{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence of chickenpox 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 chickenpox 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 1991 and ending with a recent week, is available for download." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"incidence-PAY-7.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to make the data used in the analysis available and avoid possible problems in their accessibility, dataset has been downloaded by the [main site](https://www.sentiweb.fr/datasets/incidence-PAY-3.csv) (stored into the variable `data_url`) and copied in a local file associated with this [url](https://app-learninglab.inria.fr/moocrr/gitlab/59f64515f1441eb96cdb77ebbe26d68e/mooc-rr/blob/master/module3/exo2/incidence-PAY-7.csv) (stored into the variable `data_file`). In case the local file is not present, then it is downloaded from the relative url." ] }, { "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": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1202033713201772463204FRFrance
2202032726506894611417FRFrance
3202031713031002506204FRFrance
420203071385752695204FRFrance
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720202779861491823102FRFrance
8202026769401454102FRFrance
920202572280597001FRFrance
1020202473880959102FRFrance
11202023755811115102FRFrance
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1520201973100753001FRFrance
162020187849981600102FRFrance
1720201772720658001FRFrance
182020167758781438102FRFrance
19202015719186753161315FRFrance
202020147387922275531639FRFrance
21202013773265236941611814FRFrance
222020127812357901045612816FRFrance
23202011710198756812828151119FRFrance
2420201079011669111331141018FRFrance
252020097136311054416718211626FRFrance
26202008710424770813140161220FRFrance
2720200778959657411344141018FRFrance
2820200679264692511603141018FRFrance
2920200578505631410696131016FRFrance
.................................
15211991267176081130423912312042FRFrance
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1526199121714903897520831261636FRFrance
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15291991187213851388228888382551FRFrance
1530199117713462887718047241632FRFrance
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1532199115713975978118169251832FRFrance
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15481990517190801380724353342543FRFrance
1549199050711079666015498201228FRFrance
15501990497114302610205FRFrance
\n", "

1551 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202034 7 2507 259 4755 4 1 \n", "1 202033 7 1320 177 2463 2 0 \n", "2 202032 7 2650 689 4611 4 1 \n", "3 202031 7 1303 100 2506 2 0 \n", "4 202030 7 1385 75 2695 2 0 \n", "5 202029 7 841 10 1672 1 0 \n", "6 202028 7 728 0 1515 1 0 \n", "7 202027 7 986 149 1823 1 0 \n", "8 202026 7 694 0 1454 1 0 \n", "9 202025 7 228 0 597 0 0 \n", "10 202024 7 388 0 959 1 0 \n", "11 202023 7 558 1 1115 1 0 \n", "12 202022 7 277 0 633 0 0 \n", "13 202021 7 602 36 1168 1 0 \n", "14 202020 7 824 20 1628 1 0 \n", "15 202019 7 310 0 753 0 0 \n", "16 202018 7 849 98 1600 1 0 \n", "17 202017 7 272 0 658 0 0 \n", "18 202016 7 758 78 1438 1 0 \n", "19 202015 7 1918 675 3161 3 1 \n", "20 202014 7 3879 2227 5531 6 3 \n", "21 202013 7 7326 5236 9416 11 8 \n", "22 202012 7 8123 5790 10456 12 8 \n", "23 202011 7 10198 7568 12828 15 11 \n", "24 202010 7 9011 6691 11331 14 10 \n", "25 202009 7 13631 10544 16718 21 16 \n", "26 202008 7 10424 7708 13140 16 12 \n", "27 202007 7 8959 6574 11344 14 10 \n", "28 202006 7 9264 6925 11603 14 10 \n", "29 202005 7 8505 6314 10696 13 10 \n", "... ... ... ... ... ... ... ... \n", "1521 199126 7 17608 11304 23912 31 20 \n", "1522 199125 7 16169 10700 21638 28 18 \n", "1523 199124 7 16171 10071 22271 28 17 \n", "1524 