{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence of influenza-like illness in France" ] }, { "cell_type": "code", "execution_count": 16, "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": 36, "metadata": {}, "outputs": [], "source": [ "#Data file can be downloaded at this adress = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", "#Data loaded was on the 24th of October 2023\n", "data_url = \"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": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020234136819858031.078365.010388.0118.0FRFrance
120234036985860925.078791.010592.0118.0FRFrance
220233937200363452.080554.010895.0121.0FRFrance
320233836321855227.071209.09583.0107.0FRFrance
420233734908542079.056091.07463.085.0FRFrance
520233633824732237.044257.05849.067.0FRFrance
620233533169526013.037377.04839.057.0FRFrance
720233432666321057.032269.04032.048.0FRFrance
820233331914413161.025127.02920.038.0FRFrance
920233231464110285.018997.02215.029.0FRFrance
1020233131528610705.019867.02316.030.0FRFrance
112023303132058647.017763.02013.027.0FRFrance
122023293111227113.015131.01711.023.0FRFrance
13202328391795703.012655.0149.019.0FRFrance
14202327389995763.012235.0149.019.0FRFrance
15202326390235934.012112.0149.019.0FRFrance
162023253100906739.013441.01510.020.0FRFrance
172023243113087639.014977.01711.023.0FRFrance
1820232331430010661.017939.02217.027.0FRFrance
1920232231830313822.022784.02821.035.0FRFrance
2020232131646012188.020732.02519.031.0FRFrance
2120232031616211963.020361.02418.030.0FRFrance
2220231931690112577.021225.02518.032.0FRFrance
2320231831992915402.024456.03023.037.0FRFrance
2420231732700721779.032235.04133.049.0FRFrance
2520231632787522767.032983.04234.050.0FRFrance
2620231533745530993.043917.05646.066.0FRFrance
2720231434806040671.055449.07261.083.0FRFrance
2820231336485956800.072918.09886.0110.0FRFrance
2920231237275064499.081001.010997.0121.0FRFrance
.................................
200319852132609619621.032571.04735.059.0FRFrance
200419852032789620885.034907.05138.064.0FRFrance
200519851934315432821.053487.07859.097.0FRFrance
200619851834055529935.051175.07455.093.0FRFrance
200719851733405324366.043740.06244.080.0FRFrance
200819851635036236451.064273.09166.0116.0FRFrance
200919851536388145538.082224.011683.0149.0FRFrance
20101985143134545114400.0154690.0244207.0281.0FRFrance
20111985133197206176080.0218332.0357319.0395.0FRFrance
20121985123245240223304.0267176.0445405.0485.0FRFrance
20131985113276205252399.0300011.0501458.0544.0FRFrance
20141985103353231326279.0380183.0640591.0689.0FRFrance
20151985093369895341109.0398681.0670618.0722.0FRFrance
20161985083389886359529.0420243.0707652.0762.0FRFrance
20171985073471852432599.0511105.0855784.0926.0FRFrance
20181985063565825518011.0613639.01026939.01113.0FRFrance
20191985053637302592795.0681809.011551074.01236.0FRFrance
20201985043424937390794.0459080.0770708.0832.0FRFrance
20211985033213901174689.0253113.0388317.0459.0FRFrance
202219850239758680949.0114223.0177147.0207.0FRFrance
202319850138548965918.0105060.0155120.0190.0FRFrance
202419845238483060602.0109058.0154110.0198.0FRFrance
2025198451310172680242.0123210.0185146.0224.0FRFrance
20261984503123680101401.0145959.0225184.0266.0FRFrance
2027198449310107381684.0120462.0184149.0219.0FRFrance
202819844837862060634.096606.0143110.0176.0FRFrance
202919844737202954274.089784.013199.0163.0FRFrance
203019844638733067686.0106974.0159123.0195.0FRFrance
20311984453135223101414.0169032.0246184.0308.0FRFrance
203219844436842220056.0116788.012537.0213.0FRFrance
\n", "

2033 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202341 3 68198 58031.0 78365.0 103 88.0 \n", "1 202340 3 69858 60925.0 78791.0 105 92.0 \n", "2 202339 3 72003 63452.0 80554.0 108 95.0 \n", "3 202338 3 63218 55227.0 71209.0 95 83.0 \n", "4 202337 3 49085 42079.0 56091.0 74 63.0 \n", "5 202336 3 38247 32237.0 44257.0 58 49.0 \n", "6 202335 3 31695 26013.0 37377.0 48 39.0 \n", "7 202334 3 26663 21057.0 32269.0 40 32.0 \n", "8 202333 3 19144 13161.0 25127.0 29 20.0 \n", "9 202332 3 14641 10285.0 18997.0 22 15.0 \n", "10 202331 3 15286 10705.0 19867.0 23 16.0 \n", "11 202330 3 13205 8647.0 17763.0 20 13.0 \n", "12 202329 3 11122 7113.0 15131.0 17 11.0 \n", "13 202328 3 9179 5703.0 12655.0 14 9.0 \n", "14 202327 3 8999 5763.0 12235.0 14 9.0 \n", "15 202326 3 9023 5934.0 12112.0 14 9.0 \n", "16 202325 3 10090 6739.0 