1 Library Initilization

setwd("D:/VS/COVIDModel")
.libPaths("c:/R/win-library/3.6")
library(MASS)
library(EnvStats)
library(stringr)
library(bestNormalize)
library(Ecfun)
#library(COVID19)
library(covid19.analytics)
library(jsonlite)
library(zoo)
library(tidyverse)
library(stringr)
library(PerformanceAnalytics)
library(anytime)
library(caret)
library(relaimpo)
library(stargazer)
library(car)
library(kableExtra)
library(MCDA)
library(ahp)
library(sp)
library(sf)
source('./RSCript/COVIDFunctions.R')

2 Base Data Loading from the Local Drive

setwd("D:/VS/COVIDModel")
#Stringency Index Data
SI.date = "2020-05-13"
si_source <- read.csv("D:/VS/COVIDModel/Data/si_data_rev_14may.csv", stringsAsFactors = FALSE, check.names = FALSE, fileEncoding = "UTF-8-BOM")

# Country Codes
CountryCodeList <- read.csv("d:/VS/COVIDModel/data/country_JHU.csv", stringsAsFactors = FALSE, check.names = FALSE, fileEncoding = "UTF-8-BOM")


#World Bank Indicators
BaseData <- read.csv("D:/VS/COVIDModel/data/BaseData.csv", stringsAsFactors = FALSE, check.names = FALSE, fileEncoding = "UTF-8-BOM")
BaseData <- BaseData[, c(1:2, 5:12)]

#Human Development Index Data
HDI <- read.csv("D:/VS/COVIDModel/data/HDI_country.csv", stringsAsFactors = FALSE, check.names = FALSE, fileEncoding = "UTF-8-BOM")
HDI_data <- HDI[, c("country_code", "2018")]
colnames(HDI_data) <- c("country_code", "HDI")
BaseData <- merge(BaseData, HDI_data, by = "country_code", all.x = TRUE)

#Human Capital Index
HCI <- read.csv("D:/VS/COVIDModel/data/HCI_country.csv", stringsAsFactors = FALSE, check.names = FALSE, fileEncoding = "UTF-8-BOM")
HCI_data <- HCI[, c("country_code", "HCI")]
BaseData <- merge(BaseData, HCI_data, by = "country_code", all.x = TRUE)

# Disability-Adjusted Life Year
DALYs <- read.csv("D:/VS/COVIDModel/data/DALYs.csv", stringsAsFactors = FALSE, check.names = FALSE, fileEncoding = "UTF-8-BOM")
GHDx_Countries <- read.csv("D:/VS/COVIDModel/data/GHDx_Countries_Regions.csv", stringsAsFactors = FALSE, check.names = FALSE, fileEncoding = "UTF-8-BOM")
DALYs <- merge(DALYs, GHDx_Countries, by = "location_id", all.x = TRUE)
DALYs <- DALYs[, c("country_code", "val")]
colnames(DALYs) <- c("country_code", "DALYs")
BaseData <- merge(BaseData, DALYs, by = "country_code", all.x = TRUE)

si <- si_source[c("country_code", SI.date)]
colnames(si) <- c("country_code", "stringency_wt")
BaseData <- merge(BaseData, si, by = "country_code", all.x = TRUE)
BaseData_ss = BaseData[, c(1, 3:13)]

2.1 Base Data Table 

kable(BaseData) %>%
     kable_styling("striped", full_width = F, fixed_thead = T) %>%
     row_spec(0) %>%
     scroll_box(width = "910px", height = "440px")
country_code CNTRY_NAME Pop_2018 GDP_PC A65abp_2018 PopDen_Avg Physician Nurses HospitalBeds Expenditure HDI HCI DALYs stringency_wt
ABW Aruba 105845 25630.2665 13.550947 3689.0000 1.12000 3.12810 2.900000 29.647642 NA NA NA 69.99222
AFG Afghanistan 37172386 520.8966 2.584927 7401.9054 0.28400 0.32000 0.500000 36.652642 0.496 0.389 49634.29 65.60630
AGO Angola 30809762 3432.3857 2.216374 18571.4761 0.21490 1.31230 0.800000 16.671137 0.574 0.361 46502.58 26.76743
ALB Albania 2866376 5268.8485 13.744736 6669.7894 1.19980 3.59980 2.900000 23.349881 0.791 0.621 27819.79 40.22388
AND Andorra 77006 42029.7627 16.180000 2002.7870 3.33330 4.01280 2.500000 37.756894 0.857 NA 24265.59 66.77481
ARE United Arab Emirates 9630959 43004.9534 1.085001 889.0000 2.39440 5.58570 1.200000 4.040008 0.866 0.659 22964.95 20.51222
ARG Argentina 44494502 11683.9496 11.117789 3283.1201 3.96000 2.58000 5.000000 24.317775 0.830 0.611 26785.80 40.81880
ARM Armenia 2951776 4212.0709 11.253818 3867.1812 2.89900 5.60940 4.200000 22.120846 0.760 0.572 30106.52 NA
ASM American Samoa 55465 11466.6907 6.240000 1022.0000 0.78100 3.54840 2.600000 28.251402 NA NA 28049.05 NA
ATG Antigua and Barbuda 96286 16726.9808 8.799826 2500.0000 2.76470 3.11760 3.800000 29.647642 0.776 NA 25675.70 NA
AUS Australia 24992369 57373.6867 15.656475 1693.5530 3.58740 12.66120 3.800000 26.117262 0.938 0.803 24508.29 34.89946
AUT Austria 8847037 51461.9548 19.001566 3232.3376 5.14410 8.17780 7.600000 44.181771 0.914 0.793 27251.82 52.57904
AZE Azerbaijan 9942334 4721.1781 6.195183 5296.2390 3.44660 6.96210 4.700000 27.958731 0.754 0.597 31553.63 23.21431
BDI Burundi 11175378 271.7520 2.246940 9254.3940 0.05000 0.68000 0.800000 20.647628 0.423 0.380 50800.22 20.15967
BEL Belgium 11422068 47518.6360 18.788744 2651.8007 3.32340 11.10110 6.200000 39.573699 0.919 0.757 28213.32 34.54512
BEN Benin 11485048 901.5439 3.253605 3301.5703 0.15720 0.61450 0.500000 12.562992 0.520 0.406 48486.79 17.40293
BFA Burkina Faso 19751535 715.1229 2.406981 5164.7966 0.06000 0.56960 0.400000 19.532167 0.434 0.369 61147.47 72.56256
BGD Bangladesh 161356039 1698.2628 5.158391 21517.8127 0.52680 0.30670 0.800000 9.418916 0.614 0.479 29275.39 27.37832
BGR Bulgaria 7024216 9272.6293 21.021914 1196.4233 3.98790 5.30290 6.800000 32.332434 0.816 0.676 41673.69 25.01360
BHR Bahrain 1569439 24050.7575 2.426334 15024.8524 0.92570 2.49440 2.000000 20.176116 0.838 0.668 18543.73 30.66783
BHS Bahamas 385640 32217.8716 7.257602 1312.1584 1.93730 3.13860 2.900000 17.936746 0.805 NA 29329.62 NA
BIH Bosnia and Herzegovina 3323929 6065.6727 16.470317 2000.0000 2.00030 6.29980 3.500000 35.131803 0.769 0.618 34266.70 42.20474
BLR Belarus 9485386 6289.9386 14.845148 3499.7009 4.07720 11.43830 11.000000 29.321605 0.817 NA 39002.72 NA
BLZ Belize 383071 4884.7424 4.736459 2000.0000 1.12620 1.69470 1.300000 27.568219 0.720 NA 25909.81 32.94995
BMU Bermuda 63968 85748.0000 18.530000 1246.0000 1.76600 9.22780 6.300000 19.846098 NA NA 25512.08 39.84960
