{ "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": 2, "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": 17, "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": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020210932262117527.027715.03426.042.0FRFrance
120210832090316885.024921.03226.038.0FRFrance
220210732239318303.026483.03428.040.0FRFrance
320210632318319134.027232.03529.041.0FRFrance
420210532242618445.026407.03428.040.0FRFrance
520210432580421491.030117.03932.046.0FRFrance
620210332181017894.025726.03327.039.0FRFrance
720210231732013906.020734.02621.031.0FRFrance
820210132179917778.025820.03327.039.0FRFrance
920205332122016498.025942.03225.039.0FRFrance
1020205231642812285.020571.02519.031.0FRFrance
1120205132161917370.025868.03327.039.0FRFrance
1220205031684513220.020470.02620.032.0FRFrance
132020493129399923.015955.02015.025.0FRFrance
1420204831380410641.016967.02116.026.0FRFrance
1520204731908515285.022885.02923.035.0FRFrance
1620204632480120503.029099.03831.045.0FRFrance
1720204534251636857.048175.06556.074.0FRFrance
1820204434456738521.050613.06859.077.0FRFrance
1920204334373737523.049951.06657.075.0FRFrance
2020204233514529812.040478.05345.061.0FRFrance
2120204132787723206.032548.04235.049.0FRFrance
2220204032044316381.024505.03125.037.0FRFrance
2320203931981015900.023720.03024.036.0FRFrance
2420203832555921141.029977.03932.046.0FRFrance
2520203731848514649.022321.02822.034.0FRFrance
262020363103907646.013134.01612.020.0FRFrance
27202035399186842.012994.01510.020.0FRFrance
28202034360843090.09078.094.014.0FRFrance
29202033361063411.08801.095.013.0FRFrance
.................................
186719852132609619621.032571.04735.059.0FRFrance
186819852032789620885.034907.05138.064.0FRFrance
186919851934315432821.053487.07859.097.0FRFrance
187019851834055529935.051175.07455.093.0FRFrance
187119851733405324366.043740.06244.080.0FRFrance
187219851635036236451.064273.09166.0116.0FRFrance
187319851536388145538.082224.011683.0149.0FRFrance
18741985143134545114400.0154690.0244207.0281.0FRFrance
18751985133197206176080.0218332.0357319.0395.0FRFrance
18761985123245240223304.0267176.0445405.0485.0FRFrance
18771985113276205252399.0300011.0501458.0544.0FRFrance
18781985103353231326279.0380183.0640591.0689.0FRFrance
18791985093369895341109.0398681.0670618.0722.0FRFrance
18801985083389886359529.0420243.0707652.0762.0FRFrance
18811985073471852432599.0511105.0855784.0926.0FRFrance
18821985063565825518011.0613639.01026939.01113.0FRFrance
18831985053637302592795.0681809.011551074.01236.0FRFrance
18841985043424937390794.0459080.0770708.0832.0FRFrance
18851985033213901174689.0253113.0388317.0459.0FRFrance
188619850239758680949.0114223.0177147.0207.0FRFrance
188719850138548965918.0105060.0155120.0190.0FRFrance
188819845238483060602.0109058.0154110.0198.0FRFrance
1889198451310172680242.0123210.0185146.0224.0FRFrance
18901984503123680101401.0145959.0225184.0266.0FRFrance
1891198449310107381684.0120462.0184149.0219.0FRFrance
189219844837862060634.096606.0143110.0176.0FRFrance
189319844737202954274.089784.013199.0163.0FRFrance
189419844638733067686.0106974.0159123.0195.0FRFrance
18951984453135223101414.0169032.0246184.0308.0FRFrance
189619844436842220056.0116788.012537.0213.0FRFrance
\n", "

1897 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202109 3 22621 17527.0 27715.0 34 26.0 \n", "1 202108 3 20903 16885.0 24921.0 32 26.0 \n", "2 202107 3 22393 18303.0 26483.0 34 28.0 \n", "3 202106 3 23183 19134.0 27232.0 35 29.0 \n", "4 202105 3 22426 18445.0 26407.0 34 28.0 \n", "5 202104 3 25804 21491.0 30117.0 39 32.0 \n", "6 202103 3 21810 17894.0 25726.0 33 27.0 \n", "7 202102 3 17320 13906.0 20734.0 26 21.0 \n", "8 202101 3 21799 17778.0 25820.0 33 27.0 \n", "9 202053 3 21220 16498.0 25942.0 32 25.0 \n", "10 202052 3 16428 12285.0 20571.0 25 19.0 \n", "11 202051 3 21619 17370.0 25868.0 33 27.0 \n", "12 202050 3 16845 13220.0 20470.0 26 20.0 \n", "13 202049 3 12939 9923.0 15955.0 20 15.0 \n", "14 202048 3 13804 10641.0 16967.0 21 16.0 \n", "15 202047 3 19085 15285.0 22885.0 29 23.0 \n", "16 202046 3 24801 20503.0 29099.0 38 31.0 \n", "17 202045 3 42516 36857.0 48175.0 65 56.0 \n", "18 202044 3 44567 38521.0 50613.0 68 59.0 \n", "19 202043 3 43737 37523.0 49951.0 66 57.0 \n", "20 202042 3 35145 29812.0 40478.0 53 45.0 \n", "21 202041 3 27877 23206.0 32548.0 42 35.0 \n", "22 202040 3 20443 16381.0 24505.0 31 25.0 \n", "23 202039 3 19810 15900.0 23720.0 30 24.0 \n", "24 202038 3 25559 21141.0 29977.0 39 32.0 \n", "25 202037 3 18485 14649.0 22321.0 28 22.0 \n", "26 202036 3 10390 7646.0 13134.0 16 12.0 \n", "27 202035 3 9918 6842.0 12994.0 15 10.0 \n", "28 202034 3 6084 3090.0 9078.0 9 4.0 \n", "29 202033 3 6106 3411.0 8801.0 9 5.0 \n", "... ... ... ... ... ... ... ... \n", "1867 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1868 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1869 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1870 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1871 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1872 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1873 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1874 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1875 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1876 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1877 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1878 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1879 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1880 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1881 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1882 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1883 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1884 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1885 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1886 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1887 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1888 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1889 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1890 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1891 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1892 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1893 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1894 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1895 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1896 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 42.0 FR France \n", "1 38.0 FR France \n", "2 40.0 FR France \n", "3 41.0 FR France \n", "4 40.0 FR France \n", "5 46.0 FR France \n", "6 39.0 FR France \n", "7 31.0 FR France \n", "8 39.0 FR France \n", "9 39.0 FR France \n", "10 31.0 FR France \n", "11 39.0 FR France \n", "12 32.0 FR France \n", "13 25.0 FR France \n", "14 26.0 FR France \n", "15 35.0 FR France \n", "16 45.0 FR France \n", "17 74.0 FR France \n", "18 77.0 FR France \n", "19 75.0 FR France \n", "20 61.0 FR France \n", "21 49.0 FR France \n", "22 37.0 FR France \n", "23 36.0 FR France \n", "24 46.0 FR France \n", "25 34.0 FR France \n", "26 20.0 FR France \n", "27 20.0 FR France \n", "28 14.0 FR France \n", "29 13.0 FR France \n", "... ... ... ... \n", "1867 59.0 FR France \n", "1868 64.0 FR France \n", "1869 97.0 FR France \n", "1870 93.0 FR France \n", "1871 80.0 FR France \n", "1872 116.0 FR France \n", "1873 149.0 FR France \n", "1874 281.0 FR France \n", "1875 395.0 FR France \n", "1876 485.0 FR France \n", "1877 544.0 FR France \n", "1878 689.0 FR France \n", "1879 722.0 FR France \n", "1880 762.0 FR France \n", "1881 926.0 FR France \n", "1882 1113.0 FR France \n", "1883 1236.0 FR France \n", "1884 832.0 FR France \n", "1885 459.0 FR France \n", "1886 207.0 FR France \n", "1887 190.0 FR France \n", "1888 198.0 FR France \n", "1889 224.0 FR France \n", "1890 266.0 FR France \n", "1891 219.0 FR France \n", "1892 176.0 FR France \n", "1893 163.0 FR France \n", "1894 195.0 FR France \n", "1895 308.0 FR France \n", "1896 213.0 FR France \n", "\n", "[1897 rows x 10 columns]" ] }, "execution_count": 18, "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": 16, "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
166019891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1660 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1660 FR France " ] }, "execution_count": 16, "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
020210932262117527.027715.03426.042.0FRFrance
120210832090316885.024921.03226.038.0FRFrance
220210732239318303.026483.03428.040.0FRFrance
320210632318319134.027232.03529.041.0FRFrance
420210532242618445.026407.03428.040.0FRFrance
520210432580421491.030117.03932.046.0FRFrance
620210332181017894.025726.03327.039.0FRFrance
720210231732013906.020734.02621.031.0FRFrance
820210132179917778.025820.03327.039.0FRFrance
920205332122016498.025942.03225.039.0FRFrance
1020205231642812285.020571.02519.031.0FRFrance
1120205132161917370.025868.03327.039.0FRFrance
1220205031684513220.020470.02620.032.0FRFrance
132020493129399923.015955.02015.025.0FRFrance
1420204831380410641.016967.02116.026.0FRFrance
1520204731908515285.022885.02923.035.0FRFrance
1620204632480120503.029099.03831.045.0FRFrance
1720204534251636857.048175.06556.074.0FRFrance
1820204434456738521.050613.06859.077.0FRFrance
1920204334373737523.049951.06657.075.0FRFrance
2020204233514529812.040478.05345.061.0FRFrance
2120204132787723206.032548.04235.049.0FRFrance
2220204032044316381.024505.03125.037.0FRFrance
2320203931981015900.023720.03024.036.0FRFrance
2420203832555921141.029977.03932.046.0FRFrance
2520203731848514649.022321.02822.034.0FRFrance
262020363103907646.013134.01612.020.0FRFrance
27202035399186842.012994.01510.020.0FRFrance
28202034360843090.09078.094.014.0FRFrance
29202033361063411.08801.095.013.0FRFrance
.................................
186719852132609619621.032571.04735.059.0FRFrance
186819852032789620885.034907.05138.064.0FRFrance
186919851934315432821.053487.07859.097.0FRFrance
187019851834055529935.051175.07455.093.0FRFrance
187119851733405324366.043740.06244.080.0FRFrance
187219851635036236451.064273.09166.0116.0FRFrance
187319851536388145538.082224.011683.0149.0FRFrance
18741985143134545114400.0154690.0244207.0281.0FRFrance
18751985133197206176080.0218332.0357319.0395.0FRFrance
18761985123245240223304.0267176.0445405.0485.0FRFrance
18771985113276205252399.0300011.0501458.0544.0FRFrance
18781985103353231326279.0380183.0640591.0689.0FRFrance
18791985093369895341109.0398681.0670618.0722.0FRFrance
18801985083389886359529.0420243.0707652.0762.0FRFrance
18811985073471852432599.0511105.0855784.0926.0FRFrance
18821985063565825518011.0613639.01026939.01113.0FRFrance
18831985053637302592795.0681809.011551074.01236.0FRFrance
18841985043424937390794.0459080.0770708.0832.0FRFrance
18851985033213901174689.0253113.0388317.0459.0FRFrance
188619850239758680949.0114223.0177147.0207.0FRFrance
188719850138548965918.0105060.0155120.0190.0FRFrance
188819845238483060602.0109058.0154110.0198.0FRFrance
1889198451310172680242.0123210.0185146.0224.0FRFrance
18901984503123680101401.0145959.0225184.0266.0FRFrance
1891198449310107381684.0120462.0184149.0219.0FRFrance
189219844837862060634.096606.0143110.0176.0FRFrance
189319844737202954274.089784.013199.0163.0FRFrance
189419844638733067686.0106974.0159123.0195.0FRFrance
18951984453135223101414.0169032.0246184.0308.0FRFrance
189619844436842220056.0116788.012537.0213.0FRFrance
\n", "

1896 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202109 3 22621 17527.0 27715.0 34 26.0 \n", "1 202108 3 20903 16885.0 24921.0 32 26.0 \n", "2 202107 3 22393 18303.0 26483.0 34 28.0 \n", "3 202106 3 23183 19134.0 27232.0 35 29.0 \n", "4 202105 3 22426 18445.0 26407.0 34 28.0 \n", "5 202104 3 25804 21491.0 30117.0 39 32.0 \n", "6 202103 3 21810 17894.0 25726.0 33 27.0 \n", "7 202102 3 17320 13906.0 20734.0 26 21.0 \n", "8 202101 3 21799 17778.0 25820.0 33 27.0 \n", "9 202053 3 21220 16498.0 25942.0 32 25.0 \n", "10 202052 3 16428 12285.0 20571.0 25 19.0 \n", "11 202051 3 21619 17370.0 25868.0 33 27.0 \n", "12 202050 3 16845 13220.0 20470.0 26 20.0 \n", "13 202049 3 12939 9923.0 15955.0 20 15.0 \n", "14 202048 3 13804 10641.0 16967.0 21 16.0 \n", "15 202047 3 19085 15285.0 22885.0 29 23.0 \n", "16 202046 3 24801 20503.0 29099.0 38 31.0 \n", "17 202045 3 42516 36857.0 48175.0 65 56.0 \n", "18 202044 3 44567 38521.0 50613.0 68 59.0 \n", "19 202043 3 43737 37523.0 49951.0 66 57.0 \n", "20 202042 3 35145 29812.0 40478.0 53 45.0 \n", "21 202041 3 27877 23206.0 32548.0 42 35.0 \n", "22 202040 3 20443 16381.0 24505.0 31 25.0 \n", "23 202039 3 19810 15900.0 23720.0 30 24.0 \n", "24 202038 3 25559 21141.0 29977.0 39 32.0 \n", "25 202037 3 18485 14649.0 22321.0 28 22.0 \n", "26 202036 3 10390 7646.0 13134.0 16 12.0 \n", "27 202035 3 9918 6842.0 12994.0 15 10.0 \n", "28 202034 3 6084 3090.0 9078.0 9 4.0 \n", "29 202033 3 6106 3411.0 8801.0 9 5.0 \n", "... ... ... ... ... ... ... ... \n", "1867 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1868 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1869 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1870 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1871 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1872 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1873 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1874 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1875 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1876 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1877 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1878 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1879 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1880 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1881 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1882 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1883 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1884 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1885 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1886 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1887 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1888 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1889 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1890 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1891 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1892 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1893 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1894 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1895 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1896 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 42.0 FR France \n", "1 38.0 FR France \n", "2 40.0 FR France \n", "3 41.0 FR France \n", "4 40.0 FR France \n", "5 46.0 FR France \n", "6 39.0 FR France \n", "7 31.0 FR France \n", "8 39.0 FR France \n", "9 39.0 FR France \n", "10 31.0 FR France \n", "11 39.0 FR France \n", "12 32.0 FR France \n", "13 25.0 FR France \n", "14 26.0 FR France \n", "15 35.0 FR France \n", "16 45.0 FR France \n", "17 74.0 FR France \n", "18 77.0 FR France \n", "19 75.0 FR France \n", "20 61.0 FR France \n", "21 49.0 FR France \n", "22 37.0 FR France \n", "23 36.0 FR France \n", "24 46.0 FR France \n", "25 34.0 FR France \n", "26 20.0 FR France \n", "27 20.0 FR France \n", "28 14.0 FR France \n", "29 13.0 FR France \n", "... ... ... ... \n", "1867 59.0 FR France \n", "1868 64.0 FR France \n", "1869 97.0 FR France \n", "1870 93.0 FR France \n", "1871 80.0 FR France \n", "1872 116.0 FR France \n", "1873 149.0 FR France \n", "1874 281.0 FR France \n", "1875 395.0 FR France \n", "1876 485.0 FR France \n", "1877 544.0 FR France \n", "1878 689.0 FR France \n", "1879 722.0 FR France \n", "1880 762.0 FR France \n", "1881 926.0 FR France \n", "1882 1113.0 FR France \n", "1883 1236.0 FR France \n", "1884 832.0 FR France \n", "1885 459.0 FR France \n", "1886 207.0 FR France \n", "1887 190.0 FR France \n", "1888 198.0 FR France \n", "1889 224.0 FR France \n", "1890 266.0 FR France \n", "1891 219.0 FR France \n", "1892 176.0 FR France \n", "1893 163.0 FR France \n", "1894 195.0 FR France \n", "1895 308.0 FR France \n", "1896 213.0 FR France \n", "\n", "[1896 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", "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2053781\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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