{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la Varicelle" ] }, { "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 de la varicelle sont disponibles du site Web du [Réseau Sentinelles](https://websenti.u707.jussieu.fr/sentiweb/). 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": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "data_file = \"syndrome-varicelle.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "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": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202011710236759712875161220FRFrance
120201079011669111331141018FRFrance
22020097136311054416718211626FRFrance
3202008710424770813140161220FRFrance
420200778959657411344141018FRFrance
520200679264692511603141018FRFrance
620200578505631410696131016FRFrance
72020047799158311015112915FRFrance
820200375968410078369612FRFrance
9202002765344530853810713FRFrance
1020200179835701912651151119FRFrance
112019527794152461063612816FRFrance
1220195175823367579719612FRFrance
13201950764244276857210713FRFrance
14201949766214540870210713FRFrance
1520194875542338377018511FRFrance
162019477753650581001411715FRFrance
172019467263813163960426FRFrance
1820194574492261563697410FRFrance
1920194475728362778299612FRFrance
2020194374834275169177410FRFrance
21201942762793989856910713FRFrance
222019417413020306230639FRFrance
232019407421122186204639FRFrance
242019397313713104964528FRFrance
252019387307814164740528FRFrance
2620193779701621778102FRFrance
27201936712772632291204FRFrance
28201935792201857102FRFrance
29201934719976053389315FRFrance
.................................
14981991267176081130423912312042FRFrance
14991991257161691070021638281838FRFrance
15001991247161711007122271281739FRFrance
1501199123711947767116223211329FRFrance
1502199122715452995320951271737FRFrance
1503199121714903897520831261636FRFrance
15041991207190531274225364342345FRFrance
15051991197167391124622232291939FRFrance
15061991187213851388228888382551FRFrance
1507199117713462887718047241632FRFrance
15081991167148571006819646261834FRFrance
1509199115713975978118169251832FRFrance
1510199114712265768416846221430FRFrance
151119911379567604113093171123FRFrance
1512199112710864733114397191325FRFrance
15131991117155741118419964271935FRFrance
15141991107166431137221914292038FRFrance
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1518199106710877701314741191226FRFrance
1519199105710442654414340181125FRFrance
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15211991037153871048420290271836FRFrance
15221991027162771104621508292038FRFrance
15231991017155651027120859271836FRFrance
15241990527193751329525455342345FRFrance
15251990517190801380724353342543FRFrance
1526199050711079666015498201228FRFrance
15271990497114302610205FRFrance
\n", "

1528 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202011 7 10236 7597 12875 16 12 \n", "1 202010 7 9011 6691 11331 14 10 \n", "2 202009 7 13631 10544 16718 21 16 \n", "3 202008 7 10424 7708 13140 16 12 \n", "4 202007 7 8959 6574 11344 14 10 \n", "5 202006 7 9264 6925 11603 14 10 \n", "6 202005 7 8505 6314 10696 13 10 \n", "7 202004 7 7991 5831 10151 12 9 \n", "8 202003 7 5968 4100 7836 9 6 \n", "9 202002 7 6534 4530 8538 10 7 \n", "10 202001 7 9835 7019 12651 15 11 \n", "11 201952 7 7941 5246 10636 12 8 \n", "12 201951 7 5823 3675 7971 9 6 \n", "13 201950 7 6424 4276 8572 10 7 \n", "14 201949 7 6621 4540 8702 10 7 \n", "15 201948 7 5542 3383 7701 8 5 \n", "16 201947 7 7536 5058 10014 11 7 \n", "17 201946 7 2638 1316 3960 4 2 \n", "18 201945 7 4492 2615 6369 7 4 \n", "19 201944 7 5728 3627 7829 9 6 \n", "20 201943 7 4834 2751 6917 7 4 \n", "21 201942 7 6279 3989 8569 10 7 \n", "22 201941 7 4130 2030 6230 6 3 \n", "23 201940 7 4211 2218 6204 6 3 \n", "24 201939 7 3137 1310 4964 5 2 \n", "25 201938 7 3078 1416 4740 5 2 \n", "26 201937 7 970 162 1778 1 0 \n", "27 201936 7 1277 263 2291 2 0 \n", "28 201935 7 922 0 1857 1 0 \n", "29 201934 7 1997 605 3389 3 1 \n", "... ... ... ... ... ... ... ... \n", "1498 199126 7 17608 11304 23912 