51 FR France \n", "1569 32 FR France \n", "1570 34 FR France \n", "1571 32 FR France \n", "1572 30 FR France \n", "1573 23 FR France \n", "1574 25 FR France \n", "1575 35 FR France \n", "1576 38 FR France \n", "1577 33 FR France \n", "1578 31 FR France \n", "1579 29 FR France \n", "1580 26 FR France \n", "1581 25 FR France \n", "1582 20 FR France \n", "1583 36 FR France \n", "1584 38 FR France \n", "1585 36 FR France \n", "1586 45 FR France \n", "1587 43 FR France \n", "1588 28 FR France \n", "1589 5 FR France \n", "\n", "[1590 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file_path='DonneesGrippeMOOC.csv'\n", "\n", "if pth.isfile(file_path)==0:\n", " raw_data = pd.read_csv(data_url, skiprows=1)\n", " df=pd.DataFrame(raw_data,columns=['week','indicator','inc','inc_low','inc_up','inc100','inc100_low','inc100_up','geo_insee','geo_name'])\n", " df.to_csv(file_path,index=False)\n", "\n", "raw_data = pd.read_csv(file_path)\n", "\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": 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": [ "Nous utiliserons donc l'ensemble des données disponibles pour la suite de l'étude" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "data=raw_data" ] }, { "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": 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", "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": 8, "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 pour l'ensemble des données." ] }, { "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": [ "Un premier regard sur 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", 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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 creux en été. Une réduction de l'amplitude et de la largeur du pic de 2020 et 2021 pourraient être attribués aux effets des confinements liés à la COVID-19." ] }, { "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": [ "## 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 septembre de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er septembre.\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": 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": [ "Voici 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": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre bien que la varicelle est une maladie touchant la population de manière chronique, sans vraiment présenter de forte épidémies se démarquant de la tendance moyenne. Les effets du confinement liés à la COVID-19 sont bien visibles puisque l'année 2020 voit l'incidence de la varicelle être réduite d'environ un facteur 3 vis à vis de l'incidence moyenne mesurée sur les 30 dernières années. " ] }, { "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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j8DLg2cA1Vepvl/tR4JSdznshcHWVPmj29v66nZ7ZEz+5Sg80Af2AWacvotmgPrdKD+0yLwf+sJ1+GfCa9rFU5jm9lL8uY96/AL7WHrgOzcuQYzPzYpo9wEMAMvNW4NqIODYzzwe+D3yI5seLr8rMm2ZuMDNv77DcZZWZd0TEARHxPpphk3U0K+/IiBjtcw+z7vu9gP+g2Xs9PjMvB47oe/07uQg4KyLOi4ipiHgT8FWa30g9uK2t733cDvxeRJwCbImIi2n29n67fUXU6x4y81fZvIk3M2YfNF+0dDnNeijxnAb+FTgtIj4CvBl4LPBtmh4Oauvqew+Lt4Ct3MwexsXAGe30+4G/bacfDFxIuyWjGY96NPDAYW+hdurjlTQvDzfTjIF/GXgTzQO41z0AH6HZa1pLc7zqG2gerG8stg4+RfNhiMNpnkhnAF8ptB4e0db7DzR7cS8B3k5zHPDraTayve5hp36+AZzcTl9S6TndPocvotkZezNwFnB9+/gqtR4W+tf5aJPMzIg4DDiEZotHe2cREVfQjCmvynZLlpn3Zua3M/MXXZexEjLzgmzebb6AZpzyn2heQn2cHvcQESM0e3zvBj5J8075scCLgQMj4nJ6XP9OnpeZb83MHwFvpTmM610UWA+tm2g+Dr06m3HVK9rzrqB5Q7ZCD0TEzPP/Kpo3+wDObi4q83g6CpjKZq/5vTQ7Nx+kzmNp8Ra4lXs2zRe07E2zxXsGzZ31EuDxw94SLbCXw4FPAw9pT78UOHrYde2m3gfSvOq5kOaNpgng07Mu73X9u+nrYTRvJj24Uh80H4/e0k4/iOYVxNGVemhr3Zdmw3nyTuef0vce2uz5M+DC9vQ6mh3LI6qth8X8LejHGCLiS8BvAjfSHBv5lsz8VucbGLL2KIETaDY2R9EMnZyfmffu9oo91H6I4GRgMjN/POx6FiIiHkCz4T8V+B2aw7wuyMztQy1sgSLibJrnw9E0OwJnZbMnXkpEXA+8KTM/NPM5jmHX1FVEHEnzPL6HZl38C/A32Xwico/WObzbw7zOAm4ALsn2DY9KImI1zfcb/JKmh3IvnSJiFXBfpSfYrkTEK2gO03x/xfUwIyIeCfywYg+zPnD3OJo3wbdXfFy1OzKPAL6cmXcPu56V0tufQZMkzW3ov6QjSVo4w1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jamg/wNI6CKPSu0/QAAAAABJRU5ErkJggg==\n", 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