{ "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": "markdown", "metadata": {}, "source": [ "**Observation**: We now have a local file with the data, so the following line has been commented out:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.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": "markdown", "metadata": {}, "source": [ "**Observation**: We modified and now we read the data from the file *raw_data.csv*:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202137723176693965426FRFrance
12021367356517485382528FRFrance
22021357256211074017426FRFrance
3202134714293782480204FRFrance
42021337382918305828639FRFrance
52021327410818956321639FRFrance
620213174793230172857311FRFrance
72021307719041911018911616FRFrance
8202129768004109949110614FRFrance
92021287973402173115033FRFrance
102021277902643161373614721FRFrance
112021267728441081046011616FRFrance
1220212579351654012162141018FRFrance
13202124712034893715131181323FRFrance
1420212379116642011812141018FRFrance
1520212274817275268827410FRFrance
1620212176092345887269513FRFrance
172021207748546011036911715FRFrance
18202119766544370893810713FRFrance
192021187391221105714639FRFrance
2020211774686287864947410FRFrance
2120211674780289166697410FRFrance
22202115711215762714803171222FRFrance
23202114711197799414400171222FRFrance
2420211379714628913139151020FRFrance
25202112711520841514625171222FRFrance
2620211179386667812094141018FRFrance
2720211079056645211660141018FRFrance
28202109710988793814038171222FRFrance
29202108711281836114201171321FRFrance
.................................
15771991267176081130423912312042FRFrance
15781991257161691070021638281838FRFrance
15791991247161711007122271281739FRFrance
1580199123711947767116223211329FRFrance
1581199122715452995320951271737FRFrance
1582199121714903897520831261636FRFrance
15831991207190531274225364342345FRFrance
15841991197167391124622232291939FRFrance
15851991187213851388228888382551FRFrance
1586199117713462887718047241632FRFrance
15871991167148571006819646261834FRFrance
1588199115713975978118169251832FRFrance
1589199114712265768416846221430FRFrance
159019911379567604113093171123FRFrance
1591199112710864733114397191325FRFrance
15921991117155741118419964271935FRFrance
15931991107166431137221914292038FRFrance
1594199109713741878018702241533FRFrance
1595199108713289881317765231531FRFrance
1596199107712337807716597221529FRFrance
1597199106710877701314741191226FRFrance
1598199105710442654414340181125FRFrance
15991991047791345631126314820FRFrance
16001991037153871048420290271836FRFrance
16011991027162771104621508292038FRFrance
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16031990527193751329525455342345FRFrance
16041990517190801380724353342543FRFrance
1605199050711079666015498201228FRFrance
16061990497114302610205FRFrance
\n", "

1607 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202137 7 2317 669 3965 4 2 \n", "1 202136 7 3565 1748 5382 5 2 \n", "2 202135 7 2562 1107 4017 4 2 \n", "3 202134 7 1429 378 2480 2 0 \n", "4 202133 7 3829 1830 5828 6 3 \n", "5 202132 7 4108 1895 6321 6 3 \n", "6 202131 7 4793 2301 7285 7 3 \n", "7 202130 7 7190 4191 10189 11 6 \n", "8 202129 7 6800 4109 9491 10 6 \n", "9 202128 7 9734 0 21731 15 0 \n", "10 202127 7 9026 4316 13736 14 7 \n", "11 202126 7 7284 4108 10460 11 6 \n", "12 202125 7 9351 6540 12162 14 10 \n", "13 202124 7 12034 8937 15131 18 13 \n", "14 202123 7 9116 6420 11812 14 10 \n", "15 202122 7 4817 2752 6882 7 4 \n", "16 202121 7 6092 3458 8726 9 5 \n", "17 202120 7 7485 4601 10369 11 7 \n", "18 202119 7 6654 4370 8938 10 7 \n", "19 202118 7 3912 2110 5714 6 3 \n", "20 202117 7 4686 2878 6494 7 4 \n", "21 202116 7 4780 2891 6669 7 4 \n", "22 202115 7 11215 7627 14803 17 12 \n", "23 202114 7 11197 7994 14400 17 12 \n", "24 202113 7 9714 6289 13139 15 10 \n", "25 202112 7 11520 8415 14625 17 12 \n", "26 202111 7 9386 6678 12094 14 10 \n", "27 202110 7 9056 6452 11660 14 10 \n", "28 202109 7 10988 7938 14038 17 12 \n", "29 202108 7 11281 8361 14201 17 13 \n", "... ... ... ... ... ... ... ... \n", "1577 199126 7 17608 11304 23912 31 20 \n", "1578 199125 7 16169 10700 21638 28 18 \n", "1579 199124 7 16171 10071 22271 28 17 \n", "1580 199123 7 11947 7671 16223 21 13 \n", "1581 199122 7 15452 9953 20951 27 17 \n", "1582 199121 7 14903 8975 20831 26 16 \n", "1583 199120 7 19053 12742 25364 34 23 \n", "1584 199119 7 16739 11246 22232 29 19 \n", "1585 199118 7 21385 13882 28888 38 25 \n", "1586 199117 7 13462 8877 18047 24 16 \n", "1587 199116 7 14857 10068 19646 26 18 \n", "1588 199115 7 13975 9781 18169 25 18 \n", "1589 199114 7 12265 7684 16846 22 14 \n", "1590 199113 7 9567 6041 13093 17 11 \n", "1591 199112 7 10864 7331 14397 19 13 \n", "1592 199111 7 15574 11184 19964 27 19 \n", "1593 199110 7 16643 11372 21914 29 20 \n", "1594 199109 7 13741 8780 18702 24 15 \n", "1595 199108 7 13289 8813 17765 23 15 \n", "1596 199107 7 12337 8077 16597 22 15 \n", "1597 199106 7 10877 7013 14741 19 12 \n", "1598 199105 7 10442 6544 14340 18 11 \n", "1599 199104 7 7913 4563 11263 14 8 \n", "1600 199103 7 15387 10484 20290 27 18 \n", "1601 199102 7 16277 11046 21508 29 20 \n", "1602 199101 7 15565 10271 20859 27 18 \n", "1603 199052 7 19375 13295 25455 34 23 \n", "1604 199051 7 19080 13807 24353 34 25 \n", "1605 199050 7 11079 6660 15498 20 12 \n", "1606 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 6 FR France \n", "1 8 FR France \n", "2 6 FR France \n", "3 4 FR France \n", "4 9 FR France \n", "5 9 FR France \n", "6 11 FR France \n", "7 16 FR France \n", "8 14 FR France \n", "9 33 FR France \n", "10 21 FR France \n", "11 16 FR France \n", "12 18 FR France \n", "13 23 FR France \n", "14 18 FR France \n", "15 10 FR France \n", "16 13 FR France \n", "17 15 FR France \n", "18 13 FR France \n", "19 9 FR France \n", "20 10 FR France \n", "21 10 FR France \n", "22 22 FR France \n", "23 22 FR France \n", "24 20 FR France \n", "25 22 FR France \n", "26 18 FR France \n", "27 18 FR France \n", "28 22 FR France \n", "29 21 FR France \n", "... ... ... ... \n", "1577 42 FR France \n", "1578 38 FR France \n", "1579 39 FR France \n", "1580 29 FR France \n", "1581 37 FR France \n", "1582 36 FR France \n", "1583 45 FR France \n", "1584 39 FR France \n", "1585 51 FR France \n", "1586 32 FR France \n", "1587 34 FR France \n", "1588 32 FR France \n", "1589 30 FR France \n", "1590 23 FR France \n", "1591 25 FR France \n", "1592 35 FR France \n", "1593 38 FR France \n", "1594 33 FR France \n", "1595 31 FR France \n", "1596 29 FR France \n", "1597 26 FR France \n", "1598 25 FR France \n", "1599 20 FR France \n", "1600 36 FR France \n", "1601 38 FR France \n", "1602 36 FR France \n", "1603 45 FR France \n", "1604 43 FR France \n", "1605 28 FR France \n", "1606 5 FR France \n", "\n", "[1607 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", "raw_data\n", "\n", "# raw_data = pd.read_csv(\"raw_data.csv\", index_col=0)\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", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "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": [], "source": [ "# data = raw_data.dropna().copy()\n", "data = raw_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": [], "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", 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" ] }, "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": 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": {}, "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": {}, "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": {}, "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 }