{ "cells": [ { "cell_type": "code", "execution_count": 11, "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, qui commence en 1984 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "import urllib.request\n", "import os\n", "\n", "data_url = \"http://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"\n", "data = \"data.csv\"\n", "\n", "if not os.path.exists(data):\n", " urllib.request.urlretrieve(data_url, \"data.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": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1920242979270630312237141018FRFrance
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2720242179701685112551151119FRFrance
282024207136611020917113201525FRFrance
2920241971008364131375315921FRFrance
.................................
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\n", "

1774 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202448 7 4218 1335 7101 6 2 \n", "1 202447 7 1965 738 3192 3 1 \n", "2 202446 7 2260 863 3657 3 1 \n", "3 202445 7 2713 1216 4210 4 2 \n", "4 202444 7 2135 676 3594 3 1 \n", "5 202443 7 2124 641 3607 3 1 \n", "6 202442 7 2621 1246 3996 4 2 \n", "7 202441 7 2035 381 3689 3 1 \n", "8 202440 7 2125 725 3525 3 1 \n", "9 202439 7 2898 1333 4463 4 2 \n", "10 202438 7 751 0 1513 1 0 \n", "11 202437 7 916 28 1804 1 0 \n", "12 202436 7 2235 870 3600 3 1 \n", "13 202435 7 1620 285 2955 2 0 \n", "14 202434 7 2560 622 4498 4 1 \n", "15 202433 7 1971 536 3406 3 1 \n", "16 202432 7 4399 1944 6854 7 3 \n", "17 202431 7 4500 2213 6787 7 4 \n", "18 202430 7 7004 4278 9730 11 7 \n", "19 202429 7 9270 6303 12237 14 10 \n", "20 202428 7 9364 6498 12230 14 10 \n", "21 202427 7 10247 7090 13404 15 10 \n", "22 202426 7 14368 10399 18337 22 16 \n", "23 202425 7 11174 8039 14309 17 12 \n", "24 202424 7 12621 9357 15885 19 14 \n", "25 202423 7 14657 11339 17975 22 17 \n", "26 202422 7 11628 8361 14895 17 12 \n", "27 202421 7 9701 6851 12551 15 11 \n", "28 202420 7 13661 10209 17113 20 15 \n", "29 202419 7 10083 6413 13753 15 9 \n", "... ... ... ... ... ... ... ... \n", "1744 199126 7 17608 11304 23912 31 20 \n", "1745 199125 7 16169 10700 21638 28 18 \n", "1746 199124 7 16171 10071 22271 28 17 \n", "1747 199123 7 11947 7671 16223 21 13 \n", "1748 199122 7 15452 9953 20951 27 17 \n", "1749 199121 7 14903 8975 20831 26 16 \n", "1750 199120 7 19053 12742 25364 34 23 \n", "1751 199119 7 16739 11246 22232 29 19 \n", "1752 199118 7 21385 13882 28888 38 25 \n", "1753 199117 7 13462 8877 18047 24 16 \n", "1754 199116 7 14857 10068 19646 26 18 \n", "1755 199115 7 13975 9781 18169 25 18 \n", "1756 199114 7 12265 7684 16846 22 14 \n", "1757 199113 7 9567 6041 13093 17 11 \n", "1758 199112 7 10864 7331 14397 19 13 \n", "1759 199111 7 15574 11184 19964 27 19 \n", "1760 199110 7 16643 11372 21914 29 20 \n", "1761 199109 7 13741 8780 18702 24 15 \n", "1762 199108 7 13289 8813 17765 23 15 \n", "1763 199107 7 12337 8077 16597 22 15 \n", "1764 199106 7 10877 7013 14741 19 12 \n", "1765 199105 7 10442 6544 14340 18 11 \n", "1766 199104 7 7913 4563 11263 14 8 \n", "1767 199103 7 15387 10484 20290 27 18 \n", "1768 199102 7 16277 11046 21508 29 20 \n", "1769 199101 7 15565 10271 20859 27 18 \n", "1770 199052 7 19375 13295 25455 34 23 \n", "1771 199051 7 19080 13807 24353 34 25 \n", "1772 199050 7 11079 6660 15498 20 12 \n", "1773 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 10 FR France \n", "1 5 FR France \n", "2 5 FR France \n", "3 6 FR France \n", "4 5 FR France \n", "5 5 FR France \n", "6 6 FR France \n", "7 5 FR France \n", "8 5 FR France \n", "9 6 FR France \n", "10 2 FR France \n", "11 2 FR France \n", "12 5 FR France \n", "13 4 FR France \n", "14 7 FR France \n", "15 5 FR France \n", "16 11 FR France \n", "17 10 FR France \n", "18 15 FR France \n", "19 18 FR France \n", "20 18 FR France \n", "21 20 FR France \n", "22 28 FR France \n", "23 22 FR France \n", "24 24 FR France \n", "25 27 FR France \n", "26 22 FR France \n", "27 19 FR France \n", "28 25 FR France \n", "29 21 FR France \n", "... ... ... ... \n", "1744 42 FR France \n", "1745 38 FR France \n", "1746 39 FR France \n", "1747 29 FR France \n", "1748 37 FR France \n", "1749 36 FR France \n", "1750 45 FR France \n", "1751 39 FR France \n", "1752 51 FR France \n", "1753 32 FR France \n", "1754 34 FR France \n", "1755 32 FR France \n", "1756 30 FR France \n", "1757 23 FR France \n", "1758 25 FR France \n", "1759 35 FR France \n", "1760 38 FR France \n", "1761 33 FR France \n", "1762 31 FR France \n", "1763 29 FR France \n", "1764 26 FR France \n", "1765 25 FR France \n", "1766 20 FR France \n", "1767 36 FR France \n", "1768 38 FR France \n", "1769 36 FR France \n", "1770 45 FR France \n", "1771 43 FR France \n", "1772 28 FR France \n", "1773 5 FR France \n", "\n", "[1774 rows x 10 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data, 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": 14, "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": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "data = raw_data.dropna().copy()\n", "data\n", "\n", "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": 23, "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.\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": 24, "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": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "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 printemps. Le creux des incidences se trouve en automne." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "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\n", "\n", "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", "Comme l'incidence de syndrome grippal est très faible en automne, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent an decembre 1990, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse à partir du 1er septembre 1991." ] }, { "cell_type": "code", "execution_count": 35, "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": "code", "execution_count": 36, "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, (print(len(one_year), week1, week2, \"\\n\", one_year))\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": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 39, "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": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "2023 366227\n", "2021 376290\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", "2022 641397\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": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()\n" ] }, { "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": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 41, "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.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 }