{ "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": { "collapsed": true }, "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
020191035839450263.066525.08977.0101.0FRFrance
120190938939180479.098303.0136122.0150.0FRFrance
22019083172604160024.0185184.0262243.0281.0FRFrance
32019073307338291220.0323456.0467443.0491.0FRFrance
42019063394286376782.0411790.0599572.0626.0FRFrance
52019053355785339295.0372275.0540515.0565.0FRFrance
62019043241090227261.0254919.0366345.0387.0FRFrance
72019033147063135890.0158236.0223206.0240.0FRFrance
820190237554867632.083464.0115103.0127.0FRFrance
920190135029543525.057065.07666.086.0FRFrance
1020185233790331375.044431.05848.068.0FRFrance
1120185133925932977.045541.06050.070.0FRFrance
1220185032778122638.032924.04234.050.0FRFrance
1320184931973815481.023995.03024.036.0FRFrance
1420184831950115275.023727.03024.036.0FRFrance
1520184731594912105.019793.02418.030.0FRFrance
162018463112787957.014599.01712.022.0FRFrance
172018453110657791.014339.01712.022.0FRFrance
18201844365863875.09297.0106.014.0FRFrance
19201843365503988.09112.0106.014.0FRFrance
20201842377875129.010445.0128.016.0FRFrance
21201841380485098.010998.0128.016.0FRFrance
22201840374094717.010101.0117.015.0FRFrance
23201839371744235.010113.0117.015.0FRFrance
24201838373494399.010299.0117.015.0FRFrance
25201837349152386.07444.073.011.0FRFrance
26201836332151349.05081.052.08.0FRFrance
2720183531506239.02773.020.04.0FRFrance
2820183431368116.02620.020.04.0FRFrance
29201833319625.03919.030.06.0FRFrance
.................................
176319852132609619621.032571.04735.059.0FRFrance
176419852032789620885.034907.05138.064.0FRFrance
176519851934315432821.053487.07859.097.0FRFrance
176619851834055529935.051175.07455.093.0FRFrance
176719851733405324366.043740.06244.080.0FRFrance
176819851635036236451.064273.09166.0116.0FRFrance
176919851536388145538.082224.011683.0149.0FRFrance
17701985143134545114400.0154690.0244207.0281.0FRFrance
17711985133197206176080.0218332.0357319.0395.0FRFrance
17721985123245240223304.0267176.0445405.0485.0FRFrance
17731985113276205252399.0300011.0501458.0544.0FRFrance
17741985103353231326279.0380183.0640591.0689.0FRFrance
17751985093369895341109.0398681.0670618.0722.0FRFrance
17761985083389886359529.0420243.0707652.0762.0FRFrance
17771985073471852432599.0511105.0855784.0926.0FRFrance
17781985063565825518011.0613639.01026939.01113.0FRFrance
17791985053637302592795.0681809.011551074.01236.0FRFrance
17801985043424937390794.0459080.0770708.0832.0FRFrance
17811985033213901174689.0253113.0388317.0459.0FRFrance
178219850239758680949.0114223.0177147.0207.0FRFrance
178319850138548965918.0105060.0155120.0190.0FRFrance
178419845238483060602.0109058.0154110.0198.0FRFrance
1785198451310172680242.0123210.0185146.0224.0FRFrance
17861984503123680101401.0145959.0225184.0266.0FRFrance
1787198449310107381684.0120462.0184149.0219.0FRFrance
178819844837862060634.096606.0143110.0176.0FRFrance
178919844737202954274.089784.013199.0163.0FRFrance
179019844638733067686.0106974.0159123.0195.0FRFrance
17911984453135223101414.0169032.0246184.0308.0FRFrance
179219844436842220056.0116788.012537.0213.0FRFrance
\n", "

1793 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 201910 3 58394 50263.0 66525.0 89 77.0 \n", "1 201909 3 89391 80479.0 98303.0 136 122.0 \n", "2 201908 3 172604 160024.0 185184.0 262 243.0 \n", "3 201907 3 307338 291220.0 323456.0 467 443.0 \n", "4 201906 3 394286 376782.0 411790.0 599 572.0 \n", "5 201905 3 355785 339295.0 372275.0 540 515.0 \n", "6 201904 3 241090 227261.0 254919.0 366 345.0 \n", "7 201903 3 147063 135890.0 158236.0 223 206.0 \n", "8 201902 3 75548 67632.0 83464.0 115 103.0 \n", "9 201901 3 50295 43525.0 57065.0 76 66.0 \n", "10 201852 3 37903 31375.0 44431.0 58 48.0 \n", "11 201851 3 39259 32977.0 45541.0 60 50.0 \n", "12 201850 3 27781 22638.0 32924.0 42 34.0 \n", "13 201849 3 19738 15481.0 23995.0 30 24.0 \n", "14 201848 3 19501 15275.0 23727.0 30 24.0 \n", "15 201847 3 15949 12105.0 19793.0 24 18.0 \n", "16 201846 3 11278 7957.0 14599.0 17 12.0 \n", "17 201845 3 11065 7791.0 14339.0 17 12.0 \n", "18 201844 3 6586 3875.0 9297.0 10 6.0 \n", "19 201843 3 6550 3988.0 9112.0 10 6.0 \n", "20 201842 3 7787 5129.0 10445.0 12 8.0 \n", "21 201841 3 8048 5098.0 10998.0 12 8.0 \n", "22 201840 3 7409 4717.0 10101.0 11 7.0 \n", "23 201839 3 7174 4235.0 10113.0 11 7.0 \n", "24 201838 3 7349 4399.0 10299.0 11 7.0 \n", "25 201837 3 4915 2386.0 7444.0 7 3.0 \n", "26 201836 3 3215 1349.0 5081.0 5 2.0 \n", "27 201835 3 1506 239.0 2773.0 2 0.0 \n", "28 201834 3 1368 116.0 2620.0 2 0.0 \n", "29 201833 3 1962 5.0 3919.0 3 0.0 \n", "... ... ... ... ... ... ... ... \n", "1763 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1764 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1765 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1766 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1767 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1768 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1769 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1770 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1771 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1772 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1773 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1774 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1775 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1776 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1777 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1778 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1779 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1780 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1781 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1782 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1783 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1784 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1785 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1786 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1787 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1788 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1789 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1790 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1791 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1792 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 101.0 FR France \n", "1 150.0 FR France \n", "2 281.0 FR France \n", "3 491.0 FR France \n", "4 626.0 FR France \n", "5 565.0 FR France \n", "6 387.0 FR France \n", "7 240.0 FR France \n", "8 127.0 FR France \n", "9 86.0 FR France \n", "10 68.0 FR France \n", "11 70.0 FR France \n", "12 50.0 FR France \n", "13 36.0 FR France \n", "14 36.0 FR France \n", "15 30.0 FR France \n", "16 22.0 FR France \n", "17 22.0 FR France \n", "18 14.0 FR France \n", "19 14.0 FR France \n", "20 16.0 FR France \n", "21 16.0 FR France \n", "22 15.0 FR France \n", "23 15.0 FR France \n", "24 15.0 FR France \n", "25 11.0 FR France \n", "26 8.0 FR France \n", "27 4.0 FR France \n", "28 4.0 FR France \n", "29 6.0 FR France \n", "... ... ... ... \n", "1763 59.0 FR France \n", "1764 64.0 FR France \n", "1765 97.0 FR France \n", "1766 93.0 FR France \n", "1767 80.0 FR France \n", "1768 116.0 FR France \n", "1769 149.0 FR France \n", "1770 281.0 FR France \n", "1771 395.0 FR France \n", "1772 485.0 FR France \n", "1773 544.0 FR France \n", "1774 689.0 FR France \n", "1775 722.0 FR France \n", "1776 762.0 FR France \n", "1777 926.0 FR France \n", "1778 1113.0 FR France \n", "1779 1236.0 FR France \n", "1780 832.0 FR France \n", "1781 459.0 FR France \n", "1782 207.0 FR France \n", "1783 190.0 FR France \n", "1784 198.0 FR France \n", "1785 224.0 FR France \n", "1786 266.0 FR France \n", "1787 219.0 FR France \n", "1788 176.0 FR France \n", "1789 163.0 FR France \n", "1790 195.0 FR France \n", "1791 308.0 FR France \n", "1792 213.0 FR France \n", "\n", "[1793 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
155619891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1556 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1556 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
020191035839450263.066525.08977.0101.0FRFrance
120190938939180479.098303.0136122.0150.0FRFrance
22019083172604160024.0185184.0262243.0281.0FRFrance
32019073307338291220.0323456.0467443.0491.0FRFrance
42019063394286376782.0411790.0599572.0626.0FRFrance
52019053355785339295.0372275.0540515.0565.0FRFrance
62019043241090227261.0254919.0366345.0387.0FRFrance
72019033147063135890.0158236.0223206.0240.0FRFrance
820190237554867632.083464.0115103.0127.0FRFrance
