{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 4, "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": 5, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.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": "markdown", "metadata": {}, "source": [ "Les données du réseau sentinelles sont récupérées dans un fichier local, puis les traitements sont effectués à partir de ce fichier." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1847 rows × 11 columns

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"1817 35.0 59.0 FR France \n", "1818 38.0 64.0 FR France \n", "1819 59.0 97.0 FR France \n", "1820 55.0 93.0 FR France \n", "1821 44.0 80.0 FR France \n", "1822 66.0 116.0 FR France \n", "1823 83.0 149.0 FR France \n", "1824 207.0 281.0 FR France \n", "1825 319.0 395.0 FR France \n", "1826 405.0 485.0 FR France \n", "1827 458.0 544.0 FR France \n", "1828 591.0 689.0 FR France \n", "1829 618.0 722.0 FR France \n", "1830 652.0 762.0 FR France \n", "1831 784.0 926.0 FR France \n", "1832 939.0 1113.0 FR France \n", "1833 1074.0 1236.0 FR France \n", "1834 708.0 832.0 FR France \n", "1835 317.0 459.0 FR France \n", "1836 147.0 207.0 FR France \n", "1837 120.0 190.0 FR France \n", "1838 110.0 198.0 FR France \n", "1839 146.0 224.0 FR France \n", "1840 184.0 266.0 FR France \n", "1841 149.0 219.0 FR France \n", "1842 110.0 176.0 FR France \n", "1843 99.0 163.0 FR France \n", "1844 123.0 195.0 FR France \n", "1845 184.0 308.0 FR France \n", "1846 37.0 213.0 FR France \n", "\n", "[1847 rows x 11 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pathlib import Path\n", "\n", "my_file = Path(\"./incidence_grippe.csv\")\n", "if my_file.is_file():\n", " # file exists\n", " raw_data = pd.read_csv(my_file, skiprows=0)\n", "else:\n", " raw_data = pd.read_csv(data_url, skiprows=1)\n", " raw_data.to_csv(my_file)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." ] }, { "cell_type": "code", "execution_count": 7, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
1610161019891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n", "1610 1610 198919 3 0 NaN NaN 0 NaN \n", "\n", " inc100_up geo_insee geo_name \n", "1610 NaN FR France " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1846 rows × 11 columns

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" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 \\\n", "0 0 202012 3 10125 7199.0 13051.0 15 \n", "1 1 202011 3 102048 93969.0 110127.0 155 \n", "2 2 202010 3 104977 96650.0 113304.0 159 \n", "3 3 202009 3 110696 102066.0 119326.0 168 \n", "4 4 202008 3 143753 133984.0 153522.0 218 \n", "5 5 202007 3 183610 172812.0 194408.0 279 \n", "6 6 202006 3 206669 195481.0 217857.0 314 \n", "7 7 202005 3 187957 177445.0 198469.0 285 \n", "8 8 202004 3 122331 113492.0 131170.0 186 \n", "9 9 202003 3 78413 71330.0 85496.0 119 \n", "10 10 202002 3 53614 47654.0 59574.0 81 \n", "11 11 202001 3 36850 31608.0 42092.0 56 \n", "12 12 201952 3 28135 23220.0 33050.0 43 \n", "13 13 201951 3 29786 25042.0 34530.0 45 \n", "14 14 201950 3 34223 29156.0 39290.0 52 \n", "15 15 201949 3 25662 21414.0 29910.0 39 \n", "16 16 201948 3 22367 18055.0 26679.0 34 \n", "17 17 201947 3 18669 14759.0 22579.0 28 \n", "18 18 201946 3 16030 12567.0 19493.0 24 \n", "19 19 201945 3 10138 7160.0 13116.0 15 \n", "20 20 201944 3 7822 5010.0 10634.0 12 \n", "21 21 201943 3 9487 6448.0 12526.0 14 \n", "22 22 201942 3 7747 5243.0 10251.0 12 \n", "23 23 201941 3 7122 4720.0 9524.0 11 \n", "24 24 201940 3 8505 5784.0 11226.0 13 \n", "25 25 201939 3 7091 4462.0 9720.0 11 \n", "26 26 201938 3 4897 2891.0 6903.0 7 \n", "27 27 201937 3 3172 1367.0 4977.0 5 \n", "28 28 201936 3 2295 728.0 3862.0 3 \n", "29 29 201935 3 1010 2.0 2018.0 2 \n", "... ... ... ... ... ... ... ... \n", "1817 1817 198521 3 26096 19621.0 32571.0 47 \n", "1818 1818 198520 3 27896 20885.0 34907.0 51 \n", "1819 1819 198519 3 43154 32821.0 53487.0 78 \n", "1820 1820 198518 3 40555 29935.0 51175.0 74 \n", "1821 1821 198517 3 34053 24366.0 43740.0 62 \n", "1822 1822 198516 3 50362 36451.0 64273.0 91 \n", "1823 1823 198515 3 63881 45538.0 82224.0 116 \n", "1824 1824 198514 3 134545 114400.0 154690.0 244 \n", "1825 1825 198513 3 197206 176080.0 218332.0 357 \n", "1826 1826 198512 3 245240 223304.0 267176.0 445 \n", "1827 1827 198511 3 276205 252399.0 300011.0 501 \n", "1828 1828 198510 3 353231 326279.0 380183.0 640 \n", "1829 1829 198509 3 369895 341109.0 398681.0 670 \n", "1830 1830 198508 3 389886 359529.0 420243.0 707 \n", "1831 1831 198507 3 471852 432599.0 511105.0 855 \n", "1832 1832 198506 3 565825 518011.0 613639.0 1026 \n", "1833 1833 198505 3 637302 592795.0 681809.0 1155 \n", "1834 1834 198504 3 424937 390794.0 459080.0 770 \n", "1835 1835 198503 3 213901 174689.0 253113.0 388 \n", "1836 1836 198502 3 97586 80949.0 114223.0 177 \n", "1837 1837 198501 3 85489 65918.0 105060.0 155 \n", "1838 1838 198452 3 84830 60602.0 109058.0 154 \n", "1839 1839 198451 3 101726 80242.0 123210.0 185 \n", "1840 1840 198450 3 123680 101401.0 145959.0 225 \n", "1841 1841 198449 3 101073 81684.0 120462.0 184 \n", "1842 1842 198448 3 78620 60634.0 96606.0 143 \n", "1843 1843 198447 3 72029 54274.0 89784.0 131 \n", "1844 1844 198446 3 87330 67686.0 106974.0 159 \n", "1845 1845 198445 3 135223 101414.0 169032.0 246 \n", "1846 1846 198444 3 68422 20056.0 116788.0 125 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 11.0 19.0 FR France \n", "1 143.0 167.0 FR France \n", "2 146.0 