31 20 \n", "1499 199125 7 16169 10700 21638 28 18 \n", "1500 199124 7 16171 10071 22271 28 17 \n", "1501 199123 7 11947 7671 16223 21 13 \n", "1502 199122 7 15452 9953 20951 27 17 \n", "1503 199121 7 14903 8975 20831 26 16 \n", "1504 199120 7 19053 12742 25364 34 23 \n", "1505 199119 7 16739 11246 22232 29 19 \n", "1506 199118 7 21385 13882 28888 38 25 \n", "1507 199117 7 13462 8877 18047 24 16 \n", "1508 199116 7 14857 10068 19646 26 18 \n", "1509 199115 7 13975 9781 18169 25 18 \n", "1510 199114 7 12265 7684 16846 22 14 \n", "1511 199113 7 9567 6041 13093 17 11 \n", "1512 199112 7 10864 7331 14397 19 13 \n", "1513 199111 7 15574 11184 19964 27 19 \n", "1514 199110 7 16643 11372 21914 29 20 \n", "1515 199109 7 13741 8780 18702 24 15 \n", "1516 199108 7 13289 8813 17765 23 15 \n", "1517 199107 7 12337 8077 16597 22 15 \n", "1518 199106 7 10877 7013 14741 19 12 \n", "1519 199105 7 10442 6544 14340 18 11 \n", "1520 199104 7 7913 4563 11263 14 8 \n", "1521 199103 7 15387 10484 20290 27 18 \n", "1522 199102 7 16277 11046 21508 29 20 \n", "1523 199101 7 15565 10271 20859 27 18 \n", "1524 199052 7 19375 13295 25455 34 23 \n", "1525 199051 7 19080 13807 24353 34 25 \n", "1526 199050 7 11079 6660 15498 20 12 \n", "1527 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 20 FR France \n", "1 18 FR France \n", "2 26 FR France \n", "3 20 FR France \n", "4 18 FR France \n", "5 18 FR France \n", "6 16 FR France \n", "7 15 FR France \n", "8 12 FR France \n", "9 13 FR France \n", "10 19 FR France \n", "11 16 FR France \n", "12 12 FR France \n", "13 13 FR France \n", "14 13 FR France \n", "15 11 FR France \n", "16 15 FR France \n", "17 6 FR France \n", "18 10 FR France \n", "19 12 FR France \n", "20 10 FR France \n", "21 13 FR France \n", "22 9 FR France \n", "23 9 FR France \n", "24 8 FR France \n", "25 8 FR France \n", "26 2 FR France \n", "27 4 FR France \n", "28 2 FR France \n", "29 5 FR France \n", "... ... ... ... \n", "1498 42 FR France \n", "1499 38 FR France \n", "1500 39 FR France \n", "1501 29 FR France \n", "1502 37 FR France \n", "1503 36 FR France \n", "1504 45 FR France \n", "1505 39 FR France \n", "1506 51 FR France \n", "1507 32 FR France \n", "1508 34 FR France \n", "1509 32 FR France \n", "1510 30 FR France \n", "1511 23 FR France \n", "1512 25 FR France \n", "1513 35 FR France \n", "1514 38 FR France \n", "1515 33 FR France \n", "1516 31 FR France \n", "1517 29 FR France \n", "1518 26 FR France \n", "1519 25 FR France \n", "1520 20 FR France \n", "1521 36 FR France \n", "1522 38 FR France \n", "1523 36 FR France \n", "1524 45 FR France \n", "1525 43 FR France \n", "1526 28 FR France \n", "1527 5 FR France \n", "\n", "[1528 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vérifions si nous avons des points manquants dans ce jeu de données." ] }, { "cell_type": "code", "execution_count": 8, "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": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202011710236759712875161220FRFrance
120201079011669111331141018FRFrance
22020097136311054416718211626FRFrance
3202008710424770813140161220FRFrance
420200778959657411344141018FRFrance
520200679264692511603141018FRFrance
620200578505631410696131016FRFrance
72020047799158311015112915FRFrance
820200375968410078369612FRFrance
9202002765344530853810713FRFrance
1020200179835701912651151119FRFrance
112019527794152461063612816FRFrance
1220195175823367579719612FRFrance
13201950764244276857210713FRFrance
14201949766214540870210713FRFrance
1520194875542338377018511FRFrance
162019477753650581001411715FRFrance
172019467263813163960426FRFrance
1820194574492261563697410FRFrance
1920194475728362778299612FRFrance
2020194374834275169177410FRFrance
21201942762793989856910713FRFrance
222019417413020306230639FRFrance
232019407421122186204639FRFrance
242019397313713104964528FRFrance
252019387307814164740528FRFrance
2620193779701621778102FRFrance
27201936712772632291204FRFrance
28201935792201857102FRFrance
29201934719976053389315FRFrance
.................................
14981991267176081130423912312042FRFrance
14991991257161691070021638281838FRFrance
15001991247161711007122271281739FRFrance
1501199123711947767116223211329FRFrance
1502199122715452995320951271737FRFrance
1503199121714903897520831261636FRFrance
15041991207190531274225364342345FRFrance
15051991197167391124622232291939FRFrance
15061991187213851388228888382551FRFrance
1507199117713462887718047241632FRFrance
15081991167148571006819646261834FRFrance
1509199115713975978118169251832FRFrance
1510199114712265768416846221430FRFrance
151119911379567604113093171123FRFrance
1512199112710864733114397191325FRFrance
15131991117155741118419964271935FRFrance
15141991107166431137221914292038FRFrance
1515199109713741878018702241533FRFrance
1516199108713289881317765231531FRFrance
1517199107712337807716597221529FRFrance
1518199106710877701314741191226FRFrance
1519199105710442654414340181125FRFrance
15201991047791345631126314820FRFrance
15211991037153871048420290271836FRFrance
15221991027162771104621508292038FRFrance
15231991017155651027120859271836FRFrance
15241990527193751329525455342345FRFrance
15251990517190801380724353342543FRFrance
1526199050711079666015498201228FRFrance
15271990497114302610205FRFrance
\n", "

1528 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202011 7 10236 7597 12875 16 12 \n", "1 202010 7 9011 6691 11331 14 10 \n", "2 202009 7 13631 10544 16718 21 16 \n", "3 202008 7 10424 7708 13140 16 12 \n", "4 202007 7 8959 6574 11344 14 10 \n", "5 202006 7 9264 6925 11603 14 10 \n", "6 202005 7 8505 6314 10696 13 10 \n", "7 202004 7 7991 5831 10151 12 9 \n", "8 202003 7 5968 4100 7836 9 6 \n", "9 202002 7 6534 4530 8538 10 7 \n", "10 202001 7 9835 7019 12651 15 11 \n", "11 201952 7 7941 5246 10636 12 8 \n", "12 201951 7 5823 3675 7971 9 6 \n", "13 201950 7 6424 4276 8572 10 7 \n", "14 201949 7 6621 4540 8702 10 7 \n", "15 201948 7 5542 3383 7701 8 5 \n", "16 201947 7 7536 5058 10014 11 7 \n", "17 201946 7 2638 1316 3960 4 2 \n", "18 201945 7 4492 2615 6369 7 4 \n", "19 201944 7 5728 3627 7829 9 6 \n", "20 201943 7 4834 2751 6917 7 4 \n", "21 201942 7 6279 3989 8569 10 7 \n", "22 201941 7 4130 2030 6230 6 3 \n", "23 201940 7 4211 2218 6204 6 3 \n", "24 201939 7 3137 1310 4964 5 2 \n", "25 201938 7 3078 1416 4740 5 2 \n", "26 201937 7 970 162 1778 1 0 \n", "27 201936 7 1277 263 2291 2 0 \n", "28 201935 7 922 0 1857 1 0 \n", "29 201934 7 1997 605 3389 3 1 \n", "... ... ... ... ... ... ... ... \n", "1498 199126 7 17608 11304 23912 31 20 \n", "1499 199125 7 16169 10700 21638 28 18 \n", "1500 199124 7 16171 10071 22271 28 17 \n", "1501 199123 7 11947 7671 16223 21 13 \n", "1502 199122 7 15452 9953 20951 27 17 \n", "1503 199121 7 14903 8975 20831 26 16 \n", "1504 199120 7 19053 12742 25364 34 23 \n", "1505 199119 7 16739 11246 22232 29 19 \n", "1506 199118 7 21385 13882 28888 38 25 \n", "1507 199117 7 13462 8877 18047 24 16 \n", "1508 199116 7 14857 10068 19646 26 18 \n", "1509 199115 7 13975 9781 18169 25 18 \n", "1510 199114 7 12265 7684 16846 22 14 \n", "1511 199113 7 9567 6041 13093 17 11 \n", "1512 199112 7 10864 7331 14397 19 13 \n", "1513 199111 7 15574 11184 19964 27 19 \n", "1514 199110 7 16643 11372 21914 29 20 \n", "1515 199109 7 13741 8780 18702 24 15 \n", "1516 199108 7 13289 8813 17765 23 15 \n", "1517 199107 7 12337 8077 16597 22 15 \n", "1518 199106 7 10877 7013 14741 19 12 \n", "1519 199105 