From 756a014f8ba477c3823ec816ff3b24971fa83614 Mon Sep 17 00:00:00 2001 From: 0db2f0554d3b3bbdf0f34a0c1240bdef <0db2f0554d3b3bbdf0f34a0c1240bdef@app-learninglab.inria.fr> Date: Fri, 12 Jun 2020 15:30:48 +0000 Subject: [PATCH] =?UTF-8?q?Traitement=20complet=20de=20l'incidence=20de=20?= =?UTF-8?q?la=20varicelle=20pour=20obtenir=20ann=C3=A9e=20max=20et=20ann?= =?UTF-8?q?=C3=A9e=20min?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../exo2/ analyse-incidence-varicelle.ipynb | 2385 ++++++++++++++++- module3/exo2/incidence-varicelle.csv | 1542 +++++++++++ 2 files changed, 3923 insertions(+), 4 deletions(-) create mode 100644 module3/exo2/incidence-varicelle.csv diff --git a/module3/exo2/ analyse-incidence-varicelle.ipynb b/module3/exo2/ analyse-incidence-varicelle.ipynb index 9690657..359d0a3 100644 --- a/module3/exo2/ analyse-incidence-varicelle.ipynb +++ b/module3/exo2/ analyse-incidence-varicelle.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -25,14 +25,1010 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Les données de l'incidence du syndrome grippal sont disponibles du site Web du Réseau Sentinelles. Nous les récupérons sous forme d'un fichier en format CSV" + "Les données de l'incidence du syndrome grippal sont disponibles du site Web du Réseau Sentinelles. 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 1991 et se termine avec une semaine récente." + ] + }, + { + "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": [ + "Vérifions comme pour la grippe qu'une copie locale des données existe dans le répertoire de travail sinon nous la téléchargeons." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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1540 rows × 10 columns

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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" + ], + "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": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il n'y a pas de ligne sans données dans ce jeux de données, mais dans le cas où les données seraient manquantes dans les années ultérieures, nous otons les lignes sans donnée valide.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1540 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202023 7 842 0 1690 1 0 \n", + "1 202022 7 280 0 638 0 0 \n", + "2 202021 7 602 36 1168 1 0 \n", + "3 202020 7 824 20 1628 1 0 \n", + "4 202019 7 310 0 753 0 0 \n", + "5 202018 7 849 98 1600 1 0 \n", + "6 202017 7 272 0 658 0 0 \n", + "7 202016 7 758 78 1438 1 0 \n", + "8 202015 7 1918 675 3161 3 1 \n", + "9 202014 7 3879 2227 5531 6 3 \n", + "10 202013 7 7326 5236 9416 11 8 \n", + "11 202012 7 8123 5790 10456 12 8 \n", + "12 202011 7 10198 7568 12828 15 11 \n", + "13 202010 7 9011 6691 11331 14 10 \n", + "14 202009 7 13631 10544 16718 21 16 \n", + "15 202008 7 10424 7708 13140 16 12 \n", + "16 202007 7 8959 6574 11344 14 10 \n", + "17 202006 7 9264 6925 11603 14 10 \n", + "18 202005 7 8505 6314 10696 13 10 \n", + "19 202004 7 7991 5831 10151 12 9 \n", + "20 202003 7 5968 4100 7836 9 6 \n", + "21 202002 7 6534 4530 8538 10 7 \n", + "22 202001 7 9835 7019 12651 15 11 \n", + "23 201952 7 7941 5246 10636 12 8 \n", + "24 201951 7 5823 3675 7971 9 6 \n", + "25 201950 7 6424 4276 8572 10 7 \n", + "26 201949 7 6621 4540 8702 10 7 \n", + "27 201948 7 5542 3383 7701 8 5 \n", + "28 201947 7 7536 5058 10014 11 7 \n", + "29 201946 7 2638 1316 3960 4 2 \n", + "... ... ... ... ... ... ... ... \n", + "1510 199126 7 17608 11304 23912 31 20 \n", + "1511 199125 7 16169 10700 21638 28 18 \n", + "1512 199124 7 16171 10071 22271 28 17 \n", + "1513 199123 7 11947 7671 16223 21 13 \n", + "1514 199122 7 15452 9953 20951 27 17 \n", + "1515 199121 7 14903 8975 20831 26 16 \n", + "1516 199120 7 19053 12742 25364 34 23 \n", + "1517 199119 7 16739 11246 22232 29 19 \n", + "1518 199118 7 21385 13882 28888 38 25 \n", + "1519 199117 7 13462 8877 18047 24 16 \n", + "1520 199116 7 14857 10068 19646 26 18 \n", + "1521 199115 7 13975 9781 18169 25 18 \n", + "1522 199114 7 12265 7684 16846 22 14 \n", + "1523 199113 7 9567 6041 13093 17 11 \n", + "1524 199112 7 10864 7331 14397 19 13 \n", + "1525 199111 7 15574 11184 19964 27 19 \n", + "1526 199110 7 16643 11372 21914 29 20 \n", + "1527 199109 7 13741 8780 18702 24 15 \n", + "1528 199108 7 13289 8813 17765 23 15 \n", + "1529 199107 7 12337 8077 16597 22 15 \n", + "1530 199106 7 10877 7013 14741 19 12 \n", + "1531 199105 7 10442 6544 14340 18 11 \n", + "1532 199104 7 7913 4563 11263 14 8 \n", + "1533 199103 7 15387 10484 20290 27 18 \n", + "1534 199102 7 16277 11046 21508 29 20 \n", + "1535 199101 7 15565 10271 20859 27 18 \n", + "1536 199052 7 19375 13295 25455 34 23 \n", + "1537 199051 7 19080 13807 24353 34 25 \n", + "1538 199050 7 11079 6660 15498 20 12 \n", + "1539 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 2 FR France \n", + "1 1 FR France \n", + "2 2 FR France \n", + "3 2 FR France \n", + "4 1 FR France \n", + "5 2 FR France \n", + "6 1 FR France \n", + "7 2 FR France \n", + "8 5 FR France \n", + "9 9 FR France \n", + "10 14 FR France \n", + "11 16 FR France \n", + "12 19 FR France \n", + "13 18 FR France \n", + "14 26 FR France \n", + "15 20 FR France \n", + "16 18 FR France \n", + "17 18 FR France \n", + "18 16 FR France \n", + "19 15 FR France \n", + "20 12 FR France \n", + "21 13 FR France \n", + "22 19 FR France \n", + "23 16 FR France \n", + "24 12 FR France \n", + "25 13 FR France \n", + "26 13 FR France \n", + "27 11 FR France \n", + "28 15 FR France \n", + "29 6 FR France \n", + "... ... ... ... \n", + "1510 42 FR France \n", + "1511 38 FR France \n", + "1512 39 FR France \n", + "1513 29 FR France \n", + "1514 37 FR France \n", + "1515 36 FR France \n", + "1516 45 FR France \n", + "1517 39 FR France \n", + "1518 51 FR France \n", + "1519 32 FR France \n", + "1520 34 FR France \n", + "1521 32 FR France \n", + "1522 30 FR France \n", + "1523 23 FR France \n", + "1524 25 FR France \n", + "1525 35 FR France \n", + "1526 38 FR France \n", + "1527 33 FR France \n", + "1528 31 FR France \n", + "1529 29 FR France \n", + "1530 26 FR France \n", + "1531 25 FR France \n", + "1532 20 FR France \n", + "1533 36 FR France \n", + "1534 38 FR France \n", + "1535 36 FR France \n", + "1536 45 FR France \n", + "1537 43 FR France \n", + "1538 28 FR France \n", + "1539 5 FR France \n", + "\n", + "[1540 rows x 10 columns]" + ] + }, + "execution_count": 4, + "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 semaine est collé à l'année, donnant l'impression qu'il s'agit 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 semaine. Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque isoweek.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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": 6, + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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": [ + "D'après le test ci-dessus, il n'y a pas de \"saut\" dans le jeux de données" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Portons un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "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'][-100:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On constate que le creux de cas de varicelle est en septembre." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Etant donné que l'épidémie se situe à cheval 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 faible en août et très faible en septembre, cette\n", + "modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent avec la 49 semaine de l'année 1990, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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": 12, + "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": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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"2009 842373\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/module3/exo2/incidence-varicelle.csv b/module3/exo2/incidence-varicelle.csv new file mode 100644 index 0000000..a7875bb --- /dev/null +++ b/module3/exo2/incidence-varicelle.csv @@ -0,0 +1,1542 @@ +# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[199049,202023],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2020-06-12T17:01:32+02:00 +week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name +202023,7,842,0,1690,1,0,2,FR,France +202022,7,280,0,638,0,0,1,FR,France +202021,7,602,36,1168,1,0,2,FR,France +202020,7,824,20,1628,1,0,2,FR,France 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