{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence of influenza-like illness in France" ] }, { "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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data on the incidence of influenza-like illness are available from the Web site of the [Réseau Sentinelles](http://www.sentiweb.fr/). We download them as a file in CSV format, in which each line corresponds to a week in the observation period. Only the complete dataset, starting in 1984 and ending with a recent week, is available for download." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To ensure that we always have an available copy of the data, we will dowload it and keep a local version. If we already have a local version we wont download the data again." ] }, { "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": 18, "metadata": {}, "outputs": [], "source": [ "data_file = \"syndrome-grippal.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": [ "This is the documentation of the data from [the download site](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", "\n", "| Column name | Description |\n", "|--------------|---------------------------------------------------------------------------------------------------------------------------|\n", "| `week` | ISO8601 Yearweek number as numeric (year times 100 + week nubmer) |\n", "| `indicator` | Unique identifier of the indicator, see metadata document https://www.sentiweb.fr/meta.json |\n", "| `inc` | Estimated incidence value for the time step, in the geographic level |\n", "| `inc_low` | Lower bound of the estimated incidence 95% Confidence Interval |\n", "| `inc_up` | Upper bound of the estimated incidence 95% Confidence Interval |\n", "| `inc100` | Estimated rate incidence per 100,000 inhabitants |\n", "| `inc100_low` | Lower bound of the estimated incidence 95% Confidence Interval |\n", "| `inc100_up` | Upper bound of the estimated rate incidence 95% Confidence Interval |\n", "| `geo_insee` | Identifier of the geographic area, from INSEE https://www.insee.fr |\n", "| `geo_name` | Geographic label of the area, corresponding to INSEE code. This label is not an id and is only provided for human reading |\n", "\n", "The first line of the CSV file is a comment, which we ignore with `skip=1`." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020231931711212744.021480.02619.033.0FRFrance
120231831992915402.024456.03023.037.0FRFrance
220231732700721779.032235.04133.049.0FRFrance
320231632787522767.032983.04234.050.0FRFrance
420231533745530993.043917.05646.066.0FRFrance
520231434806040671.055449.07261.083.0FRFrance
620231336485956800.072918.09886.0110.0FRFrance
720231237275064499.081001.010997.0121.0FRFrance
820231137463866420.082856.0112100.0124.0FRFrance
920231037636868243.084493.0115103.0127.0FRFrance
1020230936206254778.069346.09382.0104.0FRFrance
1120230837639168065.084717.0115102.0128.0FRFrance
1220230738985180397.099305.0135121.0149.0FRFrance
1320230639736887636.0107100.0146131.0161.0FRFrance
1420230539546986268.0104670.0144130.0158.0FRFrance
1520230437490166916.082886.0113101.0125.0FRFrance
1620230336957061893.077247.010593.0117.0FRFrance
1720230237826070090.086430.0118106.0130.0FRFrance
182023013121773111024.0132522.0183167.0199.0FRFrance
192022523155371142004.0168738.0234214.0254.0FRFrance
202022513248319232128.0264510.0374350.0398.0FRFrance
212022503234143219402.0248884.0353331.0375.0FRFrance
222022493163384151691.0175077.0246228.0264.0FRFrance
232022483121691111744.0131638.0184169.0199.0FRFrance
2420224739641687230.0105602.0145131.0159.0FRFrance
2520224636773560075.075395.010290.0114.0FRFrance
2620224534530638909.051703.06858.078.0FRFrance
2720224433471328880.040546.05243.061.0FRFrance
2820224334476936884.052654.06856.080.0FRFrance
2920224234746240773.054151.07262.082.0FRFrance
.................................
198119852132609619621.032571.04735.059.0FRFrance
198219852032789620885.034907.05138.064.0FRFrance
198319851934315432821.053487.07859.097.0FRFrance
198419851834055529935.051175.07455.093.0FRFrance
198519851733405324366.043740.06244.080.0FRFrance
198619851635036236451.064273.09166.0116.0FRFrance
198719851536388145538.082224.011683.0149.0FRFrance
19881985143134545114400.0154690.0244207.0281.0FRFrance
19891985133197206176080.0218332.0357319.0395.0FRFrance
19901985123245240223304.0267176.0445405.0485.0FRFrance
19911985113276205252399.0300011.0501458.0544.0FRFrance
19921985103353231326279.0380183.0640591.0689.0FRFrance
19931985093369895341109.0398681.0670618.0722.0FRFrance
19941985083389886359529.0420243.0707652.0762.0FRFrance
19951985073471852432599.0511105.0855784.0926.0FRFrance
19961985063565825518011.0613639.01026939.01113.0FRFrance
19971985053637302592795.0681809.011551074.01236.0FRFrance
19981985043424937390794.0459080.0770708.0832.0FRFrance
19991985033213901174689.0253113.0388317.0459.0FRFrance
200019850239758680949.0114223.0177147.0207.0FRFrance
200119850138548965918.0105060.0155120.0190.0FRFrance
200219845238483060602.0109058.0154110.0198.0FRFrance
2003198451310172680242.0123210.0185146.0224.0FRFrance
20041984503123680101401.0145959.0225184.0266.0FRFrance
2005198449310107381684.0120462.0184149.0219.0FRFrance
200619844837862060634.096606.0143110.0176.0FRFrance
200719844737202954274.089784.013199.0163.0FRFrance
200819844638733067686.0106974.0159123.0195.0FRFrance
20091984453135223101414.0169032.0246184.0308.0FRFrance
201019844436842220056.0116788.012537.0213.0FRFrance
\n", "

2011 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202319 3 17112 12744.0 21480.0 26 19.0 \n", "1 202318 3 19929 15402.0 24456.0 30 23.0 \n", "2 202317 3 27007 21779.0 32235.0 41 33.0 \n", "3 202316 3 27875 22767.0 32983.0 42 34.0 \n", "4 202315 3 37455 30993.0 43917.0 56 46.0 \n", "5 202314 3 48060 40671.0 55449.0 72 61.0 \n", "6 202313 3 64859 56800.0 72918.0 98 86.0 \n", "7 202312 3 72750 64499.0 81001.0 109 97.0 \n", "8 202311 3 74638 66420.0 82856.0 112 100.0 \n", "9 202310 3 76368 68243.0 84493.0 115 103.0 \n", "10 202309 3 62062 54778.0 69346.0 93 82.0 \n", "11 202308 3 76391 68065.0 84717.0 115 102.0 \n", "12 202307 3 89851 80397.0 99305.0 135 121.0 \n", "13 202306 3 97368 87636.0 107100.0 146 131.0 \n", "14 202305 3 95469 86268.0 104670.0 144 130.0 \n", "15 202304 3 74901 66916.0 82886.0 113 101.0 \n", "16 202303 3 69570 61893.0 77247.0 105 93.0 \n", "17 202302 3 78260 70090.0 86430.0 118 106.0 \n", "18 202301 3 121773 111024.0 132522.0 183 167.0 \n", "19 202252 3 155371 142004.0 168738.0 234 214.0 \n", "20 202251 3 248319 232128.0 264510.0 374 350.0 \n", "21 202250 3 234143 219402.0 248884.0 353 331.0 \n", "22 202249 3 163384 151691.0 175077.0 246 228.0 \n", "23 202248 3 121691 111744.0 131638.0 184 169.0 \n", "24 202247 3 96416 87230.0 105602.0 145 131.0 \n", "25 202246 3 67735 60075.0 75395.0 102 90.0 \n", "26 202245 3 45306 38909.0 51703.0 68 58.0 \n", "27 202244 3 34713 28880.0 40546.0 52 43.0 \n", "28 202243 3 44769 36884.0 52654.0 68 56.0 \n", "29 202242 3 47462 40773.0 54151.0 72 62.0 \n", "... ... ... ... ... ... ... ... \n", "1981 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1982 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1983 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1984 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1985 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1986 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1987 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1988 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1989 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1990 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1991 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1992 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1993 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1994 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1995 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1996 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1997 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1998 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1999 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2000 