920190135029543525.057065.07666.086.0FRFrance
1020185233790331375.044431.05848.068.0FRFrance
1120185133925932977.045541.06050.070.0FRFrance
1220185032778122638.032924.04234.050.0FRFrance
1320184931973815481.023995.03024.036.0FRFrance
1420184831950115275.023727.03024.036.0FRFrance
1520184731594912105.019793.02418.030.0FRFrance
162018463112787957.014599.01712.022.0FRFrance
172018453110657791.014339.01712.022.0FRFrance
18201844365863875.09297.0106.014.0FRFrance
19201843365503988.09112.0106.014.0FRFrance
20201842377875129.010445.0128.016.0FRFrance
21201841380485098.010998.0128.016.0FRFrance
22201840374094717.010101.0117.015.0FRFrance
23201839371744235.010113.0117.015.0FRFrance
24201838373494399.010299.0117.015.0FRFrance
25201837349152386.07444.073.011.0FRFrance
26201836332151349.05081.052.08.0FRFrance
2720183531506239.02773.020.04.0FRFrance
2820183431368116.02620.020.04.0FRFrance
29201833319625.03919.030.06.0FRFrance
.................................
176319852132609619621.032571.04735.059.0FRFrance
176419852032789620885.034907.05138.064.0FRFrance
176519851934315432821.053487.07859.097.0FRFrance
176619851834055529935.051175.07455.093.0FRFrance
176719851733405324366.043740.06244.080.0FRFrance
176819851635036236451.064273.09166.0116.0FRFrance
176919851536388145538.082224.011683.0149.0FRFrance
17701985143134545114400.0154690.0244207.0281.0FRFrance
17711985133197206176080.0218332.0357319.0395.0FRFrance
17721985123245240223304.0267176.0445405.0485.0FRFrance
17731985113276205252399.0300011.0501458.0544.0FRFrance
17741985103353231326279.0380183.0640591.0689.0FRFrance
17751985093369895341109.0398681.0670618.0722.0FRFrance
17761985083389886359529.0420243.0707652.0762.0FRFrance
17771985073471852432599.0511105.0855784.0926.0FRFrance
17781985063565825518011.0613639.01026939.01113.0FRFrance
17791985053637302592795.0681809.011551074.01236.0FRFrance
17801985043424937390794.0459080.0770708.0832.0FRFrance
17811985033213901174689.0253113.0388317.0459.0FRFrance
178219850239758680949.0114223.0177147.0207.0FRFrance
178319850138548965918.0105060.0155120.0190.0FRFrance
178419845238483060602.0109058.0154110.0198.0FRFrance
1785198451310172680242.0123210.0185146.0224.0FRFrance
17861984503123680101401.0145959.0225184.0266.0FRFrance
1787198449310107381684.0120462.0184149.0219.0FRFrance
178819844837862060634.096606.0143110.0176.0FRFrance
178919844737202954274.089784.013199.0163.0FRFrance
179019844638733067686.0106974.0159123.0195.0FRFrance
17911984453135223101414.0169032.0246184.0308.0FRFrance
179219844436842220056.0116788.012537.0213.0FRFrance
\n", "

1792 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 201910 3 58394 50263.0 66525.0 89 77.0 \n", "1 201909 3 89391 80479.0 98303.0 136 122.0 \n", "2 201908 3 172604 160024.0 185184.0 262 243.0 \n", "3 201907 3 307338 291220.0 323456.0 467 443.0 \n", "4 201906 3 394286 376782.0 411790.0 599 572.0 \n", "5 201905 3 355785 339295.0 372275.0 540 515.0 \n", "6 201904 3 241090 227261.0 254919.0 366 345.0 \n", "7 201903 3 147063 135890.0 158236.0 223 206.0 \n", "8 201902 3 75548 67632.0 83464.0 115 103.0 \n", "9 201901 3 50295 43525.0 57065.0 76 66.0 \n", "10 201852 3 37903 31375.0 44431.0 58 48.0 \n", "11 201851 3 39259 32977.0 45541.0 60 50.0 \n", "12 201850 3 27781 22638.0 32924.0 42 34.0 \n", "13 201849 3 19738 15481.0 23995.0 30 24.0 \n", "14 201848 3 19501 15275.0 23727.0 30 24.0 \n", "15 201847 3 15949 12105.0 19793.0 24 18.0 \n", "16 201846 3 11278 7957.0 14599.0 17 12.0 \n", "17 201845 3 11065 7791.0 14339.0 17 12.0 \n", "18 201844 3 6586 3875.0 9297.0 10 6.0 \n", "19 201843 3 6550 3988.0 9112.0 10 6.0 \n", "20 201842 3 7787 5129.0 10445.0 12 8.0 \n", "21 201841 3 8048 5098.0 10998.0 12 8.0 \n", "22 201840 3 7409 4717.0 10101.0 11 7.0 \n", "23 201839 3 7174 4235.0 10113.0 11 7.0 \n", "24 201838 3 7349 4399.0 10299.0 11 7.0 \n", "25 201837 3 4915 2386.0 7444.0 7 3.0 \n", "26 201836 3 3215 1349.0 5081.0 5 2.0 \n", "27 201835 3 1506 239.0 2773.0 2 0.0 \n", "28 201834 3 1368 116.0 2620.0 2 0.0 \n", "29 201833 3 1962 5.0 3919.0 3 0.0 \n", "... ... ... ... ... ... ... ... \n", "1763 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1764 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1765 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1766 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1767 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1768 