{ "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
020230937172962525.080933.010894.0122.0FRFrance
120230837709068677.085503.0116103.0129.0FRFrance
220230738985180397.099305.0135121.0149.0FRFrance
320230639736887636.0107100.0146131.0161.0FRFrance
420230539546986268.0104670.0144130.0158.0FRFrance
520230437490166916.082886.0113101.0125.0FRFrance
620230336957061893.077247.010593.0117.0FRFrance
720230237826070090.086430.0118106.0130.0FRFrance
82023013121773111024.0132522.0183167.0199.0FRFrance
92022523155383142015.0168751.0234214.0254.0FRFrance
102022513248311232120.0264502.0374350.0398.0FRFrance
112022503234279219533.0249025.0353331.0375.0FRFrance
122022493163421151727.0175115.0246228.0264.0FRFrance
132022483121884111932.0131836.0184169.0199.0FRFrance
1420224739644787259.0105635.0145131.0159.0FRFrance
1520224636773560075.075395.010290.0114.0FRFrance
1620224534530638909.051703.06858.078.0FRFrance
1720224433471328880.040546.05243.061.0FRFrance
1820224334476936884.052654.06856.080.0FRFrance
1920224234746240773.054151.07262.082.0FRFrance
2020224134858342388.054778.07364.082.0FRFrance
2120224034192736115.047739.06354.072.0FRFrance
2220223933990234168.045636.06051.069.0FRFrance
2320223832878123733.033829.04335.051.0FRFrance
2420223732139517076.025714.03225.039.0FRFrance
2520223631412010487.017753.02116.026.0FRFrance
26202235392836485.012081.01410.018.0FRFrance
27202234374984731.010265.0117.015.0FRFrance
28202233375864442.010730.0116.016.0FRFrance
292022323122227749.016695.01811.025.0FRFrance
.................................
197119852132609619621.032571.04735.059.0FRFrance
197219852032789620885.034907.05138.064.0FRFrance
197319851934315432821.053487.07859.097.0FRFrance
197419851834055529935.051175.07455.093.0FRFrance
197519851733405324366.043740.06244.080.0FRFrance
197619851635036236451.064273.09166.0116.0FRFrance
197719851536388145538.082224.011683.0149.0FRFrance
19781985143134545114400.0154690.0244207.0281.0FRFrance
19791985133197206176080.0218332.0357319.0395.0FRFrance
19801985123245240223304.0267176.0445405.0485.0FRFrance
19811985113276205252399.0300011.0501458.0544.0FRFrance
19821985103353231326279.0380183.0640591.0689.0FRFrance
19831985093369895341109.0398681.0670618.0722.0FRFrance
19841985083389886359529.0420243.0707652.0762.0FRFrance
19851985073471852432599.0511105.0855784.0926.0FRFrance
19861985063565825518011.0613639.01026939.01113.0FRFrance
19871985053637302592795.0681809.011551074.01236.0FRFrance
19881985043424937390794.0459080.0770708.0832.0FRFrance
19891985033213901174689.0253113.0388317.0459.0FRFrance
199019850239758680949.0114223.0177147.0207.0FRFrance
199119850138548965918.0105060.0155120.0190.0FRFrance
199219845238483060602.0109058.0154110.0198.0FRFrance
1993198451310172680242.0123210.0185146.0224.0FRFrance
19941984503123680101401.0145959.0225184.0266.0FRFrance
1995198449310107381684.0120462.0184149.0219.0FRFrance
199619844837862060634.096606.0143110.0176.0FRFrance
199719844737202954274.089784.013199.0163.0FRFrance
199819844638733067686.0106974.0159123.0195.0FRFrance
19991984453135223101414.0169032.0246184.0308.0FRFrance
200019844436842220056.0116788.012537.0213.0FRFrance
\n", "

2001 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202309 3 71729 62525.0 80933.0 108 94.0 \n", "1 202308 3 77090 68677.0 85503.0 116 103.0 \n", "2 202307 3 89851 80397.0 99305.0 135 121.0 \n", "3 202306 3 97368 87636.0 107100.0 146 131.0 \n", "4 202305 3 95469 86268.0 104670.0 144 130.0 \n", "5 202304 3 74901 66916.0 82886.0 113 101.0 \n", "6 202303 3 69570 61893.0 77247.0 105 93.0 \n", "7 202302 3 78260 70090.0 86430.0 118 106.0 \n", "8 202301 3 121773 111024.0 132522.0 183 167.0 \n", "9 202252 3 155383 142015.0 168751.0 234 214.0 \n", "10 202251 3 248311 232120.0 264502.0 374 350.0 \n", "11 202250 3 234279 219533.0 249025.0 353 331.0 \n", "12 202249 3 163421 151727.0 175115.0 246 228.0 \n", "13 202248 3 121884 111932.0 131836.0 184 169.0 \n", "14 202247 3 96447 87259.0 105635.0 145 131.0 \n", "15 202246 3 67735 60075.0 75395.0 102 90.0 \n", "16 202245 3 45306 38909.0 51703.0 68 58.0 \n", "17 202244 3 34713 28880.0 40546.0 52 43.0 \n", "18 202243 3 44769 36884.0 52654.0 68 56.0 \n", "19 202242 3 47462 40773.0 54151.0 72 62.0 \n", "20 202241 3 48583 42388.0 54778.0 73 64.0 \n", "21 202240 3 41927 36115.0 47739.0 63 54.0 \n", "22 202239 3 39902 34168.0 45636.0 60 51.0 \n", "23 202238 3 28781 23733.0 33829.0 43 35.0 \n", "24 202237 3 21395 17076.0 25714.0 32 25.0 \n", "25 202236 3 14120 10487.0 17753.0 21 16.0 \n", "26 202235 3 9283 6485.0 12081.0 14 10.0 \n", "27 202234 3 7498 4731.0 10265.0 11 7.0 \n", "28 202233 3 7586 4442.0 10730.0 11 6.0 \n", "29 202232 3 12222 7749.0 16695.0 18 11.0 \n", "... ... ... ... ... ... ... ... \n", "1971 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1972 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1973 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1974 