172.0 FR France \n", "3 155.0 181.0 FR France \n", "4 203.0 233.0 FR France \n", "5 263.0 295.0 FR France \n", "6 297.0 331.0 FR France \n", "7 269.0 301.0 FR France \n", "8 173.0 199.0 FR France \n", "9 108.0 130.0 FR France \n", "10 72.0 90.0 FR France \n", "11 48.0 64.0 FR France \n", "12 36.0 50.0 FR France \n", "13 38.0 52.0 FR France \n", "14 44.0 60.0 FR France \n", "15 33.0 45.0 FR France \n", "16 27.0 41.0 FR France \n", "17 22.0 34.0 FR France \n", "18 19.0 29.0 FR France \n", "19 10.0 20.0 FR France \n", "20 8.0 16.0 FR France \n", "21 9.0 19.0 FR France \n", "22 8.0 16.0 FR France \n", "23 7.0 15.0 FR France \n", "24 9.0 17.0 FR France \n", "25 7.0 15.0 FR France \n", "26 4.0 10.0 FR France \n", "27 2.0 8.0 FR France \n", "28 1.0 5.0 FR France \n", "29 0.0 4.0 FR France \n", "... ... ... ... ... \n", "1817 35.0 59.0 FR France \n", "1818 38.0 64.0 FR France \n", "1819 59.0 97.0 FR France \n", "1820 55.0 93.0 FR France \n", "1821 44.0 80.0 FR France \n", "1822 66.0 116.0 FR France \n", "1823 83.0 149.0 FR France \n", "1824 207.0 281.0 FR France \n", "1825 319.0 395.0 FR France \n", "1826 405.0 485.0 FR France \n", "1827 458.0 544.0 FR France \n", "1828 591.0 689.0 FR France \n", "1829 618.0 722.0 FR France \n", "1830 652.0 762.0 FR France \n", "1831 784.0 926.0 FR France \n", "1832 939.0 1113.0 FR France \n", "1833 1074.0 1236.0 FR France \n", "1834 708.0 832.0 FR France \n", "1835 317.0 459.0 FR France \n", "1836 147.0 207.0 FR France \n", "1837 120.0 190.0 FR France \n", "1838 110.0 198.0 FR France \n", "1839 146.0 224.0 FR France \n", "1840 184.0 266.0 FR France \n", "1841 149.0 219.0 FR France \n", "1842 110.0 176.0 FR France \n", "1843 99.0 163.0 FR France \n", "1844 123.0 195.0 FR France \n", "1845 184.0 308.0 FR France \n", "1846 37.0 213.0 FR France \n", "\n", "[1846 rows x 11 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "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": 9, "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": 10, "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 sauf pour deux périodes consécutives\n", "entre lesquelles il manque une semaine.\n", "\n", "Nous reconnaissons ces dates: c'est la semaine sans observations\n", "que nous avions supprimées !" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" ] } ], "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": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "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 hiver. Le creux des incidences se trouve en été." ] }, { "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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" ] }, "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 août de l'année $N$ au\n", "1er août 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 août de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er août.