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2001 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2002 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2003 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2004 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2005 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2006 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2007 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2008 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2009 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2010 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 33.0 FR France \n", "1 37.0 FR France \n", "2 49.0 FR France \n", "3 50.0 FR France \n", "4 66.0 FR France \n", "5 83.0 FR France \n", "6 110.0 FR France \n", "7 121.0 FR France \n", "8 124.0 FR France \n", "9 127.0 FR France \n", "10 104.0 FR France \n", "11 128.0 FR France \n", "12 149.0 FR France \n", "13 161.0 FR France \n", "14 158.0 FR France \n", "15 125.0 FR France \n", "16 117.0 FR France \n", "17 130.0 FR France \n", "18 199.0 FR France \n", "19 254.0 FR France \n", "20 398.0 FR France \n", "21 375.0 FR France \n", "22 264.0 FR France \n", "23 199.0 FR France \n", "24 159.0 FR France \n", "25 114.0 FR France \n", "26 78.0 FR France \n", "27 61.0 FR France \n", "28 80.0 FR France \n", "29 82.0 FR France \n", "... ... ... ... \n", "1981 59.0 FR France \n", "1982 64.0 FR France \n", "1983 97.0 FR France \n", "1984 93.0 FR France \n", "1985 80.0 FR France \n", "1986 116.0 FR France \n", "1987 149.0 FR France \n", "1988 281.0 FR France \n", "1989 395.0 FR France \n", "1990 485.0 FR France \n", "1991 544.0 FR France \n", "1992 689.0 FR France \n", "1993 722.0 FR France \n", "1994 762.0 FR France \n", "1995 926.0 FR France \n", "1996 1113.0 FR France \n", "1997 1236.0 FR France \n", "1998 832.0 FR France \n", "1999 459.0 FR France \n", "2000 207.0 FR France \n", "2001 190.0 FR France \n", "2002 198.0 FR France \n", "2003 224.0 FR France \n", "2004 266.0 FR France \n", "2005 219.0 FR France \n", "2006 176.0 FR France \n", "2007 163.0 FR France \n", "2008 195.0 FR France \n", "2009 308.0 FR France \n", "2010 213.0 FR France \n", "\n", "[2011 rows x 10 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are there missing data points? Yes, week 19 of year 1989 does not have any observed values." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
177419891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1774 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1774 FR France " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We delete this point, which does not have big consequence for our rather simple analysis." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020231931711212744.021480.02619.033.0FRFrance
120231831992915402.024456.03023.037.0FRFrance
220231732700721779.032235.04133.049.0FRFrance
320231632787522767.032983.04234.050.0FRFrance
420231533745530993.043917.05646.066.0FRFrance
520231434806040671.055449.07261.083.0FRFrance
620231336485956800.072918.09886.0110.0FRFrance
720231237275064499.081001.010997.0121.0FRFrance
820231137463866420.082856.0112100.0124.0FRFrance
920231037636868243.084493.0115103.0127.0FRFrance
1020230936206254778.069346.09382.0104.0FRFrance
1120230837639168065.084717.0115102.0128.0FRFrance
1220230738985180397.099305.0135121.0149.0FRFrance
1320230639736887636.0107100.0146131.0161.0FRFrance
1420230539546986268.0104670.0144130.0158.0FRFrance
1520230437490166916.082886.0113101.0125.0FRFrance
1620230336957061893.077247.010593.0117.0FRFrance
1720230237826070090.086430.0118106.0130.0FRFrance
182023013121773111024.0132522.0183167.0199.0FRFrance
192022523155371142004.0168738.0234214.0254.0FRFrance
202022513248319232128.0264510.0374350.0398.0FRFrance
212022503234143219402.0248884.0353331.0375.0FRFrance
222022493163384151691.0175077.0246228.0264.0FRFrance
232022483121691111744.0131638.0184169.0199.0FRFrance
2420224739641687230.0105602.0145131.0159.0FRFrance
2520224636773560075.075395.010290.0114.0FRFrance
2620224534530638909.051703.06858.078.0FRFrance
2720224433471328880.040546.05243.061.0FRFrance
2820224334476936884.052654.06856.080.0FRFrance
2920224234746240773.054151.07262.082.0FRFrance
.................................
198119852132609619621.032571.04735.059.0FRFrance
198219852032789620885.034907.05138.064.0FRFrance
198319851934315432821.053487.07859.097.0FRFrance
198419851834055529935.051175.07455.093.0FRFrance
198519851733405324366.043740.06244.080.0FRFrance
198619851635036236451.064273.09166.0116.0FRFrance
198719851536388145538.082224.011683.0149.0FRFrance
19881985143134545114400.0154690.0244207.0281.0FRFrance
19891985133197206176080.0218332.0357319.0395.0FRFrance
19901985123245240223304.0267176.0445405.0485.0FRFrance
19911985113276205252399.0300011.0501458.0544.0FRFrance
19921985103353231326279.0380183.0640591.0689.0FRFrance
19931985093369895341109.0398681.0670618.0722.0FRFrance
19941985083389886359529.0420243.0707652.0762.0FRFrance
19951985073471852432599.0511105.0855784.0926.0FRFrance
19961985063565825518011.0613639.01026939.01113.0FRFrance
19971985053637302592795.0681809.011551074.01236.0FRFrance
19981985043424937390794.0459080.0770708.0832.0FRFrance
19991985033213901174689.0253113.0388317.0459.0FRFrance
200019850239758680949.0114223.0177147.0207.0FRFrance
200119850138548965918.0105060.0155120.0190.0FRFrance
200219845238483060602.0109058.0154110.0198.0FRFrance
2003198451310172680242.0123210.0185146.0224.0FRFrance
20041984503123680101401.0145959.0225184.0266.0FRFrance
2005198449310107381684.0120462.0184149.0219.0FRFrance
200619844837862060634.096606.0143110.0176.0FRFrance
200719844737202954274.089784.013199.0163.0FRFrance
200819844638733067686.0106974.0159123.0195.0FRFrance
20091984453135223101414.0169032.0246184.0308.0FRFrance
201019844436842220056.0116788.012537.0213.0FRFrance
\n", "

2010 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202319 3 17112 12744.0 21480.0 26 19.0 \n", "1 202318 3 19929 15402.0 24456.0 30 23.0 \n", "2 202317 3 27007 21779.0 32235.0 41 33.0 \n", "3 202316 3 27875 22767.0 32983.0 42 34.0 \n", "4 202315 3 37455 30993.0 43917.0 56 46.0 \n", "5 202314 3 48060 40671.0 55449.0 72 61.0 \n", "6 202313 3 64859 56800.0 72918.0 98 86.0 \n", "7 202312 3 72750 64499.0 81001.0 109 97.0 \n", "8 202311 3 74638 66420.0 82856.0 112 100.0 \n", "9 202310 3 76368 68243.0 84493.0 115 103.0 \n", "10 202309 3 62062 54778.0 69346.0 93 82.0 \n", "11 202308 3 76391 68065.0 84717.0 115 102.0 \n", "12 202307 3 89851 80397.0 99305.0 135 121.0 \n", "13 202306 3 97368 87636.0 107100.0 146 131.0 \n", "14 202305 3 95469 86268.0 104670.0 144 130.0 \n", "15 202304 3 74901 66916.0 82886.0 113 101.0 \n", "16 202303 3 69570 61893.0 77247.0 105 93.0 \n", "17 202302 3 78260 70090.0 86430.0 118 106.0 \n", "18 202301 3 121773 111024.0 132522.0 183 167.0 \n", "19 202252 3 155371 142004.0 168738.0 234 214.0 \n", "20 202251 3 248319 232128.0 264510.0 374 350.0 \n", "21 202250 3 234143 219402.0 248884.0 353 331.0 \n", "22 202249 3 163384 151691.0 175077.0 246 228.0 \n", "23 202248 3 121691 111744.0 131638.0 184 169.0 \n", "24 202247 3 96416 87230.0 105602.0 145 131.0 \n", "25 202246 3 67735 60075.0 75395.0 102 90.0 \n", "26 202245 3 45306 38909.0 51703.0 68 58.0 \n", "27 202244 3 34713 28880.0 40546.0 52 43.0 \n", "28 202243 3 44769 36884.0 52654.0 68 56.0 \n", "29 202242 3 47462 40773.0 54151.0 72 62.0 \n", "... ... ... ... ... ... ... ... \n", "1981 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1982 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1983 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1984 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1985 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1986 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1987 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1988 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1989 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1990 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1991 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1992 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1993 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1994 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1995 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1996 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1997 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1998 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1999 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2000 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2001 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2002 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2003 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2004 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2005 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2006 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2007 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2008 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2009 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2010 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 33.0 FR France \n", "1 37.0 FR France \n", "2 49.0 FR France \n", "3 50.0 FR France \n", "4 66.0 FR France \n", "5 83.0 FR France \n", "6 110.0 FR France \n", "7 121.0 FR France \n", "8 124.0 FR France \n", "9 127.0 FR France \n", "10 104.0 FR France \n", "11 128.0 FR France \n", "12 149.0 FR France \n", "13 161.0 FR France \n", "14 158.0 FR France \n", "15 125.0 FR France \n", "16 117.0 FR France \n", "17 130.0 FR France \n", "18 199.0 FR France \n", "19 254.0 FR France \n", "20 398.0 FR France \n", "21 375.0 FR France \n", "22 264.0 FR France \n", "23 199.0 FR France \n", "24 159.0 FR France \n", "25 114.0 FR France \n", "26 78.0 FR France \n", "27 61.0 FR France \n", "28 80.0 FR France \n", "29 82.0 FR France \n", "... ... ... ... \n", "1981 59.0 FR France \n", "1982 64.0 FR France \n", "1983 97.0 FR France \n", "1984 93.0 FR France \n", "1985 80.0 FR France \n", "1986 116.0 FR France \n", "1987 149.0 FR France \n", "1988 281.0 FR France \n", "1989 395.0 FR France \n", "1990 485.0 FR France \n", "1991 544.0 FR France \n", "1992 689.0 FR France \n", "1993 722.0 FR France \n", "1994 762.0 FR France \n", "1995 926.0 FR France \n", "1996 1113.0 FR France \n", "1997 1236.0 FR France \n", "1998 832.0 FR France \n", "1999 459.0 FR France \n", "2000 207.0 FR France \n", "2001 190.0 FR France \n", "2002 198.0 FR France \n", "2003 224.0 FR France \n", "2004 266.0 FR France \n", "2005 219.0 FR France \n", "2006 176.0 FR France \n", "2007 163.0 FR France \n", "2008 195.0 FR France \n", "2009 308.0 FR France \n", "2010 213.0 FR France \n", "\n", "[2010 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our dataset uses an uncommon encoding; the week number is attached\n", "to the year number, leaving the impression of a six-digit integer.\n", "That is how Pandas interprets it.\n", "\n", "A second problem is that Pandas does not know about week numbers.\n", "It needs to be given the dates of the beginning and end of the week.\n", "We use the library `isoweek` for that.\n", "\n", "Since the conversion is a bit lengthy, we write a small Python \n", "function for doing it. Then we apply it to all points in our dataset. \n", "The results go into a new column 'period'." ] }, { "cell_type": "code", "execution_count": 6, "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": [ "There are two more small changes to make.\n", "\n", "First, we define the observation periods as the new index of\n", "our dataset. That turns it into a time series, which will be\n", "convenient later on.\n", "\n", "Second, we sort the points chronologically." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We check the consistency of the data. Between the end of a period and\n", "the beginning of the next one, the difference should be zero, or very small.\n", "We tolerate an error of one second.\n", "\n", "This is OK except for one pair of consecutive periods between which\n", "a whole week is missing.\n", "\n", "We recognize the dates: it's the week without observations that we\n", "have deleted earlier!" ] }, { "cell_type": "code", "execution_count": 8, "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": [ "A first look at the data!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "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": [ "A zoom on the last few years shows more clearly that the peaks are situated in winter." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "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": [ "## Study of the annual incidence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the peaks of the epidemic happen in winter, near the transition\n", "between calendar years, we define the reference period for the annual\n", "incidence from August 1st of year $N$ to August 1st of year $N+1$. We\n", "label this period as year $N+1$ because the peak is always located in\n", "year $N+1$. The very low incidence in summer ensures that the arbitrariness\n", "of the choice of reference period has no impact on our conclusions.\n", "\n", "Our task is a bit complicated by the fact that a year does not have an\n", "integer number of weeks. Therefore we modify our reference period a bit:\n", "instead of August 1st, we use the first day of the week containing August 1st.\n", "\n", "A final detail: the dataset starts in October 1984, the first peak is thus\n", "incomplete, We start the analysis with the first full peak." ] }, { "cell_type": "code", "execution_count": 11, "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": [ "Starting from this list of weeks that contain August 1st, we obtain intervals of approximately one year as the periods between two adjacent weeks in this list. We compute the sums of weekly incidences for all these periods.\n", "\n", "We also check that our periods contain between 51 and 52 weeks, as a safeguard against potential mistakes in our code." ] }, { "cell_type": "code", "execution_count": 12, "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": [ "And here are the annual incidences." ] }, { "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": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A sorted list makes it easier to find the highest values (at the end)." ] }, { "cell_type": "code", "execution_count": 14, "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", "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": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, a histogram clearly shows the few very strong epidemics, which affect about 10% of the French population,\n", "but are rare: there were three of them in the course of 35 years. The typical epidemic affects only half as many people." ] }, { "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", 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