199123 7 11947 7671 16223 21 13 \n", "1525 199122 7 15452 9953 20951 27 17 \n", "1526 199121 7 14903 8975 20831 26 16 \n", "1527 199120 7 19053 12742 25364 34 23 \n", "1528 199119 7 16739 11246 22232 29 19 \n", "1529 199118 7 21385 13882 28888 38 25 \n", "1530 199117 7 13462 8877 18047 24 16 \n", "1531 199116 7 14857 10068 19646 26 18 \n", "1532 199115 7 13975 9781 18169 25 18 \n", "1533 199114 7 12265 7684 16846 22 14 \n", "1534 199113 7 9567 6041 13093 17 11 \n", "1535 199112 7 10864 7331 14397 19 13 \n", "1536 199111 7 15574 11184 19964 27 19 \n", "1537 199110 7 16643 11372 21914 29 20 \n", "1538 199109 7 13741 8780 18702 24 15 \n", "1539 199108 7 13289 8813 17765 23 15 \n", "1540 199107 7 12337 8077 16597 22 15 \n", "1541 199106 7 10877 7013 14741 19 12 \n", "1542 199105 7 10442 6544 14340 18 11 \n", "1543 199104 7 7913 4563 11263 14 8 \n", "1544 199103 7 15387 10484 20290 27 18 \n", "1545 199102 7 16277 11046 21508 29 20 \n", "1546 199101 7 15565 10271 20859 27 18 \n", "1547 199052 7 19375 13295 25455 34 23 \n", "1548 199051 7 19080 13807 24353 34 25 \n", "1549 199050 7 11079 6660 15498 20 12 \n", "1550 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 7 FR France \n", "1 4 FR France \n", "2 7 FR France \n", "3 4 FR France \n", "4 4 FR France \n", "5 2 FR France \n", "6 2 FR France \n", "7 2 FR France \n", "8 2 FR France \n", "9 1 FR France \n", "10 2 FR France \n", "11 2 FR France \n", "12 1 FR France \n", "13 2 FR France \n", "14 2 FR France \n", "15 1 FR France \n", "16 2 FR France \n", "17 1 FR France \n", "18 2 FR France \n", "19 5 FR France \n", "20 9 FR France \n", "21 14 FR France \n", "22 16 FR France \n", "23 19 FR France \n", "24 18 FR France \n", "25 26 FR France \n", "26 20 FR France \n", "27 18 FR France \n", "28 18 FR France \n", "29 16 FR France \n", "... ... ... ... \n", "1521 42 FR France \n", "1522 38 FR France \n", "1523 39 FR France \n", "1524 29 FR France \n", "1525 37 FR France \n", "1526 36 FR France \n", "1527 45 FR France \n", "1528 39 FR France \n", "1529 51 FR France \n", "1530 32 FR France \n", "1531 34 FR France \n", "1532 32 FR France \n", "1533 30 FR France \n", "1534 23 FR France \n", "1535 25 FR France \n", "1536 35 FR France \n", "1537 38 FR France \n", "1538 33 FR France \n", "1539 31 FR France \n", "1540 29 FR France \n", "1541 26 FR France \n", "1542 25 FR France \n", "1543 20 FR France \n", "1544 36 FR France \n", "1545 38 FR France \n", "1546 36 FR France \n", "1547 45 FR France \n", "1548 43 FR France \n", "1549 28 FR France \n", "1550 5 FR France \n", "\n", "[1551 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are there missing data points? No." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 5, "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": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202034725072594755417FRFrance
1202033713201772463204FRFrance
2202032726506894611417FRFrance
3202031713031002506204FRFrance
420203071385752695204FRFrance
52020297841101672102FRFrance
6202028772801515102FRFrance
720202779861491823102FRFrance
8202026769401454102FRFrance
920202572280597001FRFrance
1020202473880959102FRFrance
11202023755811115102FRFrance
1220202272770633001FRFrance
132020217602361168102FRFrance
142020207824201628102FRFrance
1520201973100753001FRFrance
162020187849981600102FRFrance
1720201772720658001FRFrance
182020167758781438102FRFrance
19202015719186753161315FRFrance
202020147387922275531639FRFrance
21202013773265236941611814FRFrance
222020127812357901045612816FRFrance
23202011710198756812828151119FRFrance
2420201079011669111331141018FRFrance
252020097136311054416718211626FRFrance
26202008710424770813140161220FRFrance
2720200778959657411344141018FRFrance
2820200679264692511603141018FRFrance
2920200578505631410696131016FRFrance
.................................