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1975 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1976 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1977 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1978 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1979 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1980 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1981 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1982 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1983 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1984 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1985 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1986 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1987 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1988 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1989 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1990 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1991 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1992 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1993 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1994 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1995 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1996 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1997 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1998 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1999 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2000 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 122.0 FR France \n", "1 129.0 FR France \n", "2 149.0 FR France \n", "3 161.0 FR France \n", "4 158.0 FR France \n", "5 125.0 FR France \n", "6 117.0 FR France \n", "7 130.0 FR France \n", "8 199.0 FR France \n", "9 254.0 FR France \n", "10 398.0 FR France \n", "11 375.0 FR France \n", "12 264.0 FR France \n", "13 199.0 FR France \n", "14 159.0 FR France \n", "15 114.0 FR France \n", "16 78.0 FR France \n", "17 61.0 FR France \n", "18 80.0 FR France \n", "19 82.0 FR France \n", "20 82.0 FR France \n", "21 72.0 FR France \n", "22 69.0 FR France \n", "23 51.0 FR France \n", "24 39.0 FR France \n", "25 26.0 FR France \n", "26 18.0 FR France \n", "27 15.0 FR France \n", "28 16.0 FR France \n", "29 25.0 FR France \n", "... ... ... ... \n", "1971 59.0 FR France \n", "1972 64.0 FR France \n", "1973 97.0 FR France \n", "1974 93.0 FR France \n", "1975 80.0 FR France \n", "1976 116.0 FR France \n", "1977 149.0 FR France \n", "1978 281.0 FR France \n", "1979 395.0 FR France \n", "1980 485.0 FR France \n", "1981 544.0 FR France \n", "1982 689.0 FR France \n", "1983 722.0 FR France \n", "1984 762.0 FR France \n", "1985 926.0 FR France \n", "1986 1113.0 FR France \n", "1987 1236.0 FR France \n", "1988 832.0 FR France \n", "1989 459.0 FR France \n", "1990 207.0 FR France \n", "1991 190.0 FR France \n", "1992 198.0 FR France \n", "1993 224.0 FR France \n", "1994 266.0 FR France \n", "1995 219.0 FR France \n", "1996 176.0 FR France \n", "1997 163.0 FR France \n", "1998 195.0 FR France \n", "1999 308.0 FR France \n", "2000 213.0 FR France \n", "\n", "[2001 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
176419891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1764 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1764 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
020230937172962525.080933.010894.0122.0FRFrance
120230837709068677.085503.0116103.0129.0FRFrance
220230738985180397.099305.0135121.0149.0FRFrance
320230639736887636.0107100.0146131.0161.0FRFrance
420230539546986268.0104670.0144130.0158.0FRFrance
520230437490166916.082886.0113101.0125.0FRFrance
620230336957061893.077247.010593.0117.0FRFrance
720230237826070090.086430.0118106.0130.0FRFrance
82023013121773111024.0132522.0183167.0199.0FRFrance
92022523155383142015.0168751.0234214.0254.0FRFrance
102022513248311232120.0264502.0374350.0398.0FRFrance
112022503234279219533.0249025.0353331.0375.0FRFrance
122022493163421151727.0175115.0246228.0264.0FRFrance
132022483121884111932.0131836.0184169.0199.0FRFrance
1420224739644787259.0105635.0145131.0159.0FRFrance
1520224636773560075.075395.010290.0114.0FRFrance
1620224534530638909.051703.06858.078.0FRFrance
1720224433471328880.040546.05243.061.0FRFrance
1820224334476936884.052654.06856.080.0FRFrance
1920224234746240773.054151.07262.082.0FRFrance
2020224134858342388.054778.07364.082.0FRFrance
2120224034192736115.047739.06354.072.0FRFrance
2220223933990234168.045636.06051.069.0FRFrance
2320223832878123733.033829.04335.051.0FRFrance
2420223732139517076.025714.03225.039.0FRFrance
2520223631412010487.017753.02116.026.0FRFrance
26202235392836485.012081.01410.018.0FRFrance
27202234374984731.010265.0117.015.0FRFrance
28202233375864442.010730.0116.016.0FRFrance
292022323122227749.016695.01811.025.0FRFrance
.................................
197119852132609619621.032571.04735.059.0FRFrance
197219852032789620885.034907.05138.064.0FRFrance
197319851934315432821.053487.07859.097.0FRFrance
197419851834055529935.051175.07455.093.0FRFrance
197519851733405324366.043740.06244.080.0FRFrance
197619851635036236451.064273.09166.0116.0FRFrance
197719851536388145538.082224.011683.0149.0FRFrance
19781985143134545114400.0154690.0244207.0281.0FRFrance
19791985133197206176080.0218332.0357319.0395.0FRFrance
19801985123245240223304.0267176.0445405.0485.0FRFrance
19811985113276205252399.0300011.0501458.0544.0FRFrance
19821985103353231326279.0380183.0640591.0689.0FRFrance
19831985093369895341109.0398681.0670618.0722.0FRFrance
19841985083389886359529.0420243.0707652.0762.0FRFrance
19851985073471852432599.0511105.0855784.0926.0FRFrance
19861985063565825518011.0613639.01026939.01113.0FRFrance
19871985053637302592795.0681809.011551074.01236.0FRFrance
19881985043424937390794.0459080.0770708.0832.0FRFrance
19891985033213901174689.0253113.0388317.0459.0FRFrance
199019850239758680949.0114223.0177147.0207.0FRFrance
199119850138548965918.0105060.0155120.0190.0FRFrance
199219845238483060602.0109058.0154110.0198.0FRFrance
1993198451310172680242.0123210.0185146.0224.0FRFrance
19941984503123680101401.0145959.0225184.0266.0FRFrance
1995198449310107381684.0120462.0184149.0219.0FRFrance
199619844837862060634.096606.0143110.0176.0FRFrance
199719844737202954274.089784.013199.0163.0FRFrance
199819844638733067686.0106974.0159123.0195.0FRFrance
19991984453135223101414.0169032.0246184.0308.0FRFrance
200019844436842220056.0116788.012537.0213.0FRFrance
\n", "

2000 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202309 3 71729 62525.0 80933.0 108 94.0 \n", "1 202308 3 77090 68677.0 85503.0 116 103.0 \n", "2 202307 3 89851 80397.0 99305.0 135 121.0 \n", "3 202306 3 97368 87636.0 107100.0 146 131.0 \n", "4 202305 3 95469 86268.0 104670.0 144 130.0 \n", "5 202304 3 74901 66916.0 82886.0 113 101.0 \n", "6 202303 3 69570 61893.0 77247.0 105 93.0 \n", "7 202302 3 78260 70090.0 86430.0 118 106.0 \n", "8 202301 3 121773 111024.0 132522.0 183 167.0 \n", "9 202252 3 155383 142015.0 168751.0 234 214.0 \n", "10 202251 3 248311 232120.0 264502.0 374 350.0 \n", "11 202250 3 234279 219533.0 249025.0 353 331.0 \n", "12 202249 3 163421 151727.0 175115.0 246 228.0 \n", "13 202248 3 121884 111932.0 131836.0 184 169.0 \n", "14 202247 3 96447 87259.0 105635.0 145 131.0 \n", "15 202246 3 67735 60075.0 75395.0 102 90.0 \n", "16 202245 3 45306 38909.0 51703.0 68 58.0 \n", "17 202244 3 34713 28880.0 40546.0 52 43.0 \n", "18 202243 3 44769 36884.0 52654.0 68 56.0 \n", "19 202242 3 47462 40773.0 54151.0 72 62.0 \n", "20 202241 3 48583 42388.0 54778.0 73 64.0 \n", "21 202240 3 41927 36115.0 47739.0 63 54.0 \n", "22 202239 3 39902 34168.0 45636.0 60 51.0 \n", "23 202238 3 28781 23733.0 33829.0 43 35.0 \n", "24 202237 3 21395 17076.0 25714.0 32 25.0 \n", "25 202236 3 14120 10487.0 17753.0 21 16.0 \n", "26 202235 3 9283 6485.0 12081.0 14 10.0 \n", "27 202234 3 7498 4731.0 10265.0 11 7.0 \n", "28 202233 3 7586 4442.0 10730.0 11 6.0 \n", "29 202232 3 12222 7749.0 16695.0 18 11.0 \n", "... ... ... ... ... ... ... ... \n", "1971 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1972 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1973 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1974 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1975 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1976 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1977 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1978 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1979 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1980 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1981 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1982 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1983 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1984 