BOL Bolivia (Plurinational State of) 11353142 3548.5901 7.191947 1646.0942 1.61110 0.73570 1.100000 21.811043 0.703 NA 28080.68 118.16786
BRA Brazil 209469333 8920.7621 8.922838 5849.8937 2.14990 9.70830 2.200000 34.928812 0.761 0.560 28555.59 52.69659
BRB Barbados 286641 17949.2815 15.802694 660.0000 2.48860 6.02730 5.800000 35.212924 0.813 NA 29578.63 38.19656
BRN Brunei Darussalam 428962 31628.3288 4.873148 715.0000 1.77010 6.60120 2.700000 25.421083 0.845 NA 22350.00 36.44102
BTN Bhutan 754394 3243.2311 6.003012 623.0000 0.37150 1.50940 1.700000 18.453961 0.617 NA 24255.56 NA
BWA Botswana 2254126 8258.6417 4.223874 3000.0000 0.36880 3.30040 1.800000 23.638998 0.728 0.424 34146.00 27.97824
CAF Central African Republic 4666377 475.7212 2.825774 6726.2260 0.06290 0.20390 1.000000 7.894358 0.381 NA 89691.78 NA
CAN Canada 37058856 46232.9896 17.232007 2707.8907 2.61020 9.90560 2.700000 17.324548 0.922 0.799 25960.51 18.01809
CHE Switzerland 8516543 82796.5472 18.623217 2879.0918 4.23630 17.28280 4.700000 16.971559 0.946 0.767 24062.56 55.14040
CHL Chile 18729160 15923.3587 11.529802 3755.7118 1.08000 0.86000 2.200000 22.403473 0.847 0.674 24599.10 88.34206
CHN China 1392730000 9770.8471 10.920884 9870.0050 1.78550 2.30730 4.200000 28.251402 0.758 0.673 26300.21 75.96876
CIV Côte d’Ivoire 25069229 1715.5313 2.858943 1858.3258 0.23260 0.85170 0.400000 20.575553 0.516 0.353 50386.54 71.09026
CMR Cameroon 25216237 1533.7421 2.728877 16124.0000 0.08980 0.93370 1.300000 10.854785 0.563 0.394 44848.25 52.02868
COD Democratic Republic of the Congo 84068091 561.7772 3.017841 7959.4679 0.09000 0.47000 0.800000 11.498314 0.459 0.369 54769.25 26.24274
COG Congo 5244363 2147.7695 2.681720 3358.8594 0.11590 1.74310 1.600000 20.101405 0.609 0.420 45188.09 37.58853
COL Colombia 49648685 6667.7907 8.478047 4636.3469 2.08350 1.26270 1.500000 25.257438 0.761 0.593 21692.63 31.57038
COM Comoros 832322 1415.2636 3.007009 1190.0000 0.16990 0.92140 2.200000 20.101405 0.538 0.409 32888.07 NA
CPV Cabo Verde 543767 3635.4068 4.609327 3000.0000 0.76940 1.22720 2.100000 28.424829 0.651 NA 25869.86 179.07766
CRI Costa Rica 4999441 12027.3659 9.549848 4797.9887 1.14970 0.79570 1.200000 30.020615 0.794 0.619 21431.68 40.76266
CUB Cuba 11338138 8821.8189 15.186211 2204.3281 8.19000 7.79000 5.200000 22.383506 0.778 NA 27872.50 37.48511
CYM Cayman Islands 64174 81124.5135 13.130000 1278.0000 1.93900 3.12810 3.000000 29.647642 NA NA NA NA
CYP Cyprus 1189265 28159.3017 13.719062 1792.3269 1.95110 5.25170 3.400000 35.415707 0.873 0.751 23020.89 64.79993
CZE Czechia 10625695 23078.5735 19.420877 2005.1308 4.31400 8.40710 6.500000 32.102007 0.891 0.782 32123.76 36.15922
DEU Germany 82927922 47603.0276 21.461962 2037.3642 4.20870 13.19670 8.300000 28.170595 0.939 0.795 30834.29 31.28597
DJI Djibouti 958920 3082.5431 4.527579 3439.2612 0.22030 0.53500 1.400000 22.383506 0.495 NA 33630.96 114.96951
DMA Dominica 71625 7691.3451 11.430000 391.7141 1.08250 5.89990 3.800000 22.383506 0.724 NA 33886.40 76.75302
DNK Denmark 5797446 61350.3479 19.812953 2733.8476 4.45670 10.30040 2.500000 37.586034 0.930 0.774 28111.42 62.95395