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1769 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1770 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1771 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1772 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1773 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1774 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1775 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1776 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1777 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1778 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1779 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1780 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1781 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1782 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1783 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1784 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1785 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1786 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1787 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1788 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1789 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1790 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1791 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1792 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 101.0 FR France \n", "1 150.0 FR France \n", "2 281.0 FR France \n", "3 491.0 FR France \n", "4 626.0 FR France \n", "5 565.0 FR France \n", "6 387.0 FR France \n", "7 240.0 FR France \n", "8 127.0 FR France \n", "9 86.0 FR France \n", "10 68.0 FR France \n", "11 70.0 FR France \n", "12 50.0 FR France \n", "13 36.0 FR France \n", "14 36.0 FR France \n", "15 30.0 FR France \n", "16 22.0 FR France \n", "17 22.0 FR France \n", "18 14.0 FR France \n", "19 14.0 FR France \n", "20 16.0 FR France \n", "21 16.0 FR France \n", "22 15.0 FR France \n", "23 15.0 FR France \n", "24 15.0 FR France \n", "25 11.0 FR France \n", "26 8.0 FR France \n", "27 4.0 FR France \n", "28 4.0 FR France \n", "29 6.0 FR France \n", "... ... ... ... \n", "1763 59.0 FR France \n", "1764 64.0 FR France \n", "1765 97.0 FR France \n", "1766 93.0 FR France \n", "1767 80.0 FR France \n", "1768 116.0 FR France \n", "1769 149.0 FR France \n", "1770 281.0 FR France \n", "1771 395.0 FR France \n", "1772 485.0 FR France \n", "1773 544.0 FR France \n", "1774 689.0 FR France \n", "1775 722.0 FR France \n", "1776 762.0 FR France \n", "1777 926.0 FR France \n", "1778 1113.0 FR France \n", "1779 1236.0 FR France \n", "1780 832.0 FR France \n", "1781 459.0 FR France \n", "1782 207.0 FR France \n", "1783 190.0 FR France \n", "1784 198.0 FR France \n", "1785 224.0 FR France \n", "1786 266.0 FR France \n", "1787 219.0 FR France \n", "1788 176.0 FR France \n", "1789 163.0 FR France \n", "1790 195.0 FR France \n", "1791 308.0 FR France \n", "1792 213.0 FR France \n", "\n", "[1792 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": { "collapsed": true }, "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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v1lSSShhNJYK32Decx+BoefiyUhSUbbuSPmkqSZEsCmnCfkQ/ebQdd6/Z4Xks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\ndzBvzmAxuT7r2SkVPtyZzXVlx6qJFipj5H1vmY3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HAaQB3MIYe22ci6XRaDQTmpoVKgDA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\nATTAOcE6AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "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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J1np4VZUtrpE5niCMZ2lUFSDeaKJRRiSSSnt/ZOqWRtBPNO6k/pUqA25MIzNL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\nXfdUbQWd/VGSZVpKxFsN791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"text/plain": [ "" ] }, "metadata": {}, "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": { "collapsed": true }, "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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"text/plain": [ "" ] }, "metadata": {}, "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", "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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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }