{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The SARS-CoV-2 (Covid-19) epidemic analysis" ] }, { "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\n", "import os.path\n", "from os import path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data on the Covid-19 incidence are available [here](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv). We download them as a file in CSV format." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data downloaded on 09.06.2020\n", "\n", "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", "| `Province/State` | Province/State |\n", "| `Country/Region` | Country/Region |\n", "| `Lat` | Latitude |\n", "| `Long` | Longitude |\n", "| `1/22/20` | Dates |" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/30/205/31/206/1/206/2/206/3/206/4/206/5/206/6/206/7/206/8/20
0NaNAfghanistan33.00000065.000000000000...14525152051575016509172671805418969195512034220917
1NaNAlbania41.15330020.168300000000...1122113711431164118411971212123212461263
2NaNAlgeria28.0339001.659600000000...9267939495139626973398319935100501015410265
3NaNAndorra42.5063001.521800000000...764764765844851852852852852852
4NaNAngola-11.20270017.873900000000...84868686868686889192
5NaNAntigua and Barbuda17.060800-61.796400000000...25262626262626262626
6NaNArgentina-38.416100-63.616700000000...16214168511741518319192682019721037220202279423620
7NaNArmenia40.06910045.038200000000...89279282949210009105241122111817123641313013325
8Australian Capital TerritoryAustralia-35.473500149.012400000000...107107107107107107107108108108
9New South WalesAustralia-33.868800151.209300000034...3095309831043104310631103110310931123114
10Northern TerritoryAustralia-12.463400130.845600000000...29292929292929292929
11QueenslandAustralia-28.016700153.400000000000...1058105810591059106010601061106110621062
12South AustraliaAustralia-34.928500138.600700000000...440440440440440440440440440440
13TasmaniaAustralia-41.454500145.970700000000...228228228228228228228228228228
14VictoriaAustralia-37.813600144.963100000011...1649165316631670167816811681168516871687
15Western AustraliaAustralia-31.950500115.860500000000...586589591592592592596599599599
16NaNAustria47.51620014.550100000000...16685167311673316759167711680516843168981690216968
17NaNAzerbaijan40.14310047.576900000000...5246549456625935626065226860723975537876
18NaNBahamas25.034300-77.396300000000...102102102102102102102103103103
19NaNBahrain26.02750050.550000000000...10793113981187112311128151329613835143831476315417
20NaNBangladesh23.68500090.356300000000...44608471534953452445551405756360391630266576968504
21NaNBarbados13.193900-59.543200000000...92929292929292929292
22NaNBelarus53.70980027.953400000000...41658425564340344255451164598146868477514863049453
23NaNBelgium50.8333004.000000000000...58186583815851758615586855876758907590725922659348
24NaNBenin9.3077002.315800000000...224232243244244261261261261288
25NaNBhutan27.51420090.433600000000...33434347474748485959
26NaNBolivia-16.290200-63.588700000000...959299821053110991116381224512728133581364313949
27NaNBosnia and Herzegovina43.91590017.679100000000...2494251025242535255125942606260626062704
28NaNBrazil-14.235000-51.925300000000...498440514849526447555383584016614941645771672846691758707412
29NaNBrunei4.535300114.727700000000...141141141141141141141141141141
..................................................................
236NaNTimor-Leste-8.874217125.727539000000...24242424242424242424
237NaNBelize13.193900-59.543200000000...18181818181819191919
238NaNLaos19.856270102.495496000000...19191919191919191919
239NaNLibya26.33510017.228331000000...130156168182196209239256256332
240NaNWest Bank and Gaza31.95220035.233200000000...447448449451457464464464472473
241NaNGuinea-Bissau11.803700-15.180400000000...1256125613391339133913391368136813681389
242NaNMali17.570692-3.996166000000...1250126513151351138614611485152315331547
243NaNSaint Kitts and Nevis17.357822-62.782998000000...15151515151515151515
244Northwest TerritoriesCanada64.825500-124.845700000000...5555555555
245YukonCanada64.282300-135.000000000000...11111111111111111111
246NaNKosovo42.60263620.902977000000...1064106410641064114211421142114211421263
247NaNBurma21.91620095.956000000000...224224228232233236236240242244
248AnguillaUnited Kingdom18.220600-63.068600000000...3333333333
249British Virgin IslandsUnited Kingdom18.420700-64.640000000000...8888888888
250Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...12121212121212121212
251NaNMS Zaandam0.0000000.000000000000...9999999999
252NaNBotswana-22.32850024.684900000000...35353840404040404042
253NaNBurundi-3.37310029.918900000000...63636363636363838383
254NaNSierra Leone8.460555-11.779889000000...8528618658969099149299469691001
255Bonaire, Sint Eustatius and SabaNetherlands12.178400-68.238500000000...6677777777
256NaNMalawi-13.25430834.301525000000...279284336358369393409409438443
257Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...13131313131313131313
258Saint Pierre and MiquelonFrance46.885200-56.315900000000...1111111111
259NaNSouth Sudan6.87700031.307000000000...99499499499499499499499413171604
260NaNWestern Sahara24.215500-12.885800000000...9999999999
261NaNSao Tome and Principe0.1863606.613081000000...479483484484484485499499513513
262NaNYemen15.55272748.516388000000...310323354399419453469482484496
263NaNComoros-11.64550043.333300000000...106106106132132132132141141141
264NaNTajikistan38.86103471.276093000000...3807393040134100419142894370445345294609
265NaNLesotho-29.60998828.233608000000...2222444444
\n", "

266 rows × 143 columns

\n", "
" ], "text/plain": [ " Province/State Country/Region Lat \\\n", "0 NaN Afghanistan 33.000000 \n", "1 NaN Albania 41.153300 \n", "2 NaN Algeria 28.033900 \n", "3 NaN Andorra 42.506300 \n", "4 NaN Angola -11.202700 \n", "5 NaN Antigua and Barbuda 17.060800 \n", "6 NaN Argentina -38.416100 \n", "7 NaN Armenia 40.069100 \n", "8 Australian Capital Territory Australia -35.473500 \n", "9 New South Wales Australia -33.868800 \n", "10 Northern Territory Australia -12.463400 \n", "11 Queensland Australia -28.016700 \n", "12 South Australia Australia -34.928500 \n", "13 Tasmania Australia -41.454500 \n", "14 Victoria Australia -37.813600 \n", "15 Western Australia Australia -31.950500 \n", "16 NaN Austria 47.516200 \n", "17 NaN Azerbaijan 40.143100 \n", "18 NaN Bahamas 25.034300 \n", "19 NaN Bahrain 26.027500 \n", "20 NaN Bangladesh 23.685000 \n", "21 NaN Barbados 13.193900 \n", "22 NaN Belarus 53.709800 \n", "23 NaN Belgium 50.833300 \n", "24 NaN Benin 9.307700 \n", "25 NaN Bhutan 27.514200 \n", "26 NaN Bolivia -16.290200 \n", "27 NaN Bosnia and Herzegovina 43.915900 \n", "28 NaN Brazil -14.235000 \n", "29 NaN Brunei 4.535300 \n", ".. ... ... ... \n", "236 NaN Timor-Leste -8.874217 \n", "237 NaN Belize 13.193900 \n", "238 NaN Laos 19.856270 \n", "239 NaN Libya 26.335100 \n", "240 NaN West Bank and Gaza 31.952200 \n", "241 NaN Guinea-Bissau 11.803700 \n", "242 NaN Mali 17.570692 \n", "243 NaN Saint Kitts and Nevis 17.357822 \n", "244 Northwest Territories Canada 64.825500 \n", "245 Yukon Canada 64.282300 \n", "246 NaN Kosovo 42.602636 \n", "247 NaN Burma 21.916200 \n", "248 Anguilla United Kingdom 18.220600 \n", "249 British Virgin Islands United Kingdom 18.420700 \n", "250 Turks and Caicos Islands United Kingdom 21.694000 \n", "251 NaN MS Zaandam 0.000000 \n", "252 NaN Botswana -22.328500 \n", "253 NaN Burundi -3.373100 \n", "254 NaN Sierra Leone 8.460555 \n", "255 Bonaire, Sint Eustatius and Saba Netherlands 12.178400 \n", "256 NaN Malawi -13.254308 \n", "257 Falkland Islands (Malvinas) United Kingdom -51.796300 \n", "258 Saint Pierre and Miquelon France 46.885200 \n", "259 NaN South Sudan 6.877000 \n", "260 NaN Western Sahara 24.215500 \n", "261 NaN Sao Tome and Principe 0.186360 \n", "262 NaN Yemen 15.552727 \n", "263 NaN Comoros -11.645500 \n", "264 NaN Tajikistan 38.861034 \n", "265 NaN Lesotho -29.609988 \n", "\n", " Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... \\\n", "0 65.000000 0 0 0 0 0 0 ... \n", "1 20.168300 0 0 0 0 0 0 ... \n", "2 1.659600 0 0 0 0 0 0 ... \n", "3 1.521800 0 0 0 0 0 0 ... \n", "4 17.873900 0 0 0 0 0 0 ... \n", "5 -61.796400 0 0 0 0 0 0 ... \n", "6 -63.616700 0 0 0 0 0 0 ... \n", "7 45.038200 0 0 0 0 0 0 ... \n", "8 149.012400 0 0 0 0 0 0 ... \n", "9 151.209300 0 0 0 0 3 4 ... \n", "10 130.845600 0 0 0 0 0 0 ... \n", "11 153.400000 0 0 0 0 0 0 ... \n", "12 138.600700 0 0 0 0 0 0 ... \n", "13 145.970700 0 0 0 0 0 0 ... \n", "14 144.963100 0 0 0 0 1 1 ... \n", "15 115.860500 0 0 0 0 0 0 ... \n", "16 14.550100 0 0 0 0 0 0 ... \n", "17 47.576900 0 0 0 0 0 0 ... \n", "18 -77.396300 0 0 0 0 0 0 ... \n", "19 50.550000 0 0 0 0 0 0 ... \n", "20 90.356300 0 0 0 0 0 0 ... \n", "21 -59.543200 0 0 0 0 0 0 ... \n", "22 27.953400 0 0 0 0 0 0 ... \n", "23 4.000000 0 0 0 0 0 0 ... \n", "24 2.315800 0 0 0 0 0 0 ... \n", "25 90.433600 0 0 0 0 0 0 ... \n", "26 -63.588700 0 0 0 0 0 0 ... \n", "27 17.679100 0 0 0 0 0 0 ... \n", "28 -51.925300 0 0 0 0 0 0 ... \n", "29 114.727700 0 0 0 0 0 0 ... \n", ".. ... ... ... ... ... ... ... ... \n", "236 125.727539 0 0 0 0 0 0 ... \n", "237 -59.543200 0 0 0 0 0 0 ... \n", "238 102.495496 0 0 0 0 0 0 ... \n", "239 17.228331 0 0 0 0 0 0 ... \n", "240 35.233200 0 0 0 0 0 0 ... \n", "241 -15.180400 0 0 0 0 0 0 ... \n", "242 -3.996166 0 0 0 0 0 0 ... \n", "243 -62.782998 0 0 0 0 0 0 ... \n", "244 -124.845700 0 0 0 0 0 0 ... \n", "245 -135.000000 0 0 0 0 0 0 ... \n", "246 20.902977 0 0 0 0 0 0 ... \n", "247 95.956000 0 0 0 0 0 0 ... \n", "248 -63.068600 0 0 0 0 0 0 ... \n", "249 -64.640000 0 0 0 0 0 0 ... \n", "250 -71.797900 0 0 0 0 0 0 ... \n", "251 0.000000 0 0 0 0 0 0 ... \n", "252 24.684900 0 0 0 0 0 0 ... \n", "253 29.918900 0 0 0 0 0 0 ... \n", "254 -11.779889 0 0 0 0 0 0 ... \n", "255 -68.238500 0 0 0 0 0 0 ... \n", "256 34.301525 0 0 0 0 0 0 ... \n", "257 -59.523600 0 0 0 0 0 0 ... \n", "258 -56.315900 0 0 0 0 0 0 ... \n", "259 31.307000 0 0 0 0 0 0 ... \n", "260 -12.885800 0 0 0 0 0 0 ... \n", "261 6.613081 0 0 0 0 0 0 ... \n", "262 48.516388 0 0 0 0 0 0 ... \n", "263 43.333300 0 0 0 0 0 0 ... \n", "264 71.276093 0 0 0 0 0 0 ... \n", "265 28.233608 0 0 0 0 0 0 ... \n", "\n", " 5/30/20 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 \\\n", "0 14525 15205 15750 16509 17267 18054 18969 19551 20342 \n", "1 1122 1137 1143 1164 1184 1197 1212 1232 1246 \n", "2 9267 9394 9513 9626 9733 9831 9935 10050 10154 \n", "3 764 764 765 844 851 852 852 852 852 \n", "4 84 86 86 86 86 86 86 88 91 \n", "5 25 26 26 26 26 26 26 26 26 \n", "6 16214 16851 17415 18319 19268 20197 21037 22020 22794 \n", "7 8927 9282 9492 10009 10524 11221 11817 12364 13130 \n", "8 107 107 107 107 107 107 107 108 108 \n", "9 3095 3098 3104 3104 3106 3110 3110 3109 3112 \n", "10 29 29 29 29 29 29 29 29 29 \n", "11 1058 1058 1059 1059 1060 1060 1061 1061 1062 \n", "12 440 440 440 440 440 440 440 440 440 \n", "13 228 228 228 228 228 228 228 228 228 \n", "14 1649 1653 1663 1670 1678 1681 1681 1685 1687 \n", "15 586 589 591 592 592 592 596 599 599 \n", "16 16685 16731 16733 16759 16771 16805 16843 16898 16902 \n", "17 5246 5494 5662 5935 6260 6522 6860 7239 7553 \n", "18 102 102 102 102 102 102 102 103 103 \n", "19 10793 11398 11871 12311 12815 13296 13835 14383 14763 \n", "20 44608 47153 49534 52445 55140 57563 60391 63026 65769 \n", "21 92 92 92 92 92 92 92 92 92 \n", "22 41658 42556 43403 44255 45116 45981 46868 47751 48630 \n", "23 58186 58381 58517 58615 58685 58767 58907 59072 59226 \n", "24 224 232 243 244 244 261 261 261 261 \n", "25 33 43 43 47 47 47 48 48 59 \n", "26 9592 9982 10531 10991 11638 12245 12728 13358 13643 \n", "27 2494 2510 2524 2535 2551 2594 2606 2606 2606 \n", "28 498440 514849 526447 555383 584016 614941 645771 672846 691758 \n", "29 141 141 141 141 141 141 141 141 141 \n", ".. ... ... ... ... ... ... ... ... ... \n", "236 24 24 24 24 24 24 24 24 24 \n", "237 18 18 18 18 18 18 19 19 19 \n", "238 19 19 19 19 19 19 19 19 19 \n", "239 130 156 168 182 196 209 239 256 256 \n", "240 447 448 449 451 457 464 464 464 472 \n", "241 1256 1256 1339 1339 1339 1339 1368 1368 1368 \n", "242 1250 1265 1315 1351 1386 1461 1485 1523 1533 \n", "243 15 15 15 15 15 15 15 15 15 \n", "244 5 5 5 5 5 5 5 5 5 \n", "245 11 11 11 11 11 11 11 11 11 \n", "246 1064 1064 1064 1064 1142 1142 1142 1142 1142 \n", "247 224 224 228 232 233 236 236 240 242 \n", "248 3 3 3 3 3 3 3 3 3 \n", "249 8 8 8 8 8 8 8 8 8 \n", "250 12 12 12 12 12 12 12 12 12 \n", "251 9 9 9 9 9 9 9 9 9 \n", "252 35 35 38 40 40 40 40 40 40 \n", "253 63 63 63 63 63 63 63 83 83 \n", "254 852 861 865 896 909 914 929 946 969 \n", "255 6 6 7 7 7 7 7 7 7 \n", "256 279 284 336 358 369 393 409 409 438 \n", "257 13 13 13 13 13 13 13 13 13 \n", "258 1 1 1 1 1 1 1 1 1 \n", "259 994 994 994 994 994 994 994 994 1317 \n", "260 9 9 9 9 9 9 9 9 9 \n", "261 479 483 484 484 484 485 499 499 513 \n", "262 310 323 354 399 419 453 469 482 484 \n", "263 106 106 106 132 132 132 132 141 141 \n", "264 3807 3930 4013 4100 4191 4289 4370 4453 4529 \n", "265 2 2 2 2 4 4 4 4 4 \n", "\n", " 6/8/20 \n", "0 20917 \n", "1 1263 \n", "2 10265 \n", "3 852 \n", "4 92 \n", "5 26 \n", "6 23620 \n", "7 13325 \n", "8 108 \n", "9 3114 \n", "10 29 \n", "11 1062 \n", "12 440 \n", "13 228 \n", "14 1687 \n", "15 599 \n", "16 16968 \n", "17 7876 \n", "18 103 \n", "19 15417 \n", "20 68504 \n", "21 92 \n", "22 49453 \n", "23 59348 \n", "24 288 \n", "25 59 \n", "26 13949 \n", "27 2704 \n", "28 707412 \n", "29 141 \n", ".. ... \n", "236 24 \n", "237 19 \n", "238 19 \n", "239 332 \n", "240 473 \n", "241 1389 \n", "242 1547 \n", "243 15 \n", "244 5 \n", "245 11 \n", "246 1263 \n", "247 244 \n", "248 3 \n", "249 8 \n", "250 12 \n", "251 9 \n", "252 42 \n", "253 83 \n", "254 1001 \n", "255 7 \n", "256 443 \n", "257 13 \n", "258 1 \n", "259 1604 \n", "260 9 \n", "261 513 \n", "262 496 \n", "263 141 \n", "264 4609 \n", "265 4 \n", "\n", "[266 rows x 143 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove Long and Lat columns (just for convenience) and make a spared copy in df_total for the \"world\" graph" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(raw_data)\n", "\n", "df_total=df.drop(columns=['Lat', 'Long'])\n", "df=df_total" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove \"not interesting\" countries" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "df=df.drop(df[(df['Country/Region'] != 'Belgium') & (df['Country/Region'] != 'China') & (df['Country/Region'] != 'France') & (df['Country/Region'] != 'Germany') & (df['Country/Region'] != 'Iran') & (df['Country/Region'] != 'Italy') & (df['Country/Region'] != 'Japan') & (df['Country/Region'] != 'Korea South') & (df['Country/Region'] != 'Netherlands') & (df['Country/Region'] != 'Portugal') & (df['Country/Region'] != 'Spain') & (df['Country/Region'] != 'United Kingdom') & (df['Country/Region'] != 'US')].index)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For convenience change China to Hong Kong in the Hong Kong line" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] } ], "source": [ "df=df.set_value(df[(df['Province/State'] == 'Hong Kong')].index, 'Country/Region', 'Hong Kong')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove colonies of France, Netherlands and UK" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "fr=df[(df['Country/Region']=='France')]\n", "fr=fr['Province/State']\n", "fr=fr.dropna()\n", "\n", "ne=df[(df['Country/Region']=='Netherlands')]\n", "ne=ne['Province/State']\n", "ne=ne.dropna()\n", "\n", "uk=df[(df['Country/Region']=='United Kingdom')]\n", "uk=uk['Province/State']\n", "uk=uk.dropna()\n", "\n", "df=df.drop(fr.index)\n", "df=df.drop(ne.index)\n", "df=df.drop(uk.index)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove Province/State column and compute total daily sum for China" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "df.drop('Province/State', axis = 1, inplace = True)\n", "grouped=df.groupby('Country/Region')\n", "df=grouped.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct graphs for the countries above" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax=df.transpose().plot()\n", "ax.set_xlabel(\"date\")\n", "ax.set_ylabel(\"confirmed cases\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we make the analogous graph for the Covid-19 incidence in the world" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df=df_total\n", "df.drop('Province/State', axis = 1, inplace = True)\n", "df.drop('Country/Region', axis = 1, inplace = True)\n", "df=df.sum(axis=0)\n", "\n", "ax=df.transpose().plot()\n", "ax.set_xlabel(\"date\")\n", "ax.set_ylabel(\"confirmed cases\")\n", "plt.show()" ] } ], "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": 2 }