{ "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
0202014300.00.000.00.0FRFrance
1202013300.00.000.00.0FRFrance
2202012383215873.010769.0139.017.0FRFrance
3202011310170493652.0109756.0154142.0166.0FRFrance
4202010310497796650.0113304.0159146.0172.0FRFrance
52020093110696102066.0119326.0168155.0181.0FRFrance
62020083143753133984.0153522.0218203.0233.0FRFrance
72020073183610172812.0194408.0279263.0295.0FRFrance
82020063206669195481.0217857.0314297.0331.0FRFrance
92020053187957177445.0198469.0285269.0301.0FRFrance
102020043122331113492.0131170.0186173.0199.0FRFrance
1120200337841371330.085496.0119108.0130.0FRFrance
1220200235361447654.059574.08172.090.0FRFrance
1320200133685031608.042092.05648.064.0FRFrance
1420195232813523220.033050.04336.050.0FRFrance
1520195132978625042.034530.04538.052.0FRFrance
1620195033422329156.039290.05244.060.0FRFrance
1720194932566221414.029910.03933.045.0FRFrance
1820194832236718055.026679.03427.041.0FRFrance
1920194731866914759.022579.02822.034.0FRFrance
2020194631603012567.019493.02419.029.0FRFrance
212019453101387160.013116.01510.020.0FRFrance
22201944378225010.010634.0128.016.0FRFrance
23201943394876448.012526.0149.019.0FRFrance
24201942377475243.010251.0128.016.0FRFrance
25201941371224720.09524.0117.015.0FRFrance
26201940385055784.011226.0139.017.0FRFrance
27201939370914462.09720.0117.015.0FRFrance
28201938348972891.06903.074.010.0FRFrance
29201937331721367.04977.052.08.0FRFrance
.................................
181919852132609619621.032571.04735.059.0FRFrance
182019852032789620885.034907.05138.064.0FRFrance
182119851934315432821.053487.07859.097.0FRFrance
182219851834055529935.051175.07455.093.0FRFrance
182319851733405324366.043740.06244.080.0FRFrance
182419851635036236451.064273.09166.0116.0FRFrance
182519851536388145538.082224.011683.0149.0FRFrance
18261985143134545114400.0154690.0244207.0281.0FRFrance
18271985133197206176080.0218332.0357319.0395.0FRFrance
18281985123245240223304.0267176.0445405.0485.0FRFrance
18291985113276205252399.0300011.0501458.0544.0FRFrance
18301985103353231326279.0380183.0640591.0689.0FRFrance
18311985093369895341109.0398681.0670618.0722.0FRFrance
18321985083389886359529.0420243.0707652.0762.0FRFrance
18331985073471852432599.0511105.0855784.0926.0FRFrance
18341985063565825518011.0613639.01026939.01113.0FRFrance
18351985053637302592795.0681809.011551074.01236.0FRFrance
18361985043424937390794.0459080.0770708.0832.0FRFrance
18371985033213901174689.0253113.0388317.0459.0FRFrance
183819850239758680949.0114223.0177147.0207.0FRFrance
183919850138548965918.0105060.0155120.0190.0FRFrance
184019845238483060602.0109058.0154110.0198.0FRFrance
1841198451310172680242.0123210.0185146.0224.0FRFrance
18421984503123680101401.0145959.0225184.0266.0FRFrance
1843198449310107381684.0120462.0184149.0219.0FRFrance
184419844837862060634.096606.0143110.0176.0FRFrance
184519844737202954274.089784.013199.0163.0FRFrance
184619844638733067686.0106974.0159123.0195.0FRFrance
18471984453135223101414.0169032.0246184.0308.0FRFrance
184819844436842220056.0116788.012537.0213.0FRFrance
\n", "

1849 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202014 3 0 0.0 0.0 0 0.0 \n", "1 202013 3 0 0.0 0.0 0 0.0 \n", "2 202012 3 8321 5873.0 10769.0 13 9.0 \n", "3 202011 3 101704 93652.0 109756.0 154 142.0 \n", "4 202010 3 104977 96650.0 113304.0 159 146.0 \n", "5 202009 3 110696 102066.0 119326.0 168 155.0 \n", "6 202008 3 143753 133984.0 153522.0 218 203.0 \n", "7 202007 3 183610 172812.0 194408.0 279 263.0 \n", "8 202006 3 206669 195481.0 217857.0 314 297.0 \n", "9 202005 3 187957 177445.0 198469.0 285 269.0 \n", "10 202004 3 122331 113492.0 131170.0 186 173.0 \n", "11 202003 3 78413 71330.0 85496.0 119 108.0 \n", "12 202002 3 53614 47654.0 59574.0 81 72.0 \n", "13 202001 3 36850 31608.0 42092.0 56 48.0 \n", "14 201952 3 28135 23220.0 33050.0 43 36.0 \n", "15 201951 3 29786 25042.0 34530.0 45 38.0 \n", "16 201950 3 34223 29156.0 39290.0 52 44.0 \n", "17 201949 3 25662 21414.0 29910.0 39 33.0 \n", "18 201948 3 22367 18055.0 26679.0 34 27.0 \n", "19 201947 3 18669 14759.0 22579.0 28 22.0 \n", "20 201946 3 16030 12567.0 19493.0 24 19.0 \n", "21 201945 3 10138 7160.0 13116.0 15 10.0 \n", "22 201944 3 7822 5010.0 10634.0 12 8.0 \n", "23 201943 3 9487 6448.0 12526.0 14 9.0 \n", "24 201942 3 7747 5243.0 10251.0 12 8.0 \n", "25 201941 3 7122 4720.0 9524.0 11 7.0 \n", "26 201940 3 8505 5784.0 11226.0 13 9.0 \n", "27 201939 3 7091 4462.0 9720.0 11 7.0 \n", "28 201938 3 4897 2891.0 6903.0 7 4.0 \n", "29 201937 3 3172 1367.0 4977.0 5 2.0 \n", "... ... ... ... ... ... ... ... \n", "1819 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1820 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1821 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1822 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1823 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1824 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1825 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1826 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1827 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1828 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1829 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1830 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1831 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1832 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1833 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1834 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1835 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1836 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1837 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1838 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1839 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1840 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1841 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1842 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1843 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1844 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1845 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1846 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1847 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1848 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 0.0 FR France \n", "1 0.0 FR France \n", "2 17.0 FR France \n", "3 166.0 FR France \n", "4 172.0 FR France \n", "5 181.0 FR France \n", "6 233.0 FR France \n", "7 295.0 FR France \n", "8 331.0 FR France \n", "9 301.0 FR France \n", "10 199.0 FR France \n", "11 130.0 FR France \n", "12 90.0 FR France \n", "13 64.0 FR France \n", "14 50.0 FR France \n", "15 52.0 FR France \n", "16 60.0 FR France \n", "17 45.0 FR France \n", "18 41.0 FR France \n", "19 34.0 FR France \n", "20 29.0 FR France \n", "21 20.0 FR France \n", "22 16.0 FR France \n", "23 19.0 FR France \n", "24 16.0 FR France \n", "25 15.0 FR France \n", "26 17.0 FR France \n", "27 15.0 FR France \n", "28 10.0 FR France \n", "29 8.0 FR France \n", "... ... ... ... \n", "1819 59.0 FR France \n", "1820 64.0 FR France \n", "1821 97.0 FR France \n", "1822 93.0 FR France \n", "1823 80.0 FR France \n", "1824 116.0 FR France \n", "1825 149.0 FR France \n", "1826 281.0 FR France \n", "1827 395.0 FR France \n", "1828 485.0 FR France \n", "1829 544.0 FR France \n", "1830 689.0 FR France \n", "1831 722.0 FR France \n", "1832 762.0 FR France \n", "1833 926.0 FR France \n", "1834 1113.0 FR France \n", "1835 1236.0 FR France \n", "1836 832.0 FR France \n", "1837 459.0 FR France \n", "1838 207.0 FR France \n", "1839 190.0 FR France \n", "1840 198.0 FR France \n", "1841 224.0 FR France \n", "1842 266.0 FR France \n", "1843 219.0 FR France \n", "1844 176.0 FR France \n", "1845 163.0 FR France \n", "1846 195.0 FR France \n", "1847 308.0 FR France \n", "1848 213.0 FR France \n", "\n", "[1849 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
161219891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1612 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1612 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
0202014300.00.000.00.0FRFrance
1202013300.00.000.00.0FRFrance
2202012383215873.010769.0139.017.0FRFrance
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4202010310497796650.0113304.0159146.0172.0FRFrance
52020093110696102066.0119326.0168155.0181.0FRFrance
62020083143753133984.0153522.0218203.0233.0FRFrance
72020073183610172812.0194408.0279263.0295.0FRFrance
82020063206669195481.0217857.0314297.0331.0FRFrance
92020053187957177445.0198469.0285269.0301.0FRFrance
102020043122331113492.0131170.0186173.0199.0FRFrance
1120200337841371330.085496.0119108.0130.0FRFrance
1220200235361447654.059574.08172.090.0FRFrance
1320200133685031608.042092.05648.064.0FRFrance
1420195232813523220.033050.04336.050.0FRFrance
1520195132978625042.034530.04538.052.0FRFrance
1620195033422329156.039290.05244.060.0FRFrance
1720194932566221414.029910.03933.045.0FRFrance
1820194832236718055.026679.03427.041.0FRFrance
1920194731866914759.022579.02822.034.0FRFrance
2020194631603012567.019493.02419.029.0FRFrance
212019453101387160.013116.01510.020.0FRFrance
22201944378225010.010634.0128.016.0FRFrance
23201943394876448.012526.0149.019.0FRFrance
24201942377475243.010251.0128.016.0FRFrance
25201941371224720.09524.0117.015.0FRFrance
26201940385055784.011226.0139.017.0FRFrance
27201939370914462.09720.0117.015.0FRFrance
28201938348972891.06903.074.010.0FRFrance
29201937331721367.04977.052.08.0FRFrance
.................................