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1985." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er août, 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": 15, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_august_week[:-1],\n", " first_august_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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "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": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2012 2175217\n", "2003 2234584\n", "2019 2254386\n", "2006 2307352\n", "2017 2321583\n", "2001 2529279\n", "1992 2574578\n", "1993 2703886\n", "2018 2705325\n", "1988 2765617\n", "2007 2780164\n", "1987 2855570\n", "2016 2856393\n", "2011 2857040\n", "2008 2973918\n", "1998 3034904\n", "2002 3125418\n", "2009 3444020\n", "1994 3514763\n", "1996 3539413\n", "2004 3567744\n", "1997 3620066\n", "2015 3654892\n", "2000 3826372\n", "2005 3835025\n", "1999 3908112\n", "2010 4111392\n", "2013 4182691\n", "1986 5115251\n", "1990 5235827\n", "1989 5466192\n", "dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", " française, sont assez rares: il y en eu trois au cours des 35 dernières années." ] }, { "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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d+cp0xdwBPK3t/qHAnb2NY2Zm3SpT2K8HDpf0DEl7A68D/rE/sczMrKqOu2IiYo+kdwJfAZYAn4mIm7uY94LdNTVqcjZodj5nq67J+ZytukXP1/HBUzMze2LwladmZplxYTczy4wLu5lZZp6QhV3Sakmr684xG0nPlPQeSSfUnWWmJmeDZudztuqanK/J2aB6vidUYZe0RtLVwBXAxyW9pO5M7ST9R+BrpO/SeZukt9cc6ReanA2anc/ZqmtyviZngy7zRUSj/4B9226/FvhEcfvNwD8AQ8V91ZDtBOAZ0/MHPgCcVtx/EfAlYLiOfE3O1vR8zpZnviZn63W+Rm6xSzpA0l9JuhX4hKTDikG/A/youD0G/BA4Y/ppi5jvKEnfA/4b8FlJJ0Rq7aOAQwAi4jrgm8BbFjNfk7M1PZ+z5Zmvydn6la+RhR04EdiXtGCPAB+QtJy0W/JqgIh4GLgYeElx/7F+hZF0qKQD2h46BbgkIl5K+oB5g6TDgQun8xUuA46WtE+/8jU5W9PzOVue+ZqcbbHy1VbYlSyV9FZJX5d0lqRnFYOfDTwSEXuAPwN+CpwGfBV4iqRfK8a7FfixpOP6lPFISV8GvgF8WNL01xT/P2C/4vYXgbuAdaRP1Ce37WHcR/p2y+f9KmVrej5nyzNfk7Mtdr7aCnuxq/GbwJuAjwH7AH9TDL4LuLv4ZPoxaWGeRWqA7/P41wIvA+4tHu8JSSva7j4fuCMi1gBXAp8oHr8PeFjS/hFxH/CvwFOLHN8E3luMtzfwc2B77tmans/Z8szX5Gx15lu0wi7pOEkflXR6cV/AkcAVEfGliPgYcJikFwM7SJ9gRxZP3wYMFI/9BfAqSa8mfSgMAt/tMtuTJJ0v6Xpgo6SDinxDwLWSFBH/CNwvaR1pT2H/YjjF/YOBx0h7GAdL+hvgImBPRNydY7am53O26pqcr8nZmpJvUQq7pOcAfwk8APyepPcW814NPFAsNMD5wBtIhXoP8OLi8RtIR4wfiohrgA3A6cDxwH+PiMfaplHFS4v5vYp0UOJs4ADSl50dUuxdAFxQ5Pt2sSyvBIiIbxXTWBoR24AzgZuB/xkRb6E7Tc7W9HzOlme+JmdrRr65Tpep+kfasj6DtNuxtHjsT4Gzitst4JPAycDLga+0PfdppF0VSIX8RtKvMB0D/G/gKW3jlj4dqWjYM4GrSd05q4rHvwi8u7j9DGBjMfxYUn/YkrZl+0kxndWkPYl3Ap8FPgWs6KLdGput6fmcza+r2+7f//V0i13S80kHOH8b+CDwvmLQDtJvpkL65LkW+F3gn4FDJD1X0rJI/ek7JL0kIq4kfd3lR4FLgYsi4t+m5xVFy5R0EvAa4EPAcaS+fUhn20zvHfwY+Drwyoi4nvSJO1LMcwq4Djg2InYAbyR1Bd0FvC8iHiwbqG1P49VNyzaD266axrUbuO26yfZEaLsyP433SyS9EDgc+GpE/IS0NX5rRJwu6QXAOZJawDjwnyTtFxEPSfou8HukczQvBP4A+KSk3cAkcHsxi78CLoyIXSUyKSJC0rGk3ZyvA1sinR7568BtEXGlpNtJV6++AtgK/I6kVRFxj6R/BR6U9HTgz4HTJB1M+tWoe0m7TkTEBDBRod1apL2aB4CPA3cDz6w7m9uuWrYnQru57fJru/mU2mJXskzSmyTdSOrYPxCYLrw/B7YXW983kHYtjgMe4vFTeAAeJe2CHELaKr+J1L9+NXBPRNwBaau8YlF/KfAZ0lHllwMfKUZ5DLhV0vKIuL3I91zSi3Un6XzS6eVYQmqfS4qMpwJrgc1R8RxXSSslfbaY5u3AuRFxt6S9SJ/kdWZbUrTdb5J2BRvTdsV6NyDpfBrWdsU8Q9IwzVzn9pG0oqFtd0DD225A0r6SLqBhbbegTvprgBXAi4vbBxbBPjnLeGeRLoNdXdw/mdSffhjpKwCuLh7fl9QNs6rtuccAe3eSZ8Y89wPexuNb/suAPwLeUQx/EvC9YvqnkPq71hTDTiqWZVVxexJYSerf/3J7HmCvLrJdRLpibIDUtXRm2zjTxyHeCfyPxcrW9rqeQVrZ1pMO8DSl7aazXVqsVwc1rO32B7aQfkkM4D1NaLcZ+b4M/HVx/2PA2+puO9J74s2k9/8lTWu7tnxXAn9fPNaY9a7TvwW32CWdDdwGbJE0GBH3k/qF7iz6xl+jxy8Q+hbpAOj0hUbXkg6iPhQRFwA/lfQ50kHRW4Bf9CFFxI0R8chCeWZkOwS4HBgGPkc6QPFa0l7CnmK6PyUdeH03qe/rYB4/jfIa0rn0j0TE5cCnSVeznkc6Yv1oW75Sn6ozsv0d8PYi263AEZI2FltRv690wdUVpD2Yvmcr8q0gvblOIF0/8ArScY9jSVtKdbZde7bNpLMFXku6huE36m67wnLStRfPkrSKtM4vKaZZS7vNkm9v0rr2VFIXx9GSPlJX20laRjrGdjLw8Yj43WLQMW3TrK3tZuT7WERMb3FPAkfV2XaldfAJNkzavfhb4D3FY8eSitYdRfALgU3FsHOAD7c9/3rgmOL2PqRTgI7txacSaeV9Udv900lbJm8Gvt32+FOBO4vb7yBdtvuk4vlfAp7eNu6qPmV7E+lI968Df1/8vR74X6Rz+RctW9v0Dmy7/Z9Jb6ZT6267WbL9MemUsWc2qO3eTOprfT/wVtKBtOvrbrdZ8r2PtMezqgltR9oDO3XGY6cA1zWh7ebI9/QiQ+3rXcfL0cGCTp+acwowXtxeRtqaWlncP4y0tX4saRfwYtKW1j+RPqn26Uv41Mcl+MVvt76Ax7t77iWdMzo97tcoCi1p9+mrxTh/skjZjgG+Mb3ito23jHRw+YTi/jn9zjYj5wGk4xs7gQ8X9+8FButqu1my3VXMdwVFN19dbdf2er6F1M32WuALxWP31N1uc+QbKx5rP124lvWO1EVxK7CpmP8HivpxH3BwA9a59nxXkb6Y69C617vSy1FigZ9MulDoOcX9pTOGnw+cPL0CkboezqRPRX2OlfkCHj9f/nPAR4vbv0ba43h62wtzNG1fCbxI2d7R/lhx+5Ci7Z672NnaMvwh6XzbzaR+7W8WbzjV2XYzsp1HOq3s2U1oO9JXRi8h9aFeTdoyvgl4f93r3Cz5/pl0htkLGtJ2XyHtgT2NtBV8FmnDsCnrXHu+L5Au/T+8CW3X6d900emIpE8BP4uIDcX9vUjnXb4DeA5wSpTsJ+8VSYeS+rTeFRG3Kn2h2GiRazXwnejNVWXdZHt7RNxWPHYMqVtqXZHtD+vI1k7pOoQzSW+yI0kr66HU2HZt2Y4mvdn+nHSW1UnU1HaSBkjdHPuQ2uk3SBeenE3aUj6cGtttlnyHk45P/BbpmNfLSO1Xy3qn4rTn4vbzSO/Ta0mX1Ne+zs3IdzTpSvdzSd80W9t6V0bZ89g3A+cWBxmOJK3Ex5NelLPrKuqFYyjOgZd0Bqn//2xSF9IPIp1+WXe2HxXZbietHHtIW/E31pit3b2kg4Dvi4i/k3QacHND8t1P6ie+ifS6LqO+tttDOnviUdKW+s9J6/8k8N4GtNtc+R6W9BpSwa9tvZsumoX7Sced3h8RFzag7Wbme4C08boN+K/Uu951rOwW++tIB0ofJn3j2JURcUufspUi6VrSwbXtpHNIPxQR36s1VGFGtruADQ1qt5WkLbg3kL7/fjNwXkQ8Ou8TF8Es2T4dEZvqTfXLigtPpvuy76o7z0xFvpOBz0Y666TuPPuQfnPhjaQ96r8EPhXpa7prN0u+zRHxZ/WmKqfjwi7puaTzOS8mHSzq2VfldqvYg/ggaUv485GuWmuEJmcDkLSU1P3yMClfk17XxmaDdFEX8FiU2TpaRE3OJ+lM0mm1n2va6wrNz7eQUlvsZmbWfE39aTwzM6vIhd3MLDMu7GZmmXFhNzPLjAu7mVlmXNjNzDLjwm5mlpn/D0QBdzhJVkBDAAAAAElFTkSuQmCC\n", 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