{ "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": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020214532068716810.024564.03125.037.0FRFrance
120214431901715056.022978.02923.035.0FRFrance
220214332704021935.032145.04133.049.0FRFrance
320214232834323382.033304.04335.051.0FRFrance
420214132504320586.029500.03831.045.0FRFrance
520214032628621842.030730.04033.047.0FRFrance
620213932215518014.026296.03428.040.0FRFrance
720213831561412310.018918.02419.029.0FRFrance
820213731367310404.016942.02116.026.0FRFrance
92021363102897505.013073.01612.020.0FRFrance
102021353126099282.015936.01914.024.0FRFrance
112021343130159485.016545.02015.025.0FRFrance
122021333103927042.013742.01611.021.0FRFrance
1320213231558611009.020163.02417.031.0FRFrance
1420213131885513664.024046.02921.037.0FRFrance
152021303139919695.018287.02114.028.0FRFrance
162021293136269618.017634.02115.027.0FRFrance
17202128386365430.011842.0138.018.0FRFrance
182021273106936838.014548.01610.022.0FRFrance
19202126370864109.010063.0116.016.0FRFrance
20202125379425540.010344.0128.016.0FRFrance
21202124348553011.06699.074.010.0FRFrance
22202123367104455.08965.0107.013.0FRFrance
23202122378795495.010263.0128.016.0FRFrance
24202121378275403.010251.0128.016.0FRFrance
252021203102787540.013016.01612.020.0FRFrance
26202119395396860.012218.01410.018.0FRFrance
272021183121359165.015105.01814.022.0FRFrance
282021173120588891.015225.01813.023.0FRFrance
2920211631650512735.020275.02519.031.0FRFrance
.................................
190319852132609619621.032571.04735.059.0FRFrance
190419852032789620885.034907.05138.064.0FRFrance
190519851934315432821.053487.07859.097.0FRFrance
190619851834055529935.051175.07455.093.0FRFrance
190719851733405324366.043740.06244.080.0FRFrance
190819851635036236451.064273.09166.0116.0FRFrance
190919851536388145538.082224.011683.0149.0FRFrance
19101985143134545114400.0154690.0244207.0281.0FRFrance
19111985133197206176080.0218332.0357319.0395.0FRFrance
19121985123245240223304.0267176.0445405.0485.0FRFrance
19131985113276205252399.0300011.0501458.0544.0FRFrance
19141985103353231326279.0380183.0640591.0689.0FRFrance
19151985093369895341109.0398681.0670618.0722.0FRFrance
19161985083389886359529.0420243.0707652.0762.0FRFrance
19171985073471852432599.0511105.0855784.0926.0FRFrance
19181985063565825518011.0613639.01026939.01113.0FRFrance
19191985053637302592795.0681809.011551074.01236.0FRFrance
19201985043424937390794.0459080.0770708.0832.0FRFrance
19211985033213901174689.0253113.0388317.0459.0FRFrance
192219850239758680949.0114223.0177147.0207.0FRFrance
192319850138548965918.0105060.0155120.0190.0FRFrance
192419845238483060602.0109058.0154110.0198.0FRFrance
1925198451310172680242.0123210.0185146.0224.0FRFrance
19261984503123680101401.0145959.0225184.0266.0FRFrance
1927198449310107381684.0120462.0184149.0219.0FRFrance
192819844837862060634.096606.0143110.0176.0FRFrance
192919844737202954274.089784.013199.0163.0FRFrance
193019844638733067686.0106974.0159123.0195.0FRFrance
19311984453135223101414.0169032.0246184.0308.0FRFrance
193219844436842220056.0116788.012537.0213.0FRFrance
\n", "

1933 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202145 3 20687 16810.0 24564.0 31 25.0 \n", "1 202144 3 19017 15056.0 22978.0 29 23.0 \n", "2 202143 3 27040 21935.0 32145.0 41 33.0 \n", "3 202142 3 28343 23382.0 33304.0 43 35.0 \n", "4 202141 3 25043 20586.0 29500.0 38 31.0 \n", "5 202140 3 26286 21842.0 30730.0 40 33.0 \n", "6 202139 3 22155 18014.0 26296.0 34 28.0 \n", "7 202138 3 15614 12310.0 18918.0 24 19.0 \n", "8 202137 3 13673 10404.0 16942.0 21 16.0 \n", "9 202136 3 10289 7505.0 13073.0 16 12.0 \n", "10 202135 3 12609 9282.0 15936.0 19 14.0 \n", "11 202134 3 13015 9485.0 16545.0 20 15.0 \n", "12 202133 3 10392 7042.0 13742.0 16 11.0 \n", "13 202132 3 15586 11009.0 20163.0 24 17.0 \n", "14 202131 3 18855 13664.0 24046.0 29 21.0 \n", "15 202130 3 13991 9695.0 18287.0 21 14.0 \n", "16 202129 3 13626 9618.0 17634.0 21 15.0 \n", "17 202128 3 8636 5430.0 11842.0 13 8.0 \n", "18 202127 3 10693 6838.0 14548.0 16 10.0 \n", "19 202126 3 7086 4109.0 10063.0 11 6.0 \n", "20 202125 3 7942 5540.0 10344.0 12 8.0 \n", "21 202124 3 4855 3011.0 6699.0 7 4.0 \n", "22 202123 3 6710 4455.0 8965.0 10 7.0 \n", "23 202122 3 7879 5495.0 10263.0 12 8.0 \n", "24 202121 3 7827 5403.0 10251.0 12 8.0 \n", "25 202120 3 10278 7540.0 13016.0 16 12.0 \n", "26 202119 3 9539 6860.0 12218.0 14 10.0 \n", "27 202118 3 12135 9165.0 15105.0 18 14.0 \n", "28 202117 3 12058 8891.0 15225.0 18 13.0 \n", "29 202116 3 16505 12735.0 20275.0 25 19.0 \n", "... ... ... ... ... ... ... ... \n", "1903 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1904 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1905 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1906 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1907 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1908 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1909 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1910 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1911 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1912 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1913 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1914 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1915 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1916 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1917 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1918 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1919 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1920 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1921 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1922 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1923 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1924 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1925 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1926 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1927 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1928 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1929 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1930 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1931 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1932 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 37.0 FR France \n", "1 35.0 FR France \n", "2 49.0 FR France \n", "3 51.0 FR France \n", "4 45.0 FR France \n", "5 47.0 FR France \n", "6 40.0 FR France \n", "7 29.0 FR France \n", "8 26.0 FR France \n", "9 20.0 FR France \n", "10 24.0 FR France \n", "11 25.0 FR France \n", "12 21.0 FR France \n", "13 31.0 FR France \n", "14 37.0 FR France \n", "15 28.0 FR France \n", "16 27.0 FR France \n", "17 18.0 FR France \n", "18 22.0 FR France \n", "19 16.0 FR France \n", "20 16.0 FR France \n", "21 10.0 FR France \n", "22 13.0 FR France \n", "23 16.0 FR France \n", "24 16.0 FR France \n", "25 20.0 FR France \n", "26 18.0 FR France \n", "27 22.0 FR France \n", "28 23.0 FR France \n", "29 31.0 FR France \n", "... ... ... ... \n", "1903 59.0 FR France \n", "1904 64.0 FR France \n", "1905 97.0 FR France \n", "1906 93.0 FR France \n", "1907 80.0 FR France \n", "1908 116.0 FR France \n", "1909 149.0 FR France \n", "1910 281.0 FR France \n", "1911 395.0 FR France \n", "1912 485.0 FR France \n", "1913 544.0 FR France \n", "1914 689.0 FR France \n", "1915 722.0 FR France \n", "1916 762.0 FR France \n", "1917 926.0 FR France \n", "1918 1113.0 FR France \n", "1919 1236.0 FR France \n", "1920 832.0 FR France \n", "1921 459.0 FR France \n", "1922 207.0 FR France \n", "1923 190.0 FR France \n", "1924 198.0 FR France \n", "1925 224.0 FR France \n", "1926 266.0 FR France \n", "1927 219.0 FR France \n", "1928 176.0 FR France \n", "1929 163.0 FR France \n", "1930 195.0 FR France \n", "1931 308.0 FR France \n", "1932 213.0 FR France \n", "\n", "[1933 rows x 10 columns]" ] }, "execution_count": 3, "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": 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
169619891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1696 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1696 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
020214532068716810.024564.03125.037.0FRFrance
120214431901715056.022978.02923.035.0FRFrance
220214332704021935.032145.04133.049.0FRFrance
320214232834323382.033304.04335.051.0FRFrance
420214132504320586.029500.03831.045.0FRFrance
520214032628621842.030730.04033.047.0FRFrance
620213932215518014.026296.03428.040.0FRFrance
720213831561412310.018918.02419.029.0FRFrance
820213731367310404.016942.02116.026.0FRFrance
92021363102897505.013073.01612.020.0FRFrance
102021353126099282.015936.01914.024.0FRFrance
112021343130159485.016545.02015.025.0FRFrance
122021333103927042.013742.01611.021.0FRFrance
1320213231558611009.020163.02417.031.0FRFrance
1420213131885513664.024046.02921.037.0FRFrance
152021303139919695.018287.02114.028.0FRFrance
162021293136269618.017634.02115.027.0FRFrance
17202128386365430.011842.0138.018.0FRFrance
182021273106936838.014548.01610.022.0FRFrance
19202126370864109.010063.0116.016.0FRFrance
20202125379425540.010344.0128.016.0FRFrance
21202124348553011.06699.074.010.0FRFrance
22202123367104455.08965.0107.013.0FRFrance
23202122378795495.010263.0128.016.0FRFrance
24202121378275403.010251.0128.016.0FRFrance
252021203102787540.013016.01612.020.0FRFrance
26202119395396860.012218.01410.018.0FRFrance
272021183121359165.015105.01814.022.0FRFrance
282021173120588891.015225.01813.023.0FRFrance
2920211631650512735.020275.02519.031.0FRFrance
.................................
190319852132609619621.032571.04735.059.0FRFrance
190419852032789620885.034907.05138.064.0FRFrance
190519851934315432821.053487.07859.097.0FRFrance
190619851834055529935.051175.07455.093.0FRFrance
190719851733405324366.043740.06244.080.0FRFrance
190819851635036236451.064273.09166.0116.0FRFrance
190919851536388145538.082224.011683.0149.0FRFrance
19101985143134545114400.0154690.0244207.0281.0FRFrance
19111985133197206176080.0218332.0357319.0395.0FRFrance
19121985123245240223304.0267176.0445405.0485.0FRFrance
19131985113276205252399.0300011.0501458.0544.0FRFrance
19141985103353231326279.0380183.0640591.0689.0FRFrance
19151985093369895341109.0398681.0670618.0722.0FRFrance
19161985083389886359529.0420243.0707652.0762.0FRFrance
19171985073471852432599.0511105.0855784.0926.0FRFrance
19181985063565825518011.0613639.01026939.01113.0FRFrance
19191985053637302592795.0681809.011551074.01236.0FRFrance
19201985043424937390794.0459080.0770708.0832.0FRFrance
19211985033213901174689.0253113.0388317.0459.0FRFrance
192219850239758680949.0114223.0177147.0207.0FRFrance
192319850138548965918.0105060.0155120.0190.0FRFrance
192419845238483060602.0109058.0154110.0198.0FRFrance
1925198451310172680242.0123210.0185146.0224.0FRFrance
19261984503123680101401.0145959.0225184.0266.0FRFrance
1927198449310107381684.0120462.0184149.0219.0FRFrance
192819844837862060634.096606.0143110.0176.0FRFrance
192919844737202954274.089784.013199.0163.0FRFrance
193019844638733067686.0106974.0159123.0195.0FRFrance
19311984453135223101414.0169032.0246184.0308.0FRFrance
193219844436842220056.0116788.012537.0213.0FRFrance
\n", "

1932 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202145 3 20687 16810.0 24564.0 31 25.0 \n", "1 202144 3 19017 15056.0 22978.0 29 23.0 \n", "2 202143 3 27040 21935.0 32145.0 41 33.0 \n", "3 202142 3 28343 23382.0 33304.0 43 35.0 \n", "4 202141 3 25043 20586.0 29500.0 38 31.0 \n", "5 202140 3 26286 21842.0 30730.0 40 33.0 \n", "6 202139 3 22155 18014.0 26296.0 34 28.0 \n", "7 202138 3 15614 12310.0 18918.0 24 19.0 \n", "8 202137 3 13673 10404.0 16942.0 21 16.0 \n", "9 202136 3 10289 7505.0 13073.0 16 12.0 \n", "10 202135 3 12609 9282.0 15936.0 19 14.0 \n", "11 202134 3 13015 9485.0 16545.0 20 15.0 \n", "12 202133 3 10392 7042.0 13742.0 16 11.0 \n", "13 202132 3 15586 11009.0 20163.0 24 17.0 \n", "14 202131 3 18855 13664.0 24046.0 29 21.0 \n", "15 202130 3 13991 9695.0 18287.0 21 14.0 \n", "16 202129 3 13626 9618.0 17634.0 21 15.0 \n", "17 202128 3 8636 5430.0 11842.0 13 8.0 \n", "18 202127 3 10693 6838.0 14548.0 16 10.0 \n", "19 202126 3 7086 4109.0 10063.0 11 6.0 \n", "20 202125 3 7942 5540.0 10344.0 12 8.0 \n", "21 202124 3 4855 3011.0 6699.0 7 4.0 \n", "22 202123 3 6710 4455.0 8965.0 10 7.0 \n", "23 202122 3 7879 5495.0 10263.0 12 8.0 \n", "24 202121 3 7827 5403.0 10251.0 12 8.0 \n", "25 202120 3 10278 7540.0 13016.0 16 12.0 \n", "26 202119 3 9539 6860.0 12218.0 14 10.0 \n", "27 202118 3 12135 9165.0 15105.0 18 14.0 \n", "28 202117 3 12058 8891.0 15225.0 18 13.0 \n", "29 202116 3 16505 12735.0 20275.0 25 19.0 \n", "... ... ... ... ... ... ... ... \n", "1903 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1904 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1905 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1906 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1907 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1908 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1909 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1910 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1911 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1912 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1913 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1914 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1915 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1916 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1917 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1918 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1919 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1920 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1921 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1922 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1923 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1924 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1925 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1926 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1927 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1928 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1929 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1930 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1931 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1932 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 37.0 FR France \n", "1 35.0 FR France \n", "2 49.0 FR France \n", "3 51.0 FR France \n", "4 45.0 FR France \n", "5 47.0 FR France \n", "6 40.0 FR France \n", "7 29.0 FR France \n", "8 26.0 FR France \n", "9 20.0 FR France \n", "10 24.0 FR France \n", "11 25.0 FR France \n", "12 21.0 FR France \n", "13 31.0 FR France \n", "14 37.0 FR France \n", "15 28.0 FR France \n", "16 27.0 FR France \n", "17 18.0 FR France \n", "18 22.0 FR France \n", "19 16.0 FR France \n", "20 16.0 FR France \n", "21 10.0 FR France \n", "22 13.0 FR France \n", "23 16.0 FR France \n", "24 16.0 FR France \n", "25 20.0 FR France \n", "26 18.0 FR France \n", "27 22.0 FR France \n", "28 23.0 FR France \n", "29 31.0 FR France \n", "... ... ... ... \n", "1903 59.0 FR France \n", "1904 64.0 FR France \n", "1905 97.0 FR France \n", "1906 93.0 FR France \n", "1907 80.0 FR France \n", "1908 116.0 FR France \n", "1909 149.0 FR France \n", "1910 281.0 FR France \n", "1911 395.0 FR France \n", "1912 485.0 FR France \n", "1913 544.0 FR France \n", "1914 689.0 FR France \n", "1915 722.0 FR France \n", "1916 762.0 FR France \n", "1917 926.0 FR France \n", "1918 1113.0 FR France \n", "1919 1236.0 FR France \n", "1920 832.0 FR France \n", "1921 459.0 FR France \n", "1922 207.0 FR France \n", "1923 190.0 FR France \n", "1924 198.0 FR France \n", "1925 224.0 FR France \n", "1926 266.0 FR France \n", "1927 219.0 FR France \n", "1928 176.0 FR France \n", "1929 163.0 FR France \n", "1930 195.0 FR France \n", "1931 308.0 FR France \n", "1932 213.0 FR France \n", "\n", "[1932 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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2Pmerrsn5nK26fuQr0xVzB/C0tvuHAnfWG8fMzLpVprBfDxwu6RmS9gZeB/xjb2KZmVlVHXfFRMQeSe8EvgIsAT4TETd3Me8Fu2v6qMnZoNn5nK26JudztuoWPV/HB0/NzOyJwVeempllxoXdzCwzLuxmZpl5QhZ2Saslre53jtlIeqak90g6od9ZZmpyNmh2Pmerrsn5mpwNqud7QhV2SUOSrgauAD4u6SX9ztRO0n8Evkb6Lp23SXp7nyP9QpOzQbPzOVt1Tc7X5GzQZb6IaPQfsG/b7dcCnyhuvxn4B2BNcV99yHYC8Izp+QMfAE4r7r8I+BIw3I98Tc7W9HzOlme+JmerO18jt9glHSDpryTdCnxC0mHFoN8BflTcHgN+CJwx/bRFzHeUpO8B/w34rKQTIrX2UcAhABFxHfBN4C2Lma/J2Zqez9nyzNfkbL3K18jCDpwI7EtasEeAD0haTtoteTVARDwMXAy8pLj/WK/CSDpU0gFtD50CXBIRLyV9wLxB0uHAhdP5CpcBR0vap1f5mpyt6fmcLc98Tc62WPn6VtiVLJX0Vklfl3SWpGcVg58NPBIRe4A/A34KnAZ8FXiKpF8rxrsV+LGk43qU8UhJXwa+AXxY0vTXFP8/YL/i9heBu4B1pE/UJ7ftYdxH+nbL5/0qZWt6PmfLM1+Tsy12vr4V9mJX4zeBNwEfA/YB/qYYfBdwd/HJ9GPSwjyL1ADf5/GvBV4G3Fs8XgtJK9ruPh+4IyKGgCuBTxSP3wc8LGn/iLgP+FfgqUWObwLvLcbbG/g5sD33bE3P52x55mtytn7mW7TCLuk4SR+VdHpxX8CRwBUR8aWI+BhwmKQXAztIn2BHFk/fBgwUj/0F8CpJryZ9KAwC3+0y25MknS/pemCjpIOKfGuAayUpIv4RuF/SOtKewv7FcIr7BwOPkfYwDpb0N8BFwJ6IuDvHbE3P52zVNTlfk7M1Jd+iFHZJzwH+EngA+D1J7y3mvRp4oFhogPOBN5AK9R7gxcXjN5COGD8UEdcAG4DTgeOB/x4Rj7VNo4qXFvN7FemgxNnAAaQvOzuk2LsAuKDI9+1iWV4JEBHfKqaxNCK2AWcCNwP/MyLeQneanK3p+Zwtz3xNztaMfHOdLlP1j7RlfQZpt2Np8difAmcVt1vAJ4GTgZcDX2l77tNIuyqQCvmNpF9hOgb438BT2sYtfTpS0bBnAleTunNWFY9/EXh3cfsZwMZi+LGk/rAlbcv2k2I6q0l7Eu8EPgt8CljRRbs1NlvT8zmbX1e33b//q3WLXdLzSQc4fxv4IPC+YtAO0m+mQvrkuRb4XeCfgUMkPVfSskj96TskvSQiriR93eVHgUuBiyLi36bnFUXLlHQS8BrgQ8BxpL59SGfbTO8d/Bj4OvDKiLie9Ik7UsxzCrgOODYidgBvJHUF3QW8LyIeLBuobU/j1U3LNoPbrprGtRu47brJ9kRouzI/jfdLJL0QOBz4akT8hLQ1fmtEnC7pBcA5klrAOPCfJO0XEQ9J+i7we6RzNC8E/gD4pKTdwCRwezGLvwIujIhdJTIpIkLSsaTdnK8DWyKdHvnrwG0RcaWk20lXr74C2Ar8jqRVEXGPpH8FHpT0dODPgdMkHUz61ah7SbtORMQEMFGh3VqkvZoHgI8DdwPP7Hc2t121bE+EdnPb5dd28ym1xa5kmaQ3SbqR1LF/IDBdeH8ObC+2vm8g7VocBzzE46fwADxK2gU5hLRVfhOpf/1q4J6IuAPSVnnFov5S4DOko8ovBz5SjPIYcKuk5RFxe5HvuaQX607S+aTTy7GE1D6XFBlPBdYCm6PiOa6SVkr6bDHN24FzI+JuSXuRPsn7mW1J0Xa/SdoVbEzbFevdgKTzaVjbFfMMScM0c53bR9KKhrbdAQ1vuwFJ+0q6gIa13YI66a8BVgAvLm4fWAT75CzjnUW6DHZ1cf9kUn/6YaSvALi6eHxfUjfMqrbnHgPs3UmeGfPcD3gbj2/5LwP+CHhHMfxJwPeK6Z9C6u8aKoadVCzLquL2JLCS1L//5fY8wF5dZLuIdMXYAKlr6cy2caaPQ7wT+B+Lla3tdT2DtLKtJx3gaUrbTWe7tFivDmpY2+0PbCH9khjAe5rQbjPyfRn46+L+x4C39bvtSO+JN5Pe/5c0re3a8l0J/H3xWGPWu07/Ftxil3Q2cBuwRdJgRNxP6he6s+gbf40ev0DoW6QDoNMXGl1LOoj6UERcAPxU0udIB0VvAX7RhxQRN0bEIwvlmZHtEOByYBj4HOkAxWtJewl7iun+lHTg9d2kvq+Defw0ymtI59I/EhGXA58mXc16HumI9aNt+Up9qs7I9nfA24tstwJHSNpYbEX9vtIFV1eQ9mB6nq3It4L05jqBdP3AK0jHPY4lbSn1s+3as20mnS3wWtI1DL/R77YrLCdde/EsSatI6/ySYpp9abdZ8u1NWteeSuriOFrSR/rVdpKWkY6xnQx8PCJ+txh0TNs0+9Z2M/J9LCKmt7gngaP62XaldfAJNkzavfhb4D3FY8eSitYdRfALgU3FsHOAD7c9/3rgmOL2PqRTgI6t41OJtPK+qO3+6aQtkzcD3257/KnAncXtd5Au231S8fwvAU9vG3dVj7K9iXSk+9eBvy/+Xg/8L9K5/IuWrW16B7bd/s+kN9Op/W67WbL9MemUsWc2qO3eTOprfT/wVtKBtOv73W6z5HsfaY9nVRPajrQHduqMx04BrmtC282R7+lFhr6vdx0vRwcLOn1qzinAeHF7GWlramVx/zDS1vqxpF3Ai0lbWv9E+qTapyfhUx+X4Be/3foCHu/uuZd0zuj0uF+jKLSk3aevFuP8ySJlOwb4xvSK2zbeMtLB5ROK++f0OtuMnAeQjm/sBD5c3L8XGOxX282S7a5ivisouvn61XZtr+dbSN1srwW+UDx2T7/bbY58Y8Vj7acL92W9I3VR3ApsKub/gaJ+3Acc3IB1rj3fVaQv5jq03+td6eUoscBPJl0o9Jzi/tIZw88HTp5egUhdD2fSo6I+x8p8AY+fL/854KPF7V8j7XE8ve2FOZq2rwRepGzvaH+suH1I0XbPXexsbRn+kHS+7WZSv/Y3izec+tl2M7KdRzqt7NlNaDvSV0YvIfWhXk3aMr4JeH+/17lZ8v0z6QyzFzSk7b5C2gN7Gmkr+CzShmFT1rn2fF8gXfp/eBPartO/6aLTEUmfAn4WERuK+3uRzrt8B/Ac4JQo2U9eF0mHkvq03hURtyp9odhokWs18J2o56qybrK9PSJuKx47htQtta7I9of9yNZO6TqEM0lvsiNJK+uh9LHt2rIdTXqz/TnpLKuT6FPbSRogdXPsQ2qn3yBdeHI2aUv5cPrYbrPkO5x0fOK3SMe8XkZqv76sdypOey5uP4/0Pr2WdEl939e5GfmOJl3pfi7pm2b7tt6VUfY89s3AucVBhiNJK/HxpBfl7H4V9cIxFOfASzqD1P9/NqkL6QeRTr/sd7YfFdluJ60ce0hb8Tf2MVu7e0kHAd8XEX8n6TTg5obku5/UT3wT6XVdRv/abg/p7IlHSVvqPyet/5PAexvQbnPle1jSa0gFv2/r3XTRLNxPOu70/oi4sAFtNzPfA6SN123Af6W/613Hym6xv450oPRh0jeOXRkRt/QoWymSriUdXNtOOof0QxHxvb6GKszIdhewoUHttpK0BfcG0vffbwbOi4hH533iIpgl26cjYlN/U/2y4sKT6b7su/qdZ6Yi38nAZyOdddLvPPuQfnPhjaQ96r8EPhXpa7r7bpZ8myPiz/qbqpyOC7uk55LO57yYdLCotq/K7VaxB/FB0pbw5yNdtdYITc4GIGkpqfvlYVK+Jr2ujc0G6aIu4LEos3W0iJqcT9KZpNNqP9e01xWan28hpbbYzcys+Zr603hmZlaRC7uZWWZc2M3MMuPCbmaWGRd2M7PMuLCbmWXGhd3MLDP/H+KofDj+oV4qAAAAAElFTkSuQmCC\n", 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