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182519851536388145538.082224.011683.0149.0FRFrance
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18301985103353231326279.0380183.0640591.0689.0FRFrance
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\n", "

1848 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202014 3 0 0.0 0.0 0 0.0 \n", "1 202013 3 0 0.0 0.0 0 0.0 \n", "2 202012 3 8321 5873.0 10769.0 13 9.0 \n", "3 202011 3 101704 93652.0 109756.0 154 142.0 \n", "4 202010 3 104977 96650.0 113304.0 159 146.0 \n", "5 202009 3 110696 102066.0 119326.0 168 155.0 \n", "6 202008 3 143753 133984.0 153522.0 218 203.0 \n", "7 202007 3 183610 172812.0 194408.0 279 263.0 \n", "8 202006 3 206669 195481.0 217857.0 314 297.0 \n", "9 202005 3 187957 177445.0 198469.0 285 269.0 \n", "10 202004 3 122331 113492.0 131170.0 186 173.0 \n", "11 202003 3 78413 71330.0 85496.0 119 108.0 \n", "12 202002 3 53614 47654.0 59574.0 81 72.0 \n", "13 202001 3 36850 31608.0 42092.0 56 48.0 \n", "14 201952 3 28135 23220.0 33050.0 43 36.0 \n", "15 201951 3 29786 25042.0 34530.0 45 38.0 \n", "16 201950 3 34223 29156.0 39290.0 52 44.0 \n", "17 201949 3 25662 21414.0 29910.0 39 33.0 \n", "18 201948 3 22367 18055.0 26679.0 34 27.0 \n", "19 201947 3 18669 14759.0 22579.0 28 22.0 \n", "20 201946 3 16030 12567.0 19493.0 24 19.0 \n", "21 201945 3 10138 7160.0 13116.0 15 10.0 \n", "22 201944 3 7822 5010.0 10634.0 12 8.0 \n", "23 201943 3 9487 6448.0 12526.0 14 9.0 \n", "24 201942 3 7747 5243.0 10251.0 12 8.0 \n", "25 201941 3 7122 4720.0 9524.0 11 7.0 \n", "26 201940 3 8505 5784.0 11226.0 13 9.0 \n", "27 201939 3 7091 4462.0 9720.0 11 7.0 \n", "28 201938 3 4897 2891.0 6903.0 7 4.0 \n", "29 201937 3 3172 1367.0 4977.0 5 2.0 \n", "... ... ... ... ... ... ... ... \n", "1819 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1820 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1821 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1822 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1823 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1824 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1825 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1826 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1827 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1828 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1829 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1830 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1831 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1832 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1833 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1834 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1835 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1836 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1837 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1838 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1839 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1840 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1841 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1842 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1843 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1844 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1845 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1846 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1847 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1848 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 0.0 FR France \n", "1 0.0 FR France \n", "2 17.0 FR France \n", "3 166.0 FR France \n", "4 172.0 FR France \n", "5 181.0 FR France \n", "6 233.0 FR France \n", "7 295.0 FR France \n", "8 331.0 FR France \n", "9 301.0 FR France \n", "10 199.0 FR France \n", "11 130.0 FR France \n", "12 90.0 FR France \n", "13 64.0 FR France \n", "14 50.0 FR France \n", "15 52.0 FR France \n", "16 60.0 FR France \n", "17 45.0 FR France \n", "18 41.0 FR France \n", "19 34.0 FR France \n", "20 29.0 FR France \n", "21 20.0 FR France \n", "22 16.0 FR France \n", "23 19.0 FR France \n", "24 16.0 FR France \n", "25 15.0 FR France \n", "26 17.0 FR France \n", "27 15.0 FR France \n", "28 10.0 FR France \n", "29 8.0 FR France \n", "... ... ... ... \n", "1819 59.0 FR France \n", "1820 64.0 FR France \n", "1821 97.0 FR France \n", "1822 93.0 FR France \n", "1823 80.0 FR France \n", "1824 116.0 FR France \n", "1825 149.0 FR France \n", "1826 281.0 FR France \n", "1827 395.0 FR France \n", "1828 485.0 FR France \n", "1829 544.0 FR France \n", "1830 689.0 FR France \n", "1831 722.0 FR France \n", "1832 762.0 FR France \n", "1833 926.0 FR France \n", "1834 1113.0 FR France \n", "1835 1236.0 FR France \n", "1836 832.0 FR France \n", "1837 459.0 FR France \n", "1838 207.0 FR France \n", "1839 190.0 FR France \n", "1840 198.0 FR France \n", "1841 224.0 FR France \n", "1842 266.0 FR France \n", "1843 219.0 FR France \n", "1844 176.0 FR France \n", "1845 163.0 FR France \n", "1846 195.0 FR France \n", "1847 308.0 FR France \n", "1848 213.0 FR France \n", "\n", "[1848 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": 16, "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", "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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