13441.0 15 10.0 \n", "17 202324 3 11308 7639.0 14977.0 17 11.0 \n", "18 202323 3 14300 10661.0 17939.0 22 17.0 \n", "19 202322 3 18303 13822.0 22784.0 28 21.0 \n", "20 202321 3 16460 12188.0 20732.0 25 19.0 \n", "21 202320 3 16162 11963.0 20361.0 24 18.0 \n", "22 202319 3 16901 12577.0 21225.0 25 18.0 \n", "23 202318 3 19929 15402.0 24456.0 30 23.0 \n", "24 202317 3 27007 21779.0 32235.0 41 33.0 \n", "25 202316 3 27875 22767.0 32983.0 42 34.0 \n", "26 202315 3 37455 30993.0 43917.0 56 46.0 \n", "27 202314 3 48060 40671.0 55449.0 72 61.0 \n", "28 202313 3 64859 56800.0 72918.0 98 86.0 \n", "29 202312 3 72750 64499.0 81001.0 109 97.0 \n", "... ... ... ... ... ... ... ... \n", "2003 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2004 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2005 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2006 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2007 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2008 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2009 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2010 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2011 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2012 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2013 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2014 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2015 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2016 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2017 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2018 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2019 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2020 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2021 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2022 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2023 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2024 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2025 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2026 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2027 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2028 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2029 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2030 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2031 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2032 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 118.0 FR France \n", "1 118.0 FR France \n", "2 121.0 FR France \n", "3 107.0 FR France \n", "4 85.0 FR France \n", "5 67.0 FR France \n", "6 57.0 FR France \n", "7 48.0 FR France \n", "8 38.0 FR France \n", "9 29.0 FR France \n", "10 30.0 FR France \n", "11 27.0 FR France \n", "12 23.0 FR France \n", "13 19.0 FR France \n", "14 19.0 FR France \n", "15 19.0 FR France \n", "16 20.0 FR France \n", "17 23.0 FR France \n", "18 27.0 FR France \n", "19 35.0 FR France \n", "20 31.0 FR France \n", "21 30.0 FR France \n", "22 32.0 FR France \n", "23 37.0 FR France \n", "24 49.0 FR France \n", "25 50.0 FR France \n", "26 66.0 FR France \n", "27 83.0 FR France \n", "28 110.0 FR France \n", "29 121.0 FR France \n", "... ... ... ... \n", "2003 59.0 FR France \n", "2004 64.0 FR France \n", "2005 97.0 FR France \n", "2006 93.0 FR France \n", "2007 80.0 FR France \n", "2008 116.0 FR France \n", "2009 149.0 FR France \n", "2010 281.0 FR France \n", "2011 395.0 FR France \n", "2012 485.0 FR France \n", "2013 544.0 FR France \n", "2014 689.0 FR France \n", "2015 722.0 FR France \n", "2016 762.0 FR France \n", "2017 926.0 FR France \n", "2018 1113.0 FR France \n", "2019 1236.0 FR France \n", "2020 832.0 FR France \n", "2021 459.0 FR France \n", "2022 207.0 FR France \n", "2023 190.0 FR France \n", "2024 198.0 FR France \n", "2025 224.0 FR France \n", "2026 266.0 FR France \n", "2027 219.0 FR France \n", "2028 176.0 FR France \n", "2029 163.0 FR France \n", "2030 195.0 FR France \n", "2031 308.0 FR France \n", "2032 213.0 FR France \n", "\n", "[2033 rows x 10 columns]" ] }, "execution_count": 37, "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": { "collapsed": true }, "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": null, "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": null, "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": null, "metadata": { "collapsed": true }, "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }