diff --git a/module3/exo2/module3_exo1_analyse-syndrome-grippal.ipynb b/module3/exo2/module3_exo1_analyse-syndrome-grippal.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..549153d4c7afb3e37cea609620a7cb989a5a428c --- /dev/null +++ b/module3/exo2/module3_exo1_analyse-syndrome-grippal.ipynb @@ -0,0 +1,2562 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek\n", + "import os\n", + "import urllib.request" + ] + }, + { + "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": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "local_filename = \"incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Télécharger si le fichier local n'existe pas, afin de se protéger contre une éventuelle modification de l'url ou bien la disparition du Réseau Sentinelle" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Téléchargement du fichier\n" + ] + } + ], + "source": [ + " if not os.path.exists(local_filename):\n", + " print(\"Téléchargement du fichier\")\n", + " urllib.request.urlretrieve(data_url, local_filename)\n", + "else:\n", + " print(\"Fichier déjà présent localement.\")" + ] + }, + { + "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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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212024513201697187843.0215551.0302281.0323.0FRFrance
222024503136694126369.0147019.0205190.0220.0FRFrance
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.................................
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2116 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202520 3 23077 17228.0 28926.0 34 25.0 \n", + "1 202519 3 16342 12366.0 20318.0 24 18.0 \n", + "2 202518 3 18115 13975.0 22255.0 27 21.0 \n", + "3 202517 3 22150 17291.0 27009.0 33 26.0 \n", + "4 202516 3 28564 22550.0 34578.0 43 34.0 \n", + "5 202515 3 35721 29592.0 41850.0 53 44.0 \n", + "6 202514 3 37579 31232.0 43926.0 56 47.0 \n", + "7 202513 3 39673 33686.0 45660.0 59 50.0 \n", + "8 202512 3 52543 45627.0 59459.0 78 68.0 \n", + "9 202511 3 59469 52154.0 66784.0 89 78.0 \n", + "10 202510 3 60334 53048.0 67620.0 90 79.0 \n", + "11 202509 3 84531 74994.0 94068.0 126 112.0 \n", + "12 202508 3 136020 124824.0 147216.0 203 186.0 \n", + "13 202507 3 208952 195988.0 221916.0 312 293.0 \n", + "14 202506 3 273519 258159.0 288879.0 408 385.0 \n", + "15 202505 3 334395 318416.0 350374.0 499 475.0 \n", + "16 202504 3 350043 332885.0 367201.0 522 496.0 \n", + "17 202503 3 252772 238917.0 266627.0 377 356.0 \n", + "18 202502 3 257247 242991.0 271503.0 384 363.0 \n", + "19 202501 3 231549 214627.0 248471.0 345 320.0 \n", + "20 202452 3 201726 185870.0 217582.0 302 278.0 \n", + "21 202451 3 201697 187843.0 215551.0 302 281.0 \n", + "22 202450 3 136694 126369.0 147019.0 205 190.0 \n", + "23 202449 3 108487 99037.0 117937.0 163 149.0 \n", + "24 202448 3 87381 78687.0 96075.0 131 118.0 \n", + "25 202447 3 76286 67626.0 84946.0 114 101.0 \n", + "26 202446 3 56399 49006.0 63792.0 85 74.0 \n", + "27 202445 3 47347 40843.0 53851.0 71 61.0 \n", + "28 202444 3 36039 30122.0 41956.0 54 45.0 \n", + "29 202443 3 46572 39928.0 53216.0 70 60.0 \n", + "... ... ... ... ... ... ... ... \n", + "2086 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2087 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2088 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2089 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2090 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2091 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2092 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2093 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2094 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2095 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2096 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2097 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2098 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2099 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2100 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2101 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2102 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2103 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2104 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2105 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2106 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2107 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2108 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2109 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2110 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2111 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2112 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2113 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2114 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2115 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 43.0 FR France \n", + "1 30.0 FR France \n", + "2 33.0 FR France \n", + "3 40.0 FR France \n", + "4 52.0 FR France \n", + "5 62.0 FR France \n", + "6 65.0 FR France \n", + "7 68.0 FR France \n", + "8 88.0 FR France \n", + "9 100.0 FR France \n", + "10 101.0 FR France \n", + "11 140.0 FR France \n", + "12 220.0 FR France \n", + "13 331.0 FR France \n", + "14 431.0 FR France \n", + "15 523.0 FR France \n", + "16 548.0 FR France \n", + "17 398.0 FR France \n", + "18 405.0 FR France \n", + "19 370.0 FR France \n", + "20 326.0 FR France \n", + "21 323.0 FR France \n", + "22 220.0 FR France \n", + "23 177.0 FR France \n", + "24 144.0 FR France \n", + "25 127.0 FR France \n", + "26 96.0 FR France \n", + "27 81.0 FR France \n", + "28 63.0 FR France \n", + "29 80.0 FR France \n", + "... ... ... ... \n", + "2086 59.0 FR France \n", + "2087 64.0 FR France \n", + "2088 97.0 FR France \n", + "2089 93.0 FR France \n", + "2090 80.0 FR France \n", + "2091 116.0 FR France \n", + "2092 149.0 FR France \n", + "2093 281.0 FR France \n", + "2094 395.0 FR France \n", + "2095 485.0 FR France \n", + "2096 544.0 FR France \n", + "2097 689.0 FR France \n", + "2098 722.0 FR France \n", + "2099 762.0 FR France \n", + "2100 926.0 FR France \n", + "2101 1113.0 FR France \n", + "2102 1236.0 FR France \n", + "2103 832.0 FR France \n", + "2104 459.0 FR France \n", + "2105 207.0 FR France \n", + "2106 190.0 FR France \n", + "2107 198.0 FR France \n", + "2108 224.0 FR France \n", + "2109 266.0 FR France \n", + "2110 219.0 FR France \n", + "2111 176.0 FR France \n", + "2112 163.0 FR France \n", + "2113 195.0 FR France \n", + "2114 308.0 FR France \n", + "2115 213.0 FR France \n", + "\n", + "[2116 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(local_filename, 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": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18791989193-NaNNaN-NaNNaNFRFrance
\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1879 198919 3 - NaN NaN - NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1879 FR France " + ] + }, + "execution_count": 6, + "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": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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21021985053637302592795.0681809.011551074.01236.0FRFrance
21031985043424937390794.0459080.0770708.0832.0FRFrance
21041985033213901174689.0253113.0388317.0459.0FRFrance
210519850239758680949.0114223.0177147.0207.0FRFrance
210619850138548965918.0105060.0155120.0190.0FRFrance
210719845238483060602.0109058.0154110.0198.0FRFrance
2108198451310172680242.0123210.0185146.0224.0FRFrance
21091984503123680101401.0145959.0225184.0266.0FRFrance
2110198449310107381684.0120462.0184149.0219.0FRFrance
211119844837862060634.096606.0143110.0176.0FRFrance
211219844737202954274.089784.013199.0163.0FRFrance
211319844638733067686.0106974.0159123.0195.0FRFrance
21141984453135223101414.0169032.0246184.0308.0FRFrance
211519844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2115 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202520 3 23077 17228.0 28926.0 34 25.0 \n", + "1 202519 3 16342 12366.0 20318.0 24 18.0 \n", + "2 202518 3 18115 13975.0 22255.0 27 21.0 \n", + "3 202517 3 22150 17291.0 27009.0 33 26.0 \n", + "4 202516 3 28564 22550.0 34578.0 43 34.0 \n", + "5 202515 3 35721 29592.0 41850.0 53 44.0 \n", + "6 202514 3 37579 31232.0 43926.0 56 47.0 \n", + "7 202513 3 39673 33686.0 45660.0 59 50.0 \n", + "8 202512 3 52543 45627.0 59459.0 78 68.0 \n", + "9 202511 3 59469 52154.0 66784.0 89 78.0 \n", + "10 202510 3 60334 53048.0 67620.0 90 79.0 \n", + "11 202509 3 84531 74994.0 94068.0 126 112.0 \n", + "12 202508 3 136020 124824.0 147216.0 203 186.0 \n", + "13 202507 3 208952 195988.0 221916.0 312 293.0 \n", + "14 202506 3 273519 258159.0 288879.0 408 385.0 \n", + "15 202505 3 334395 318416.0 350374.0 499 475.0 \n", + "16 202504 3 350043 332885.0 367201.0 522 496.0 \n", + "17 202503 3 252772 238917.0 266627.0 377 356.0 \n", + "18 202502 3 257247 242991.0 271503.0 384 363.0 \n", + "19 202501 3 231549 214627.0 248471.0 345 320.0 \n", + "20 202452 3 201726 185870.0 217582.0 302 278.0 \n", + "21 202451 3 201697 187843.0 215551.0 302 281.0 \n", + "22 202450 3 136694 126369.0 147019.0 205 190.0 \n", + "23 202449 3 108487 99037.0 117937.0 163 149.0 \n", + "24 202448 3 87381 78687.0 96075.0 131 118.0 \n", + "25 202447 3 76286 67626.0 84946.0 114 101.0 \n", + "26 202446 3 56399 49006.0 63792.0 85 74.0 \n", + "27 202445 3 47347 40843.0 53851.0 71 61.0 \n", + "28 202444 3 36039 30122.0 41956.0 54 45.0 \n", + "29 202443 3 46572 39928.0 53216.0 70 60.0 \n", + "... ... ... ... ... ... ... ... \n", + "2086 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2087 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2088 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2089 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2090 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2091 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2092 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2093 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2094 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2095 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2096 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2097 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2098 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2099 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2100 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2101 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2102 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2103 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2104 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2105 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2106 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2107 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2108 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2109 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2110 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2111 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2112 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2113 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2114 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2115 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 43.0 FR France \n", + "1 30.0 FR France \n", + "2 33.0 FR France \n", + "3 40.0 FR France \n", + "4 52.0 FR France \n", + "5 62.0 FR France \n", + "6 65.0 FR France \n", + "7 68.0 FR France \n", + "8 88.0 FR France \n", + "9 100.0 FR France \n", + "10 101.0 FR France \n", + "11 140.0 FR France \n", + "12 220.0 FR France \n", + "13 331.0 FR France \n", + "14 431.0 FR France \n", + "15 523.0 FR France \n", + "16 548.0 FR France \n", + "17 398.0 FR France \n", + "18 405.0 FR France \n", + "19 370.0 FR France \n", + "20 326.0 FR France \n", + "21 323.0 FR France \n", + "22 220.0 FR France \n", + "23 177.0 FR France \n", + "24 144.0 FR France \n", + "25 127.0 FR France \n", + "26 96.0 FR France \n", + "27 81.0 FR France \n", + "28 63.0 FR France \n", + "29 80.0 FR France \n", + "... ... ... ... \n", + "2086 59.0 FR France \n", + "2087 64.0 FR France \n", + "2088 97.0 FR France \n", + "2089 93.0 FR France \n", + "2090 80.0 FR France \n", + "2091 116.0 FR France \n", + "2092 149.0 FR France \n", + "2093 281.0 FR France \n", + "2094 395.0 FR France \n", + "2095 485.0 FR France \n", + "2096 544.0 FR France \n", + "2097 689.0 FR France \n", + "2098 722.0 FR France \n", + "2099 762.0 FR France \n", + "2100 926.0 FR France \n", + "2101 1113.0 FR France \n", + "2102 1236.0 FR France \n", + "2103 832.0 FR France \n", + "2104 459.0 FR France \n", + "2105 207.0 FR France \n", + "2106 190.0 FR France \n", + "2107 198.0 FR France \n", + "2108 224.0 FR France \n", + "2109 266.0 FR France \n", + "2110 219.0 FR France \n", + "2111 176.0 FR France \n", + "2112 163.0 FR France \n", + "2113 195.0 FR France \n", + "2114 308.0 FR France \n", + "2115 213.0 FR France \n", + "\n", + "[2115 rows x 10 columns]" + ] + }, + "execution_count": 13, + "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": 8, + "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": 9, + "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": 14, + "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": 17, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].astype(int)" + ] + }, + { + "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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\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": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "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": 20, + "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": 21, + "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": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "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.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": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2021 743449\n", + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2010315\n", + "2022 2060304\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", + "2023 2873501\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", + "2024 3670417\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": 23, + "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": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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