15211991267176081130423912312042FRFrance
15221991257161691070021638281838FRFrance
15231991247161711007122271281739FRFrance
1524199123711947767116223211329FRFrance
1525199122715452995320951271737FRFrance
1526199121714903897520831261636FRFrance
15271991207190531274225364342345FRFrance
15281991197167391124622232291939FRFrance
15291991187213851388228888382551FRFrance
1530199117713462887718047241632FRFrance
15311991167148571006819646261834FRFrance
1532199115713975978118169251832FRFrance
1533199114712265768416846221430FRFrance
153419911379567604113093171123FRFrance
1535199112710864733114397191325FRFrance
15361991117155741118419964271935FRFrance
15371991107166431137221914292038FRFrance
1538199109713741878018702241533FRFrance
1539199108713289881317765231531FRFrance
1540199107712337807716597221529FRFrance
1541199106710877701314741191226FRFrance
1542199105710442654414340181125FRFrance
15431991047791345631126314820FRFrance
15441991037153871048420290271836FRFrance
15451991027162771104621508292038FRFrance
15461991017155651027120859271836FRFrance
15471990527193751329525455342345FRFrance
15481990517190801380724353342543FRFrance
1549199050711079666015498201228FRFrance
15501990497114302610205FRFrance
\n", "

1551 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202034 7 2507 259 4755 4 1 \n", "1 202033 7 1320 177 2463 2 0 \n", "2 202032 7 2650 689 4611 4 1 \n", "3 202031 7 1303 100 2506 2 0 \n", "4 202030 7 1385 75 2695 2 0 \n", "5 202029 7 841 10 1672 1 0 \n", "6 202028 7 728 0 1515 1 0 \n", "7 202027 7 986 149 1823 1 0 \n", "8 202026 7 694 0 1454 1 0 \n", "9 202025 7 228 0 597 0 0 \n", "10 202024 7 388 0 959 1 0 \n", "11 202023 7 558 1 1115 1 0 \n", "12 202022 7 277 0 633 0 0 \n", "13 202021 7 602 36 1168 1 0 \n", "14 202020 7 824 20 1628 1 0 \n", "15 202019 7 310 0 753 0 0 \n", "16 202018 7 849 98 1600 1 0 \n", "17 202017 7 272 0 658 0 0 \n", "18 202016 7 758 78 1438 1 0 \n", "19 202015 7 1918 675 3161 3 1 \n", "20 202014 7 3879 2227 5531 6 3 \n", "21 202013 7 7326 5236 9416 11 8 \n", "22 202012 7 8123 5790 10456 12 8 \n", "23 202011 7 10198 7568 12828 15 11 \n", "24 202010 7 9011 6691 11331 14 10 \n", "25 202009 7 13631 10544 16718 21 16 \n", "26 202008 7 10424 7708 13140 16 12 \n", "27 202007 7 8959 6574 11344 14 10 \n", "28 202006 7 9264 6925 11603 14 10 \n", "29 202005 7 8505 6314 10696 13 10 \n", "... ... ... ... ... ... ... ... \n", "1521 199126 7 17608 11304 23912 31 20 \n", "1522 199125 7 16169 10700 21638 28 18 \n", "1523 199124 7 16171 10071 22271 28 17 \n", "1524 199123 7 11947 7671 16223 21 13 \n", "1525 199122 7 15452 9953 20951 27 17 \n", "1526 199121 7 14903 8975 20831 26 16 \n", "1527 199120 7 19053 12742 25364 34 23 \n", "1528 199119 7 16739 11246 22232 29 19 \n", "1529 199118 7 21385 13882 28888 38 25 \n", "1530 199117 7 13462 8877 18047 24 16 \n", "1531 199116 7 14857 10068 19646 26 18 \n", "1532 199115 7 13975 9781 18169 25 18 \n", "1533 199114 7 12265 7684 16846 22 14 \n", "1534 199113 7 9567 6041 13093 17 11 \n", "1535 199112 7 10864 7331 14397 19 13 \n", "1536 199111 7 15574 11184 19964 27 19 \n", "1537 199110 7 16643 11372 21914 29 20 \n", "1538 199109 7 13741 8780 18702 24 15 \n", "1539 199108 7 13289 8813 17765 23 15 \n", "1540 199107 7 12337 8077 16597 22 15 \n", "1541 199106 7 10877 7013 14741 19 12 \n", "1542 199105 7 10442 6544 14340 18 11 \n", "1543 199104 7 7913 4563 11263 14 8 \n", "1544 199103 7 15387 10484 20290 27 18 \n", "1545 199102 7 16277 11046 21508 29 20 \n", "1546 199101 7 15565 10271 20859 27 18 \n", "1547 199052 7 19375 13295 25455 34 23 \n", "1548 199051 7 19080 13807 24353 34 25 \n", "1549 