DOM Dominican Republic 10627165 8050.6316 7.082817 6686.6982 1.56000 0.30990 1.600000 15.704552 0.745 0.492 29737.85 56.34624
DZA Algeria 42228429 4114.7151 6.362497 4051.6210 1.83000 2.24000 1.900000 22.383506 0.759 0.523 22953.96 50.85326
ECU Ecuador 17084357 6344.8720 7.157290 2826.0753 2.05000 1.20000 1.500000 22.383506 0.758 0.602 24186.41 42.08474
EGY Egypt 98423595 2549.1323 5.229779 21946.3583 0.79020 1.39890 1.600000 30.230369 0.700 0.486 28086.87 81.81099
ERI Eritrea 3538381 811.3800 3.950000 3417.0000 0.05570 0.64920 0.700000 12.562992 0.434 NA 45816.84 NA
ESP Spain 46723749 30370.8923 19.378508 4676.3705 4.06910 5.53070 3.000000 18.816089 0.893 0.743 25224.30 42.45657
EST Estonia 1320884 23266.3463 19.626357 2378.8761 3.46510 6.45260 5.000000 35.006568 0.882 0.747 34193.70 128.68112
ETH Ethiopia 109224559 772.3135 3.501133 8321.9784 0.10000 0.84000 0.300000 10.806414 0.470 0.385 36767.57 108.23213
FIN Finland 5518050 50152.3401 21.720788 1199.5169 3.80800 14.72300 4.400000 37.881170 0.925 0.814 29091.06 33.47612
FJI Fiji 883483 6266.9680 5.449680 3096.5327 0.83740 2.93800 2.300000 28.251402 0.724 NA 35978.20 NA
FRA France 66987244 41463.6440 20.034625 3988.0406 3.23490 9.68870 6.500000 47.511637 0.891 0.765 25376.30 34.11460
FSM Micronesia (Federated States of) 112640 3568.2908 3.996804 300.0000 0.17700 3.31900 3.200000 21.507162 0.614 NA 34801.23 NA
GAB Gabon 2119275 7952.5259 3.563907 2616.0560 0.36110 2.58060 6.300000 13.701733 0.702 0.453 36741.67 30.20095
GBR United Kingdom of Great Britain and Northern Ireland 66488991 42943.9023 18.395866 4029.4078 2.80580 8.28780 2.800000 36.032335 0.920 0.781 27664.54 38.93694
GEO Georgia 3731000 4717.1430 14.865491 2896.7173 5.09760 4.09140 2.600000 21.398766 0.786 0.614 38878.07 NA
GHA Ghana 29767108 2202.3122 3.068898 10517.8969 0.18000 1.20000 0.900000 30.532917 0.596 0.439 39775.01 17.33792
GIN Guinea 12414318 878.5996 2.926022 3256.8706 0.07880 0.38440 0.300000 12.562992 0.466 0.374 58109.00 NA
GMB Gambia 2280102 716.1185 2.589981 4245.7841 0.10770 1.62920 1.100000 10.651398 0.466 0.397 39182.44 31.85005
GNB Guinea-Bissau 1874309 777.9699 2.823716 5973.8862 0.20000 1.40000 1.000000 12.627126 0.461 NA 55151.76 NA
GNQ Equatorial Guinea 1308974 10261.7600 2.457877 4588.0000 0.40000 0.50000 2.100000 9.806560 0.588 NA 37430.12 NA
GRC Greece 10727668 20324.2536 21.655272 6619.4029 4.59200 3.37280 4.300000 46.550442 0.872 0.681 30425.98 57.95948
GRD Grenada 111454 10640.4968 9.621907 492.0000 1.44710 3.14470 3.700000 22.383506 0.763 NA 31670.58 NA
GRL Greenland 56025 48181.8700 9.570000 1250.0000 1.13500 8.59195 14.353400 37.756894 NA NA 33089.18 63.41477
GTM Guatemala 17247807 4549.0101 4.812073 5533.5684 0.35500 0.94700 0.600000 11.841197 0.651 0.460 28822.63 26.43050
GUM Guam 165768 35712.5621 9.849911 1429.0000 1.08400 7.21410 4.300000 25.421083 NA NA 31247.30 99.00314
GUY Guyana 779004 4979.0022 6.450271 3654.6123 0.79900 1.33980 1.600000 22.383506 0.670 0.495 34812.79 20.54277
HND Honduras 9587522 2500.1132 4.690618 1019.5430 0.31440 0.88150 0.700000 21.976087 0.623 0.490 24083.54 62.70662