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1985 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1986 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1987 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1988 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1989 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1990 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1991 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1992 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1993 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1994 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1995 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1996 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1997 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1998 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1999 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2000 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 122.0 FR France \n", "1 129.0 FR France \n", "2 149.0 FR France \n", "3 161.0 FR France \n", "4 158.0 FR France \n", "5 125.0 FR France \n", "6 117.0 FR France \n", "7 130.0 FR France \n", "8 199.0 FR France \n", "9 254.0 FR France \n", "10 398.0 FR France \n", "11 375.0 FR France \n", "12 264.0 FR France \n", "13 199.0 FR France \n", "14 159.0 FR France \n", "15 114.0 FR France \n", "16 78.0 FR France \n", "17 61.0 FR France \n", "18 80.0 FR France \n", "19 82.0 FR France \n", "20 82.0 FR France \n", "21 72.0 FR France \n", "22 69.0 FR France \n", "23 51.0 FR France \n", "24 39.0 FR France \n", "25 26.0 FR France \n", "26 18.0 FR France \n", "27 15.0 FR France \n", "28 16.0 FR France \n", "29 25.0 FR France \n", "... ... ... ... \n", "1971 59.0 FR France \n", "1972 64.0 FR France \n", "1973 97.0 FR France \n", "1974 93.0 FR France \n", "1975 80.0 FR France \n", "1976 116.0 FR France \n", "1977 149.0 FR France \n", "1978 281.0 FR France \n", "1979 395.0 FR France \n", "1980 485.0 FR France \n", "1981 544.0 FR France \n", "1982 689.0 FR France \n", "1983 722.0 FR France \n", "1984 762.0 FR France \n", "1985 926.0 FR France \n", "1986 1113.0 FR France \n", "1987 1236.0 FR France \n", "1988 832.0 FR France \n", "1989 459.0 FR France \n", "1990 207.0 FR France \n", "1991 190.0 FR France \n", "1992 198.0 FR France \n", "1993 224.0 FR France \n", "1994 266.0 FR France \n", "1995 219.0 FR France \n", "1996 176.0 FR France \n", "1997 163.0 FR France \n", "1998 195.0 FR France \n", "1999 308.0 FR France \n", "2000 213.0 FR France \n", "\n", "[2000 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", 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" ] }, "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": [ "2021 743449\n", "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2010315\n", "2022 2060360\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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# @source=\"réseau SentinellesINSERMSorbonne Universitéhttp://www.sentiweb.fr\"@meta={\"period\":[198444202309]geo:[\"PAY\"1]geo_ref:\"insee\"indicator:\"3\"type:\"all\"conf_int:truecompact:false}@date=2023-03-12T17:29:51+01:00
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [# @source=\"réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr\", @meta={\"period\":[198444, 202309], geo:[\"PAY\", 1], geo_ref:\"insee\", indicator:\"3\", type:\"all\", conf_int:true, compact:false}, @date=2023-03-12T17:29:51+01:00]\n", "Index: []" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os\n", "\n", "\n", "data_file = \"incidence-PAY-3.csv\"\n", "\n", "if not os.path.isfile(data_file):\n", " data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", " raw_data = pd.read_csv(data_url, encoding='iso-8859-1', skiprows=1)\n", " raw_data.to_csv(data_file, index=False)\n", "else:\n", " raw_data = pd.read_csv(data_file, encoding='iso-8859-1')\n", "\n", "data = raw_data.dropna().copy()\n", "\n", "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }