{ "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": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "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": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020210632336519231.027499.03529.041.0FRFrance
120210532242618445.026407.03428.040.0FRFrance
220210432580421491.030117.03932.046.0FRFrance
320210332181017894.025726.03327.039.0FRFrance
420210231732013906.020734.02621.031.0FRFrance
520210132179917778.025820.03327.039.0FRFrance
620205332122016498.025942.03225.039.0FRFrance
720205231642812285.020571.02519.031.0FRFrance
820205132161917370.025868.03327.039.0FRFrance
920205031684513220.020470.02620.032.0FRFrance
102020493129399923.015955.02015.025.0FRFrance
1120204831380410641.016967.02116.026.0FRFrance
1220204731908515285.022885.02923.035.0FRFrance
1320204632480120503.029099.03831.045.0FRFrance
1420204534251636857.048175.06556.074.0FRFrance
1520204434456738521.050613.06859.077.0FRFrance
1620204334373737523.049951.06657.075.0FRFrance
1720204233514529812.040478.05345.061.0FRFrance
1820204132787723206.032548.04235.049.0FRFrance
1920204032044316381.024505.03125.037.0FRFrance
2020203931981015900.023720.03024.036.0FRFrance
2120203832556221142.029982.03932.046.0FRFrance
2220203731848514649.022321.02822.034.0FRFrance
232020363103907646.013134.01612.020.0FRFrance
24202035399186842.012994.01510.020.0FRFrance
25202034360843090.09078.094.014.0FRFrance
26202033361063411.08801.095.013.0FRFrance
27202032359183330.08506.095.013.0FRFrance
28202031343512269.06433.074.010.0FRFrance
29202030381795442.010916.0128.016.0FRFrance
.................................
186419852132609619621.032571.04735.059.0FRFrance
186519852032789620885.034907.05138.064.0FRFrance
186619851934315432821.053487.07859.097.0FRFrance
186719851834055529935.051175.07455.093.0FRFrance
186819851733405324366.043740.06244.080.0FRFrance
186919851635036236451.064273.09166.0116.0FRFrance
187019851536388145538.082224.011683.0149.0FRFrance
18711985143134545114400.0154690.0244207.0281.0FRFrance
18721985133197206176080.0218332.0357319.0395.0FRFrance
18731985123245240223304.0267176.0445405.0485.0FRFrance
18741985113276205252399.0300011.0501458.0544.0FRFrance
18751985103353231326279.0380183.0640591.0689.0FRFrance
18761985093369895341109.0398681.0670618.0722.0FRFrance
18771985083389886359529.0420243.0707652.0762.0FRFrance
18781985073471852432599.0511105.0855784.0926.0FRFrance
18791985063565825518011.0613639.01026939.01113.0FRFrance
18801985053637302592795.0681809.011551074.01236.0FRFrance
18811985043424937390794.0459080.0770708.0832.0FRFrance
18821985033213901174689.0253113.0388317.0459.0FRFrance
188319850239758680949.0114223.0177147.0207.0FRFrance
188419850138548965918.0105060.0155120.0190.0FRFrance
188519845238483060602.0109058.0154110.0198.0FRFrance
1886198451310172680242.0123210.0185146.0224.0FRFrance
18871984503123680101401.0145959.0225184.0266.0FRFrance
1888198449310107381684.0120462.0184149.0219.0FRFrance
188919844837862060634.096606.0143110.0176.0FRFrance
189019844737202954274.089784.013199.0163.0FRFrance
189119844638733067686.0106974.0159123.0195.0FRFrance
18921984453135223101414.0169032.0246184.0308.0FRFrance
189319844436842220056.0116788.012537.0213.0FRFrance
\n", "

1894 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202106 3 23365 19231.0 27499.0 35 29.0 \n", "1 202105 3 22426 18445.0 26407.0 34 28.0 \n", "2 202104 3 25804 21491.0 30117.0 39 32.0 \n", "3 202103 3 21810 17894.0 25726.0 33 27.0 \n", "4 202102 3 17320 13906.0 20734.0 26 21.0 \n", "5 202101 3 21799 17778.0 25820.0 33 27.0 \n", "6 202053 3 21220 16498.0 25942.0 32 25.0 \n", "7 202052 3 16428 12285.0 20571.0 25 19.0 \n", "8 202051 3 21619 17370.0 25868.0 33 27.0 \n", "9 202050 3 16845 13220.0 20470.0 26 20.0 \n", "10 202049 3 12939 9923.0 15955.0 20 15.0 \n", "11 202048 3 13804 10641.0 16967.0 21 16.0 \n", "12 202047 3 19085 15285.0 22885.0 29 23.0 \n", "13 202046 3 24801 20503.0 29099.0 38 31.0 \n", "14 202045 3 42516 36857.0 48175.0 65 56.0 \n", "15 202044 3 44567 38521.0 50613.0 68 59.0 \n", "16 202043 3 43737 37523.0 49951.0 66 57.0 \n", "17 202042 3 35145 29812.0 40478.0 53 45.0 \n", "18 202041 3 27877 23206.0 32548.0 42 35.0 \n", "19 202040 3 20443 16381.0 24505.0 31 25.0 \n", "20 202039 3 19810 15900.0 23720.0 30 24.0 \n", "21 202038 3 25562 21142.0 29982.0 39 32.0 \n", "22 202037 3 18485 14649.0 22321.0 28 22.0 \n", "23 202036 3 10390 7646.0 13134.0 16 12.0 \n", "24 202035 3 9918 6842.0 12994.0 15 10.0 \n", "25 202034 3 6084 3090.0 9078.0 9 4.0 \n", "26 202033 3 6106 3411.0 8801.0 9 5.0 \n", "27 202032 3 5918 3330.0 8506.0 9 5.0 \n", "28 202031 3 4351 2269.0 6433.0 7 4.0 \n", "29 202030 3 8179 5442.0 10916.0 12 8.0 \n", "... ... ... ... ... ... ... ... \n", "1864 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1865 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1866 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1867 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1868 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1869 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1870 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1871 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1872 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1873 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1874 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1875 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1876 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1877 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1878 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1879 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1880 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1881 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1882 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1883 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1884 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1885 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1886 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1887 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1888 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1889 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1890 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1891 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1892 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1893 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 41.0 FR France \n", "1 40.0 FR France \n", "2 46.0 FR France \n", "3 39.0 FR France \n", "4 31.0 FR France \n", "5 39.0 FR France \n", "6 39.0 FR France \n", "7 31.0 FR France \n", "8 39.0 FR France \n", "9 32.0 FR France \n", "10 25.0 FR France \n", "11 26.0 FR France \n", "12 35.0 FR France \n", "13 45.0 FR France \n", "14 74.0 FR France \n", "15 77.0 FR France \n", "16 75.0 FR France \n", "17 61.0 FR France \n", "18 49.0 FR France \n", "19 37.0 FR France \n", "20 36.0 FR France \n", "21 46.0 FR France \n", "22 34.0 FR France \n", "23 20.0 FR France \n", "24 20.0 FR France \n", "25 14.0 FR France \n", "26 13.0 FR France \n", "27 13.0 FR France \n", "28 10.0 FR France \n", "29 16.0 FR France \n", "... ... ... ... \n", "1864 59.0 FR France \n", "1865 64.0 FR France \n", "1866 97.0 FR France \n", "1867 93.0 FR France \n", "1868 80.0 FR France \n", "1869 116.0 FR France \n", "1870 149.0 FR France \n", "1871 281.0 FR France \n", "1872 395.0 FR France \n", "1873 485.0 FR France \n", "1874 544.0 FR France \n", "1875 689.0 FR France \n", "1876 722.0 FR France \n", "1877 762.0 FR France \n", "1878 926.0 FR France \n", "1879 1113.0 FR France \n", "1880 1236.0 FR France \n", "1881 832.0 FR France \n", "1882 459.0 FR France \n", "1883 207.0 FR France \n", "1884 190.0 FR France \n", "1885 198.0 FR France \n", "1886 224.0 FR France \n", "1887 266.0 FR France \n", "1888 219.0 FR France \n", "1889 176.0 FR France \n", "1890 163.0 FR France \n", "1891 195.0 FR France \n", "1892 308.0 FR France \n", "1893 213.0 FR France \n", "\n", "[1894 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, 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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
165719891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1657 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1657 FR France " ] }, "execution_count": 5, "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": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020210632336519231.027499.03529.041.0FRFrance
120210532242618445.026407.03428.040.0FRFrance
220210432580421491.030117.03932.046.0FRFrance
320210332181017894.025726.03327.039.0FRFrance
420210231732013906.020734.02621.031.0FRFrance
520210132179917778.025820.03327.039.0FRFrance
620205332122016498.025942.03225.039.0FRFrance
720205231642812285.020571.02519.031.0FRFrance
820205132161917370.025868.03327.039.0FRFrance
920205031684513220.020470.02620.032.0FRFrance
102020493129399923.015955.02015.025.0FRFrance
1120204831380410641.016967.02116.026.0FRFrance
1220204731908515285.022885.02923.035.0FRFrance
1320204632480120503.029099.03831.045.0FRFrance
1420204534251636857.048175.06556.074.0FRFrance
1520204434456738521.050613.06859.077.0FRFrance
1620204334373737523.049951.06657.075.0FRFrance
1720204233514529812.040478.05345.061.0FRFrance
1820204132787723206.032548.04235.049.0FRFrance
1920204032044316381.024505.03125.037.0FRFrance
2020203931981015900.023720.03024.036.0FRFrance
2120203832556221142.029982.03932.046.0FRFrance
2220203731848514649.022321.02822.034.0FRFrance
232020363103907646.013134.01612.020.0FRFrance
24202035399186842.012994.01510.020.0FRFrance
25202034360843090.09078.094.014.0FRFrance
26202033361063411.08801.095.013.0FRFrance
27202032359183330.08506.095.013.0FRFrance
28202031343512269.06433.074.010.0FRFrance
29202030381795442.010916.0128.016.0FRFrance
.................................
186419852132609619621.032571.04735.059.0FRFrance
186519852032789620885.034907.05138.064.0FRFrance
186619851934315432821.053487.07859.097.0FRFrance
186719851834055529935.051175.07455.093.0FRFrance
186819851733405324366.043740.06244.080.0FRFrance
186919851635036236451.064273.09166.0116.0FRFrance
187019851536388145538.082224.011683.0149.0FRFrance
18711985143134545114400.0154690.0244207.0281.0FRFrance
18721985133197206176080.0218332.0357319.0395.0FRFrance
18731985123245240223304.0267176.0445405.0485.0FRFrance
18741985113276205252399.0300011.0501458.0544.0FRFrance
18751985103353231326279.0380183.0640591.0689.0FRFrance
18761985093369895341109.0398681.0670618.0722.0FRFrance
18771985083389886359529.0420243.0707652.0762.0FRFrance
18781985073471852432599.0511105.0855784.0926.0FRFrance
18791985063565825518011.0613639.01026939.01113.0FRFrance
18801985053637302592795.0681809.011551074.01236.0FRFrance
18811985043424937390794.0459080.0770708.0832.0FRFrance
18821985033213901174689.0253113.0388317.0459.0FRFrance
188319850239758680949.0114223.0177147.0207.0FRFrance
188419850138548965918.0105060.0155120.0190.0FRFrance
188519845238483060602.0109058.0154110.0198.0FRFrance
1886198451310172680242.0123210.0185146.0224.0FRFrance
18871984503123680101401.0145959.0225184.0266.0FRFrance
1888198449310107381684.0120462.0184149.0219.0FRFrance
188919844837862060634.096606.0143110.0176.0FRFrance
189019844737202954274.089784.013199.0163.0FRFrance
189119844638733067686.0106974.0159123.0195.0FRFrance
18921984453135223101414.0169032.0246184.0308.0FRFrance
189319844436842220056.0116788.012537.0213.0FRFrance
\n", "

1893 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202106 3 23365 19231.0 27499.0 35 29.0 \n", "1 202105 3 22426 18445.0 26407.0 34 28.0 \n", "2 202104 3 25804 21491.0 30117.0 39 32.0 \n", "3 202103 3 21810 17894.0 25726.0 33 27.0 \n", "4 202102 3 17320 13906.0 20734.0 26 21.0 \n", "5 202101 3 21799 17778.0 25820.0 33 27.0 \n", "6 202053 3 21220 16498.0 25942.0 32 25.0 \n", "7 202052 3 16428 12285.0 20571.0 25 19.0 \n", "8 202051 3 21619 17370.0 25868.0 33 27.0 \n", "9 202050 3 16845 13220.0 20470.0 26 20.0 \n", "10 202049 3 12939 9923.0 15955.0 20 15.0 \n", "11 202048 3 13804 10641.0 16967.0 21 16.0 \n", "12 202047 3 19085 15285.0 22885.0 29 23.0 \n", "13 202046 3 24801 20503.0 29099.0 38 31.0 \n", "14 202045 3 42516 36857.0 48175.0 65 56.0 \n", "15 202044 3 44567 38521.0 50613.0 68 59.0 \n", "16 202043 3 43737 37523.0 49951.0 66 57.0 \n", "17 202042 3 35145 29812.0 40478.0 53 45.0 \n", "18 202041 3 27877 23206.0 32548.0 42 35.0 \n", "19 202040 3 20443 16381.0 24505.0 31 25.0 \n", "20 202039 3 19810 15900.0 23720.0 30 24.0 \n", "21 202038 3 25562 21142.0 29982.0 39 32.0 \n", "22 202037 3 18485 14649.0 22321.0 28 22.0 \n", "23 202036 3 10390 7646.0 13134.0 16 12.0 \n", "24 202035 3 9918 6842.0 12994.0 15 10.0 \n", "25 202034 3 6084 3090.0 9078.0 9 4.0 \n", "26 202033 3 6106 3411.0 8801.0 9 5.0 \n", "27 202032 3 5918 3330.0 8506.0 9 5.0 \n", "28 202031 3 4351 2269.0 6433.0 7 4.0 \n", "29 202030 3 8179 5442.0 10916.0 12 8.0 \n", "... ... ... ... ... ... ... ... \n", "1864 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1865 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1866 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1867 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1868 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1869 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1870 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1871 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1872 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1873 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1874 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1875 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1876 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1877 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1878 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1879 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1880 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1881 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1882 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1883 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1884 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1885 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1886 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1887 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1888 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1889 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1890 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1891 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1892 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1893 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 41.0 FR France \n", "1 40.0 FR France \n", "2 46.0 FR France \n", "3 39.0 FR France \n", "4 31.0 FR France \n", "5 39.0 FR France \n", "6 39.0 FR France \n", "7 31.0 FR France \n", "8 39.0 FR France \n", "9 32.0 FR France \n", "10 25.0 FR France \n", "11 26.0 FR France \n", "12 35.0 FR France \n", "13 45.0 FR France \n", "14 74.0 FR France \n", "15 77.0 FR France \n", "16 75.0 FR France \n", "17 61.0 FR France \n", "18 49.0 FR France \n", "19 37.0 FR France \n", "20 36.0 FR France \n", "21 46.0 FR France \n", "22 34.0 FR France \n", "23 20.0 FR France \n", "24 20.0 FR France \n", "25 14.0 FR France \n", "26 13.0 FR France \n", "27 13.0 FR France \n", "28 10.0 FR France \n", "29 16.0 FR France \n", "... ... ... ... \n", "1864 59.0 FR France \n", "1865 64.0 FR France \n", "1866 97.0 FR France \n", "1867 93.0 FR France \n", "1868 80.0 FR France \n", "1869 116.0 FR France \n", "1870 149.0 FR France \n", "1871 281.0 FR France \n", "1872 395.0 FR France \n", "1873 485.0 FR France \n", "1874 544.0 FR France \n", "1875 689.0 FR France \n", "1876 722.0 FR France \n", "1877 762.0 FR France \n", "1878 926.0 FR France \n", "1879 1113.0 FR France \n", "1880 1236.0 FR France \n", "1881 832.0 FR France \n", "1882 459.0 FR France \n", "1883 207.0 FR France \n", "1884 190.0 FR France \n", "1885 198.0 FR France \n", "1886 224.0 FR France \n", "1887 266.0 FR France \n", "1888 219.0 FR France \n", "1889 176.0 FR France \n", "1890 163.0 FR France \n", "1891 195.0 FR France \n", "1892 308.0 FR France \n", "1893 213.0 FR France \n", "\n", "[1893 rows x 10 columns]" ] }, "execution_count": 6, "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": 7, "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": 8, "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": 9, "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": 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'].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": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "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": 12, "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": 13, "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2042389\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": 15, "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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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