diff --git a/module3/exo1/analyse-syndrome-varicelle_fr-Copy1.ipynb b/module3/exo1/analyse-syndrome-varicelle_fr-Copy1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a540eecb2d6fbb9f28f9d0355abd01f50f19971c --- /dev/null +++ b/module3/exo1/analyse-syndrome-varicelle_fr-Copy1.ipynb @@ -0,0 +1,2487 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "hideCode": true + }, + "source": [ + "# Incidence de la varicelle" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "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": "markdown", + "metadata": {}, + "source": [ + "Les données ont été téléchargées sur le site du réseau sentinelles à l'[adresse suivante](http://www.sentiweb.fr/datasets/incidence-PAY-3.csv) et stockée sur le dossier gitlab disponible via ce [lien](https://app-learninglab.inria.fr/moocrr/gitlab/b2c48a7ab4afbff5f4d26650b09eb6b4/mooc-rr/blob/master/module3/exo1/incidence-PAY-3.csv)." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://app-learninglab.inria.fr/moocrr/gitlab/b2c48a7ab4afbff5f4d26650b09eb6b4/mooc-rr/raw/master/module3/exo1/incidence-PAY-7.csv?inline=false\"" + ] + }, + { + "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": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02020137803056381042212816FRFrance
12020127819258221056212816FRFrance
2202011710198756812828151119FRFrance
320201079011669111331141018FRFrance
42020097136311054416718211626FRFrance
5202008710424770813140161220FRFrance
620200778959657411344141018FRFrance
720200679264692511603141018FRFrance
820200578505631410696131016FRFrance
92020047799158311015112915FRFrance
1020200375968410078369612FRFrance
11202002765344530853810713FRFrance
1220200179835701912651151119FRFrance
132019527794152461063612816FRFrance
1420195175823367579719612FRFrance
15201950764244276857210713FRFrance
16201949766214540870210713FRFrance
1720194875542338377018511FRFrance
182019477753650581001411715FRFrance
192019467263813163960426FRFrance
2020194574492261563697410FRFrance
2120194475728362778299612FRFrance
2220194374834275169177410FRFrance
23201942762793989856910713FRFrance
242019417413020306230639FRFrance
252019407421122186204639FRFrance
262019397313713104964528FRFrance
272019387307814164740528FRFrance
2820193779701621778102FRFrance
29201936712772632291204FRFrance
.................................
15001991267176081130423912312042FRFrance
15011991257161691070021638281838FRFrance
15021991247161711007122271281739FRFrance
1503199123711947767116223211329FRFrance
1504199122715452995320951271737FRFrance
1505199121714903897520831261636FRFrance
15061991207190531274225364342345FRFrance
15071991197167391124622232291939FRFrance
15081991187213851388228888382551FRFrance
1509199117713462887718047241632FRFrance
15101991167148571006819646261834FRFrance
1511199115713975978118169251832FRFrance
1512199114712265768416846221430FRFrance
151319911379567604113093171123FRFrance
1514199112710864733114397191325FRFrance
15151991117155741118419964271935FRFrance
15161991107166431137221914292038FRFrance
1517199109713741878018702241533FRFrance
1518199108713289881317765231531FRFrance
1519199107712337807716597221529FRFrance
1520199106710877701314741191226FRFrance
1521199105710442654414340181125FRFrance
15221991047791345631126314820FRFrance
15231991037153871048420290271836FRFrance
15241991027162771104621508292038FRFrance
15251991017155651027120859271836FRFrance
15261990527193751329525455342345FRFrance
15271990517190801380724353342543FRFrance
1528199050711079666015498201228FRFrance
15291990497114302610205FRFrance
\n", + "

1530 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202013 7 8030 5638 10422 12 8 \n", + "1 202012 7 8192 5822 10562 12 8 \n", + "2 202011 7 10198 7568 12828 15 11 \n", + "3 202010 7 9011 6691 11331 14 10 \n", + "4 202009 7 13631 10544 16718 21 16 \n", + "5 202008 7 10424 7708 13140 16 12 \n", + "6 202007 7 8959 6574 11344 14 10 \n", + "7 202006 7 9264 6925 11603 14 10 \n", + "8 202005 7 8505 6314 10696 13 10 \n", + "9 202004 7 7991 5831 10151 12 9 \n", + "10 202003 7 5968 4100 7836 9 6 \n", + "11 202002 7 6534 4530 8538 10 7 \n", + "12 202001 7 9835 7019 12651 15 11 \n", + "13 201952 7 7941 5246 10636 12 8 \n", + "14 201951 7 5823 3675 7971 9 6 \n", + "15 201950 7 6424 4276 8572 10 7 \n", + "16 201949 7 6621 4540 8702 10 7 \n", + "17 201948 7 5542 3383 7701 8 5 \n", + "18 201947 7 7536 5058 10014 11 7 \n", + "19 201946 7 2638 1316 3960 4 2 \n", + "20 201945 7 4492 2615 6369 7 4 \n", + "21 201944 7 5728 3627 7829 9 6 \n", + "22 201943 7 4834 2751 6917 7 4 \n", + "23 201942 7 6279 3989 8569 10 7 \n", + "24 201941 7 4130 2030 6230 6 3 \n", + "25 201940 7 4211 2218 6204 6 3 \n", + "26 201939 7 3137 1310 4964 5 2 \n", + "27 201938 7 3078 1416 4740 5 2 \n", + "28 201937 7 970 162 1778 1 0 \n", + "29 201936 7 1277 263 2291 2 0 \n", + "... ... ... ... ... ... ... ... \n", + "1500 199126 7 17608 11304 23912 31 20 \n", + "1501 199125 7 16169 10700 21638 28 18 \n", + "1502 199124 7 16171 10071 22271 28 17 \n", + "1503 199123 7 11947 7671 16223 21 13 \n", + "1504 199122 7 15452 9953 20951 27 17 \n", + "1505 199121 7 14903 8975 20831 26 16 \n", + "1506 199120 7 19053 12742 25364 34 23 \n", + "1507 199119 7 16739 11246 22232 29 19 \n", + "1508 199118 7 21385 13882 28888 38 25 \n", + "1509 199117 7 13462 8877 18047 24 16 \n", + "1510 199116 7 14857 10068 19646 26 18 \n", + "1511 199115 7 13975 9781 18169 25 18 \n", + "1512 199114 7 12265 7684 16846 22 14 \n", + "1513 199113 7 9567 6041 13093 17 11 \n", + "1514 199112 7 10864 7331 14397 19 13 \n", + "1515 199111 7 15574 11184 19964 27 19 \n", + "1516 199110 7 16643 11372 21914 29 20 \n", + "1517 199109 7 13741 8780 18702 24 15 \n", + "1518 199108 7 13289 8813 17765 23 15 \n", + "1519 199107 7 12337 8077 16597 22 15 \n", + "1520 199106 7 10877 7013 14741 19 12 \n", + "1521 199105 7 10442 6544 14340 18 11 \n", + "1522 199104 7 7913 4563 11263 14 8 \n", + "1523 199103 7 15387 10484 20290 27 18 \n", + "1524 199102 7 16277 11046 21508 29 20 \n", + "1525 199101 7 15565 10271 20859 27 18 \n", + "1526 199052 7 19375 13295 25455 34 23 \n", + "1527 199051 7 19080 13807 24353 34 25 \n", + "1528 199050 7 11079 6660 15498 20 12 \n", + "1529 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 16 FR France \n", + "1 16 FR France \n", + "2 19 FR France \n", + "3 18 FR France \n", + "4 26 FR France \n", + "5 20 FR France \n", + "6 18 FR France \n", + "7 18 FR France \n", + "8 16 FR France \n", + "9 15 FR France \n", + "10 12 FR France \n", + "11 13 FR France \n", + "12 19 FR France \n", + "13 16 FR France \n", + "14 12 FR France \n", + "15 13 FR France \n", + "16 13 FR France \n", + "17 11 FR France \n", + "18 15 FR France \n", + "19 6 FR France \n", + "20 10 FR France \n", + "21 12 FR France \n", + "22 10 FR France \n", + "23 13 FR France \n", + "24 9 FR France \n", + "25 9 FR France \n", + "26 8 FR France \n", + "27 8 FR France \n", + "28 2 FR France \n", + "29 4 FR France \n", + "... ... ... ... \n", + "1500 42 FR France \n", + "1501 38 FR France \n", + "1502 39 FR France \n", + "1503 29 FR France \n", + "1504 37 FR France \n", + "1505 36 FR France \n", + "1506 45 FR France \n", + "1507 39 FR France \n", + "1508 51 FR France \n", + "1509 32 FR France \n", + "1510 34 FR France \n", + "1511 32 FR France \n", + "1512 30 FR France \n", + "1513 23 FR France \n", + "1514 25 FR France \n", + "1515 35 FR France \n", + "1516 38 FR France \n", + "1517 33 FR France \n", + "1518 31 FR France \n", + "1519 29 FR France \n", + "1520 26 FR France \n", + "1521 25 FR France \n", + "1522 20 FR France \n", + "1523 36 FR France \n", + "1524 38 FR France \n", + "1525 36 FR France \n", + "1526 45 FR France \n", + "1527 43 FR France \n", + "1528 28 FR France \n", + "1529 5 FR France \n", + "\n", + "[1530 rows x 10 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, skiprows=1)\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": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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": 40, + "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": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1530 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202013 7 8030 5638 10422 12 8 \n", + "1 202012 7 8192 5822 10562 12 8 \n", + "2 202011 7 10198 7568 12828 15 11 \n", + "3 202010 7 9011 6691 11331 14 10 \n", + "4 202009 7 13631 10544 16718 21 16 \n", + "5 202008 7 10424 7708 13140 16 12 \n", + "6 202007 7 8959 6574 11344 14 10 \n", + "7 202006 7 9264 6925 11603 14 10 \n", + "8 202005 7 8505 6314 10696 13 10 \n", + "9 202004 7 7991 5831 10151 12 9 \n", + "10 202003 7 5968 4100 7836 9 6 \n", + "11 202002 7 6534 4530 8538 10 7 \n", + "12 202001 7 9835 7019 12651 15 11 \n", + "13 201952 7 7941 5246 10636 12 8 \n", + "14 201951 7 5823 3675 7971 9 6 \n", + "15 201950 7 6424 4276 8572 10 7 \n", + "16 201949 7 6621 4540 8702 10 7 \n", + "17 201948 7 5542 3383 7701 8 5 \n", + "18 201947 7 7536 5058 10014 11 7 \n", + "19 201946 7 2638 1316 3960 4 2 \n", + "20 201945 7 4492 2615 6369 7 4 \n", + "21 201944 7 5728 3627 7829 9 6 \n", + "22 201943 7 4834 2751 6917 7 4 \n", + "23 201942 7 6279 3989 8569 10 7 \n", + "24 201941 7 4130 2030 6230 6 3 \n", + "25 201940 7 4211 2218 6204 6 3 \n", + "26 201939 7 3137 1310 4964 5 2 \n", + "27 201938 7 3078 1416 4740 5 2 \n", + "28 201937 7 970 162 1778 1 0 \n", + "29 201936 7 1277 263 2291 2 0 \n", + "... ... ... ... ... ... ... ... \n", + "1500 199126 7 17608 11304 23912 31 20 \n", + "1501 199125 7 16169 10700 21638 28 18 \n", + "1502 199124 7 16171 10071 22271 28 17 \n", + "1503 199123 7 11947 7671 16223 21 13 \n", + "1504 199122 7 15452 9953 20951 27 17 \n", + "1505 199121 7 14903 8975 20831 26 16 \n", + "1506 199120 7 19053 12742 25364 34 23 \n", + "1507 199119 7 16739 11246 22232 29 19 \n", + "1508 199118 7 21385 13882 28888 38 25 \n", + "1509 199117 7 13462 8877 18047 24 16 \n", + "1510 199116 7 14857 10068 19646 26 18 \n", + "1511 199115 7 13975 9781 18169 25 18 \n", + "1512 199114 7 12265 7684 16846 22 14 \n", + "1513 199113 7 9567 6041 13093 17 11 \n", + "1514 199112 7 10864 7331 14397 19 13 \n", + "1515 199111 7 15574 11184 19964 27 19 \n", + "1516 199110 7 16643 11372 21914 29 20 \n", + "1517 199109 7 13741 8780 18702 24 15 \n", + "1518 199108 7 13289 8813 17765 23 15 \n", + "1519 199107 7 12337 8077 16597 22 15 \n", + "1520 199106 7 10877 7013 14741 19 12 \n", + "1521 199105 7 10442 6544 14340 18 11 \n", + "1522 199104 7 7913 4563 11263 14 8 \n", + "1523 199103 7 15387 10484 20290 27 18 \n", + "1524 199102 7 16277 11046 21508 29 20 \n", + "1525 199101 7 15565 10271 20859 27 18 \n", + "1526 199052 7 19375 13295 25455 34 23 \n", + "1527 199051 7 19080 13807 24353 34 25 \n", + "1528 199050 7 11079 6660 15498 20 12 \n", + "1529 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 16 FR France \n", + "1 16 FR France \n", + "2 19 FR France \n", + "3 18 FR France \n", + "4 26 FR France \n", + "5 20 FR France \n", + "6 18 FR France \n", + "7 18 FR France \n", + "8 16 FR France \n", + "9 15 FR France \n", + "10 12 FR France \n", + "11 13 FR France \n", + "12 19 FR France \n", + "13 16 FR France \n", + "14 12 FR France \n", + "15 13 FR France \n", + "16 13 FR France \n", + "17 11 FR France \n", + "18 15 FR France \n", + "19 6 FR France \n", + "20 10 FR France \n", + "21 12 FR France \n", + "22 10 FR France \n", + "23 13 FR France \n", + "24 9 FR France \n", + "25 9 FR France \n", + "26 8 FR France \n", + "27 8 FR France \n", + "28 2 FR France \n", + "29 4 FR France \n", + "... ... ... ... \n", + "1500 42 FR France \n", + "1501 38 FR France \n", + "1502 39 FR France \n", + "1503 29 FR France \n", + "1504 37 FR France \n", + "1505 36 FR France \n", + "1506 45 FR France \n", + "1507 39 FR France \n", + "1508 51 FR France \n", + "1509 32 FR France \n", + "1510 34 FR France \n", + "1511 32 FR France \n", + "1512 30 FR France \n", + "1513 23 FR France \n", + "1514 25 FR France \n", + "1515 35 FR France \n", + "1516 38 FR France \n", + "1517 33 FR France \n", + "1518 31 FR France \n", + "1519 29 FR France \n", + "1520 26 FR France \n", + "1521 25 FR France \n", + "1522 20 FR France \n", + "1523 36 FR France \n", + "1524 38 FR France \n", + "1525 36 FR France \n", + "1526 45 FR France \n", + "1527 43 FR France \n", + "1528 28 FR France \n", + "1529 5 FR France \n", + "\n", + "[1530 rows x 10 columns]" + ] + }, + "execution_count": 41, + "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": 42, + "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": 43, + "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": 44, + "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": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "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'].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": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "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" + ] + }, + { + "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": 47, + "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 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": 48, + "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": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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] + }, + "execution_count": 50, + "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": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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