7 10442 6544 14340 18 11 \n", "1520 199104 7 7913 4563 11263 14 8 \n", "1521 199103 7 15387 10484 20290 27 18 \n", "1522 199102 7 16277 11046 21508 29 20 \n", "1523 199101 7 15565 10271 20859 27 18 \n", "1524 199052 7 19375 13295 25455 34 23 \n", "1525 199051 7 19080 13807 24353 34 25 \n", "1526 199050 7 11079 6660 15498 20 12 \n", "1527 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 20 FR France \n", "1 18 FR France \n", "2 26 FR France \n", "3 20 FR France \n", "4 18 FR France \n", "5 18 FR France \n", "6 16 FR France \n", "7 15 FR France \n", "8 12 FR France \n", "9 13 FR France \n", "10 19 FR France \n", "11 16 FR France \n", "12 12 FR France \n", "13 13 FR France \n", "14 13 FR France \n", "15 11 FR France \n", "16 15 FR France \n", "17 6 FR France \n", "18 10 FR France \n", "19 12 FR France \n", "20 10 FR France \n", "21 13 FR France \n", "22 9 FR France \n", "23 9 FR France \n", "24 8 FR France \n", "25 8 FR France \n", "26 2 FR France \n", "27 4 FR France \n", "28 2 FR France \n", "29 5 FR France \n", "... ... ... ... \n", "1498 42 FR France \n", "1499 38 FR France \n", "1500 39 FR France \n", "1501 29 FR France \n", "1502 37 FR France \n", "1503 36 FR France \n", "1504 45 FR France \n", "1505 39 FR France \n", "1506 51 FR France \n", "1507 32 FR France \n", "1508 34 FR France \n", "1509 32 FR France \n", "1510 30 FR France \n", "1511 23 FR France \n", "1512 25 FR France \n", "1513 35 FR France \n", "1514 38 FR France \n", "1515 33 FR France \n", "1516 31 FR France \n", "1517 29 FR France \n", "1518 26 FR France \n", "1519 25 FR France \n", "1520 20 FR France \n", "1521 36 FR France \n", "1522 38 FR France \n", "1523 36 FR France \n", "1524 45 FR France \n", "1525 43 FR France \n", "1526 28 FR France \n", "1527 5 FR France \n", "\n", "[1528 rows x 10 columns]" ] }, "execution_count": 10, "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": 11, "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": 12, "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." ] }, { "cell_type": "code", "execution_count": 13, "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 !\n" ] }, { "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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\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": 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": [ "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 entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er août de l'année 𝑁 au 1er septembre de l'année 𝑁+1\n", "\n", ".\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en été, cette modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent en octobre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 19, "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": { "hideCode": true }, "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.\n" ] }, { "cell_type": "code", "execution_count": 21, "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": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { 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" ] }, "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).\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "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": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "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 }