199050 7 11079 6660 15498 20 12 \n", "1550 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 7 FR France \n", "1 4 FR France \n", "2 7 FR France \n", "3 4 FR France \n", "4 4 FR France \n", "5 2 FR France \n", "6 2 FR France \n", "7 2 FR France \n", "8 2 FR France \n", "9 1 FR France \n", "10 2 FR France \n", "11 2 FR France \n", "12 1 FR France \n", "13 2 FR France \n", "14 2 FR France \n", "15 1 FR France \n", "16 2 FR France \n", "17 1 FR France \n", "18 2 FR France \n", "19 5 FR France \n", "20 9 FR France \n", "21 14 FR France \n", "22 16 FR France \n", "23 19 FR France \n", "24 18 FR France \n", "25 26 FR France \n", "26 20 FR France \n", "27 18 FR France \n", "28 18 FR France \n", "29 16 FR France \n", "... ... ... ... \n", "1521 42 FR France \n", "1522 38 FR France \n", "1523 39 FR France \n", "1524 29 FR France \n", "1525 37 FR France \n", "1526 36 FR France \n", "1527 45 FR France \n", "1528 39 FR France \n", "1529 51 FR France \n", "1530 32 FR France \n", "1531 34 FR France \n", "1532 32 FR France \n", "1533 30 FR France \n", "1534 23 FR France \n", "1535 25 FR France \n", "1536 35 FR France \n", "1537 38 FR France \n", "1538 33 FR France \n", "1539 31 FR France \n", "1540 29 FR France \n", "1541 26 FR France \n", "1542 25 FR France \n", "1543 20 FR France \n", "1544 36 FR France \n", "1545 38 FR France \n", "1546 36 FR France \n", "1547 45 FR France \n", "1548 43 FR France \n", "1549 28 FR France \n", "1550 5 FR France \n", "\n", "[1551 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.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": 7, "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": 8, "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": 9, "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": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "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 the annual trend." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "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": { "hideCode": true }, "source": [ "Since the peaks of the epidemic do not happen in autumn, near the transition\n", "between calendar years, we define the reference period for the annual\n", "incidence from September 1st of year $N$ to September 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 September 1st, we use the first day of the week containing September 1st.\n", "\n", "A final detail: the dataset starts in December 1990, the first peak is thus\n", "incomplete, We start the analysis with the first full peak." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\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": 13, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_september_week[:-1],\n", " first_september_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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2002 516689\n", "2018 542312\n", "2017 551041\n", "1996 564901\n", "2019 584066\n", "2015 604382\n", "2000 617597\n", "2001 619041\n", "2012 624573\n", "2005 628464\n", "2006 632833\n", "2011 642368\n", "1993 643387\n", "1995 652478\n", "1994 661409\n", "1998 677775\n", "1997 683434\n", "2014 685769\n", "2013 698332\n", "2007 717352\n", "2008 749478\n", "1999 756456\n", "2003 758363\n", "2004 777388\n", "2016 782114\n", "2010 829911\n", "1992 832939\n", "2009 842373\n", "dtype: int64" ] }, "execution_count": 15, "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 30 years. The typical epidemic affects only half as many people." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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