{ "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\n", "import os\n", "import requests" ] }, { "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, and store them in case either the URL or the format ever change, 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": [ "local_data_file_name = \"incidence-PAY-3.csv\"\n", "if not os.path.isfile(local_data_file_name) or os.path.getsize(local_data_file_name) == 0:\n", " with open(local_data_file_name, 'wb') as local_data_file:\n", " data_url = f\"http://www.sentiweb.fr/datasets/{local_data_file_name}\"\n", " r = requests.get(data_url)\n", " local_data_file.write(r.content)" ] }, { "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": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02021173126239325.015921.01914.024.0FRFrance
120211631650512735.020275.02519.031.0FRFrance
220211531930615398.023214.02923.035.0FRFrance
320211432107317099.025047.03226.038.0FRFrance
420211332641322094.030732.04033.047.0FRFrance
520211233065825919.035397.04639.053.0FRFrance
620211132498820718.029258.03832.044.0FRFrance
720211031953915951.023127.03025.035.0FRFrance
820210931757213926.021218.02721.033.0FRFrance
920210832088216907.024857.03226.038.0FRFrance
1020210732239318303.026483.03428.040.0FRFrance
1120210632318319134.027232.03529.041.0FRFrance
1220210532242618445.026407.03428.040.0FRFrance
1320210432580421491.030117.03932.046.0FRFrance
1420210332181017894.025726.03327.039.0FRFrance
1520210231732013906.020734.02621.031.0FRFrance
1620210132179917778.025820.03327.039.0FRFrance
1720205332122016498.025942.03225.039.0FRFrance
1820205231642812285.020571.02519.031.0FRFrance
1920205132161917370.025868.03327.039.0FRFrance
2020205031684513220.020470.02620.032.0FRFrance
212020493129399923.015955.02015.025.0FRFrance
2220204831380410641.016967.02116.026.0FRFrance
2320204731908515285.022885.02923.035.0FRFrance
2420204632480120503.029099.03831.045.0FRFrance
2520204534251636857.048175.06556.074.0FRFrance
2620204434456738521.050613.06859.077.0FRFrance
2720204334373737523.049951.06657.075.0FRFrance
2820204233514529812.040478.05345.061.0FRFrance
2920204132787723206.032548.04235.049.0FRFrance
.................................
187519852132609619621.032571.04735.059.0FRFrance
187619852032789620885.034907.05138.064.0FRFrance
187719851934315432821.053487.07859.097.0FRFrance
187819851834055529935.051175.07455.093.0FRFrance
187919851733405324366.043740.06244.080.0FRFrance
188019851635036236451.064273.09166.0116.0FRFrance
188119851536388145538.082224.011683.0149.0FRFrance
18821985143134545114400.0154690.0244207.0281.0FRFrance
18831985133197206176080.0218332.0357319.0395.0FRFrance
18841985123245240223304.0267176.0445405.0485.0FRFrance
18851985113276205252399.0300011.0501458.0544.0FRFrance
18861985103353231326279.0380183.0640591.0689.0FRFrance
18871985093369895341109.0398681.0670618.0722.0FRFrance
18881985083389886359529.0420243.0707652.0762.0FRFrance
18891985073471852432599.0511105.0855784.0926.0FRFrance
18901985063565825518011.0613639.01026939.01113.0FRFrance
18911985053637302592795.0681809.011551074.01236.0FRFrance
18921985043424937390794.0459080.0770708.0832.0FRFrance
18931985033213901174689.0253113.0388317.0459.0FRFrance
189419850239758680949.0114223.0177147.0207.0FRFrance
189519850138548965918.0105060.0155120.0190.0FRFrance
189619845238483060602.0109058.0154110.0198.0FRFrance
1897198451310172680242.0123210.0185146.0224.0FRFrance
18981984503123680101401.0145959.0225184.0266.0FRFrance
1899198449310107381684.0120462.0184149.0219.0FRFrance
190019844837862060634.096606.0143110.0176.0FRFrance
190119844737202954274.089784.013199.0163.0FRFrance
190219844638733067686.0106974.0159123.0195.0FRFrance
19031984453135223101414.0169032.0246184.0308.0FRFrance
190419844436842220056.0116788.012537.0213.0FRFrance
\n", "

1905 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202117 3 12623 9325.0 15921.0 19 14.0 \n", "1 202116 3 16505 12735.0 20275.0 25 19.0 \n", "2 202115 3 19306 15398.0 23214.0 29 23.0 \n", "3 202114 3 21073 17099.0 25047.0 32 26.0 \n", "4 202113 3 26413 22094.0 30732.0 40 33.0 \n", "5 202112 3 30658 25919.0 35397.0 46 39.0 \n", "6 202111 3 24988 20718.0 29258.0 38 32.0 \n", "7 202110 3 19539 15951.0 23127.0 30 25.0 \n", "8 202109 3 17572 13926.0 21218.0 27 21.0 \n", "9 202108 3 20882 16907.0 24857.0 32 26.0 \n", "10 202107 3 22393 18303.0 26483.0 34 28.0 \n", "11 202106 3 23183 19134.0 27232.0 35 29.0 \n", "12 202105 3 22426 18445.0 26407.0 34 28.0 \n", "13 202104 3 25804 21491.0 30117.0 39 32.0 \n", "14 202103 3 21810 17894.0 25726.0 33 27.0 \n", "15 202102 3 17320 13906.0 20734.0 26 21.0 \n", "16 202101 3 21799 17778.0 25820.0 33 27.0 \n", "17 202053 3 21220 16498.0 25942.0 32 25.0 \n", "18 202052 3 16428 12285.0 20571.0 25 19.0 \n", "19 202051 3 21619 17370.0 25868.0 33 27.0 \n", "20 202050 3 16845 13220.0 20470.0 26 20.0 \n", "21 202049 3 12939 9923.0 15955.0 20 15.0 \n", "22 202048 3 13804 10641.0 16967.0 21 16.0 \n", "23 202047 3 19085 15285.0 22885.0 29 23.0 \n", "24 202046 3 24801 20503.0 29099.0 38 31.0 \n", "25 202045 3 42516 36857.0 48175.0 65 56.0 \n", "26 202044 3 44567 38521.0 50613.0 68 59.0 \n", "27 202043 3 43737 37523.0 49951.0 66 57.0 \n", "28 202042 3 35145 29812.0 40478.0 53 45.0 \n", "29 202041 3 27877 23206.0 32548.0 42 35.0 \n", "... ... ... ... ... ... ... ... \n", "1875 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1876 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1877 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1878 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1879 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1880 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1881 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1882 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1883 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1884 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1885 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1886 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1887 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1888 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1889 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1890 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1891 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1892 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1893 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1894 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1895 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1896 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1897 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1898 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1899 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1900 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1901 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1902 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1903 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1904 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 24.0 FR France \n", "1 31.0 FR France \n", "2 35.0 FR France \n", "3 38.0 FR France \n", "4 47.0 FR France \n", "5 53.0 FR France \n", "6 44.0 FR France \n", "7 35.0 FR France \n", "8 33.0 FR France \n", "9 38.0 FR France \n", "10 40.0 FR France \n", "11 41.0 FR France \n", "12 40.0 FR France \n", "13 46.0 FR France \n", "14 39.0 FR France \n", "15 31.0 FR France \n", "16 39.0 FR France \n", "17 39.0 FR France \n", "18 31.0 FR France \n", "19 39.0 FR France \n", "20 32.0 FR France \n", "21 25.0 FR France \n", "22 26.0 FR France \n", "23 35.0 FR France \n", "24 45.0 FR France \n", "25 74.0 FR France \n", "26 77.0 FR France \n", "27 75.0 FR France \n", "28 61.0 FR France \n", "29 49.0 FR France \n", "... ... ... ... \n", "1875 59.0 FR France \n", "1876 64.0 FR France \n", "1877 97.0 FR France \n", "1878 93.0 FR France \n", "1879 80.0 FR France \n", "1880 116.0 FR France \n", "1881 149.0 FR France \n", "1882 281.0 FR France \n", "1883 395.0 FR France \n", "1884 485.0 FR France \n", "1885 544.0 FR France \n", "1886 689.0 FR France \n", "1887 722.0 FR France \n", "1888 762.0 FR France \n", "1889 926.0 FR France \n", "1890 1113.0 FR France \n", "1891 1236.0 FR France \n", "1892 832.0 FR France \n", "1893 459.0 FR France \n", "1894 207.0 FR France \n", "1895 190.0 FR France \n", "1896 198.0 FR France \n", "1897 224.0 FR France \n", "1898 266.0 FR France \n", "1899 219.0 FR France \n", "1900 176.0 FR France \n", "1901 163.0 FR France \n", "1902 195.0 FR France \n", "1903 308.0 FR France \n", "1904 213.0 FR France \n", "\n", "[1905 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(local_data_file_name, 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": [ "
\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
166819891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1668 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1668 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
02021173126239325.015921.01914.024.0FRFrance
120211631650512735.020275.02519.031.0FRFrance
220211531930615398.023214.02923.035.0FRFrance
320211432107317099.025047.03226.038.0FRFrance
420211332641322094.030732.04033.047.0FRFrance
520211233065825919.035397.04639.053.0FRFrance
620211132498820718.029258.03832.044.0FRFrance
720211031953915951.023127.03025.035.0FRFrance
820210931757213926.021218.02721.033.0FRFrance
920210832088216907.024857.03226.038.0FRFrance
1020210732239318303.026483.03428.040.0FRFrance
1120210632318319134.027232.03529.041.0FRFrance
1220210532242618445.026407.03428.040.0FRFrance
1320210432580421491.030117.03932.046.0FRFrance
1420210332181017894.025726.03327.039.0FRFrance
1520210231732013906.020734.02621.031.0FRFrance
1620210132179917778.025820.03327.039.0FRFrance
1720205332122016498.025942.03225.039.0FRFrance
1820205231642812285.020571.02519.031.0FRFrance
1920205132161917370.025868.03327.039.0FRFrance
2020205031684513220.020470.02620.032.0FRFrance
212020493129399923.015955.02015.025.0FRFrance
2220204831380410641.016967.02116.026.0FRFrance
2320204731908515285.022885.02923.035.0FRFrance
2420204632480120503.029099.03831.045.0FRFrance
2520204534251636857.048175.06556.074.0FRFrance
2620204434456738521.050613.06859.077.0FRFrance
2720204334373737523.049951.06657.075.0FRFrance
2820204233514529812.040478.05345.061.0FRFrance
2920204132787723206.032548.04235.049.0FRFrance
.................................
187519852132609619621.032571.04735.059.0FRFrance
187619852032789620885.034907.05138.064.0FRFrance
187719851934315432821.053487.07859.097.0FRFrance
187819851834055529935.051175.07455.093.0FRFrance
187919851733405324366.043740.06244.080.0FRFrance
188019851635036236451.064273.09166.0116.0FRFrance
188119851536388145538.082224.011683.0149.0FRFrance
18821985143134545114400.0154690.0244207.0281.0FRFrance
18831985133197206176080.0218332.0357319.0395.0FRFrance
18841985123245240223304.0267176.0445405.0485.0FRFrance
18851985113276205252399.0300011.0501458.0544.0FRFrance
18861985103353231326279.0380183.0640591.0689.0FRFrance
18871985093369895341109.0398681.0670618.0722.0FRFrance
18881985083389886359529.0420243.0707652.0762.0FRFrance
18891985073471852432599.0511105.0855784.0926.0FRFrance
18901985063565825518011.0613639.01026939.01113.0FRFrance
18911985053637302592795.0681809.011551074.01236.0FRFrance
18921985043424937390794.0459080.0770708.0832.0FRFrance
18931985033213901174689.0253113.0388317.0459.0FRFrance
189419850239758680949.0114223.0177147.0207.0FRFrance
189519850138548965918.0105060.0155120.0190.0FRFrance
189619845238483060602.0109058.0154110.0198.0FRFrance
1897198451310172680242.0123210.0185146.0224.0FRFrance
18981984503123680101401.0145959.0225184.0266.0FRFrance
1899198449310107381684.0120462.0184149.0219.0FRFrance
190019844837862060634.096606.0143110.0176.0FRFrance
190119844737202954274.089784.013199.0163.0FRFrance
190219844638733067686.0106974.0159123.0195.0FRFrance
19031984453135223101414.0169032.0246184.0308.0FRFrance
190419844436842220056.0116788.012537.0213.0FRFrance
\n", "

1904 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202117 3 12623 9325.0 15921.0 19 14.0 \n", "1 202116 3 16505 12735.0 20275.0 25 19.0 \n", "2 202115 3 19306 15398.0 23214.0 29 23.0 \n", "3 202114 3 21073 17099.0 25047.0 32 26.0 \n", "4 202113 3 26413 22094.0 30732.0 40 33.0 \n", "5 202112 3 30658 25919.0 35397.0 46 39.0 \n", "6 202111 3 24988 20718.0 29258.0 38 32.0 \n", "7 202110 3 19539 15951.0 23127.0 30 25.0 \n", "8 202109 3 17572 13926.0 21218.0 27 21.0 \n", "9 202108 3 20882 16907.0 24857.0 32 26.0 \n", "10 202107 3 22393 18303.0 26483.0 34 28.0 \n", "11 202106 3 23183 19134.0 27232.0 35 29.0 \n", "12 202105 3 22426 18445.0 26407.0 34 28.0 \n", "13 202104 3 25804 21491.0 30117.0 39 32.0 \n", "14 202103 3 21810 17894.0 25726.0 33 27.0 \n", "15 202102 3 17320 13906.0 20734.0 26 21.0 \n", "16 202101 3 21799 17778.0 25820.0 33 27.0 \n", "17 202053 3 21220 16498.0 25942.0 32 25.0 \n", "18 202052 3 16428 12285.0 20571.0 25 19.0 \n", "19 202051 3 21619 17370.0 25868.0 33 27.0 \n", "20 202050 3 16845 13220.0 20470.0 26 20.0 \n", "21 202049 3 12939 9923.0 15955.0 20 15.0 \n", "22 202048 3 13804 10641.0 16967.0 21 16.0 \n", "23 202047 3 19085 15285.0 22885.0 29 23.0 \n", "24 202046 3 24801 20503.0 29099.0 38 31.0 \n", "25 202045 3 42516 36857.0 48175.0 65 56.0 \n", "26 202044 3 44567 38521.0 50613.0 68 59.0 \n", "27 202043 3 43737 37523.0 49951.0 66 57.0 \n", "28 202042 3 35145 29812.0 40478.0 53 45.0 \n", "29 202041 3 27877 23206.0 32548.0 42 35.0 \n", "... ... ... ... ... ... ... ... \n", "1875 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1876 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1877 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1878 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1879 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1880 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1881 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1882 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1883 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1884 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1885 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1886 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1887 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1888 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1889 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1890 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1891 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1892 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1893 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1894 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1895 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1896 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1897 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1898 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1899 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1900 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1901 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1902 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1903 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1904 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 24.0 FR France \n", "1 31.0 FR France \n", "2 35.0 FR France \n", "3 38.0 FR France \n", "4 47.0 FR France \n", "5 53.0 FR France \n", "6 44.0 FR France \n", "7 35.0 FR France \n", "8 33.0 FR France \n", "9 38.0 FR France \n", "10 40.0 FR France \n", "11 41.0 FR France \n", "12 40.0 FR France \n", "13 46.0 FR France \n", "14 39.0 FR France \n", "15 31.0 FR France \n", "16 39.0 FR France \n", "17 39.0 FR France \n", "18 31.0 FR France \n", "19 39.0 FR France \n", "20 32.0 FR France \n", "21 25.0 FR France \n", "22 26.0 FR France \n", "23 35.0 FR France \n", "24 45.0 FR France \n", "25 74.0 FR France \n", "26 77.0 FR France \n", "27 75.0 FR France \n", "28 61.0 FR France \n", "29 49.0 FR France \n", "... ... ... ... \n", "1875 59.0 FR France \n", "1876 64.0 FR France \n", "1877 97.0 FR France \n", "1878 93.0 FR France \n", "1879 80.0 FR France \n", "1880 116.0 FR France \n", "1881 149.0 FR France \n", "1882 281.0 FR France \n", "1883 395.0 FR France \n", "1884 485.0 FR France \n", "1885 544.0 FR France \n", "1886 689.0 FR France \n", "1887 722.0 FR France \n", "1888 762.0 FR France \n", "1889 926.0 FR France \n", "1890 1113.0 FR France \n", "1891 1236.0 FR France \n", "1892 832.0 FR France \n", "1893 459.0 FR France \n", "1894 207.0 FR France \n", "1895 190.0 FR France \n", "1896 198.0 FR France \n", "1897 224.0 FR France \n", "1898 266.0 FR France \n", "1899 219.0 FR France \n", "1900 176.0 FR France \n", "1901 163.0 FR France \n", "1902 195.0 FR France \n", "1903 308.0 FR France \n", "1904 213.0 FR France \n", "\n", "[1904 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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