HRV Croatia 4089400 14909.6932 20.445433 1107.9589 2.99620 8.11340 5.600000 38.476868 0.837 0.723 33702.43 35.99865
HTI Haiti 11123176 868.3420 4.949404 18010.0979 0.23450 0.68040 0.700000 12.562992 0.503 0.446 43644.21 NA
HUN Hungary 9768785 16161.9805 19.157725 2304.7777 3.23120 6.64250 7.000000 41.601221 0.845 0.703 37054.77 50.39711
IDN Indonesia 267663435 3893.5961 5.857166 6212.6763 0.37770 2.05830 1.200000 14.301779 0.707 0.535 28918.11 29.35859
IMN Man, Isle of 84077 80989.1722 20.600000 2953.0000 1.44900 2.27300 1.584279 37.756894 NA NA NA NA
IND India 1352617328 2009.9789 6.179956 7017.7348 0.77760 2.10710 0.700000 14.862593 0.647 0.440 34821.51 28.57908
IRL Ireland 4853506 78806.4320 13.865802 3419.2908 3.08610 14.29490 2.800000 23.283000 0.942 0.806 22827.71 32.83974
IRN Iran (Islamic Republic of) 81800269 5627.7493 6.184574 1072.7324 1.14000 1.87000 1.500000 19.637386 0.797 0.591 24796.70 38.42209
IRQ Iraq 38433600 5834.1662 3.323600 2561.6599 0.82170 1.67990 1.400000 25.303019 0.689 0.398 27100.99 63.36255
ISL Iceland 353574 73191.1163 14.795093 508.3561 3.97010 15.68060 3.200000 30.775804 0.938 0.740 22348.55 32.95967
ISR Israel 8883800 41715.0293 11.976986 3834.7727 3.21760 5.20440 3.100000 37.027773 0.906 0.763 19302.87 33.23459
ITA Italy 60431283 34483.2040 22.751680 5121.1514 4.09310 5.86930 3.400000 41.263633 0.883 0.769 27217.37 35.57398
JAM Jamaica 2934855 5354.2369 8.796643 1721.2794 1.31990 1.13970 1.700000 26.666371 0.726 0.544 28065.26 41.20042
JOR Jordan 9956011 4241.7888 3.846490 4131.0094 2.34360 3.38860 1.400000 26.123615 0.723 0.562 19379.48 61.75628
JPN Japan 126529100 39289.9584 27.576370 3788.8347 2.41180 11.51840 13.400000 16.441992 0.915 0.844 27366.88 17.71950
KAZ Kazakhstan 18276499 9812.6011 7.391846 833.1313 3.25220 8.48900 6.700000 14.705043 0.817 0.746 31327.04 88.75176
KEN Kenya 51393010 1710.5101 2.339187 15719.0314 0.19880 1.54210 1.400000 26.174526 0.579 0.518 36950.44 20.19120
KGZ Kyrgyzstan 6315800 1281.3637 4.494728 3504.0806 1.87600 6.42990 4.500000 26.046652 0.674 0.580 26618.30 26.31316
KHM Cambodia 16249798 1510.3249 4.568680 16916.0714 0.16820 0.95470 0.800000 14.959800 0.581 0.493 31981.13 NA
KIR Kiribati 115847 1625.2861 3.952627 2289.2253 0.20280 4.82870 1.900000 77.360789 0.623 0.479 45382.34 NA
KNA Saint Kitts and Nevis 52441 19275.4185 9.030000 757.0000 2.52300 3.97790 2.300000 24.305378 0.777 NA NA NA
KOR Republic of Korea 51635256 31362.7515 14.418556 7690.2882 2.36610 6.97350 11.500000 25.421083 0.906 0.845 22538.36 53.42279
KWT Kuwait 4137309 33994.4066 2.550472 6493.4568 2.57890 6.96940 2.000000 51.999664 0.808 0.576 16869.47 33.37819
LAO Lao People’s Democratic Republic 7061507 2542.4865 4.075150 2420.6386 0.49970 0.97560 1.500000 22.383506 0.604 0.452 36844.85 61.69925
LBN Lebanon 6848925 8269.7877 7.002368 31341.1887 2.27100 2.64480 2.900000 25.805421 0.730 0.538 21119.88 33.31959
LBR Liberia 4818977 677.3222 3.253432 3223.5510 0.03730 0.10070 0.800000 16.484887 0.465 0.319 45781.67 59.09309
LBY Libya 6678567 7241.7045 4.392040 1553.0000 2.15810 6.74160 3.700000 22.383506 0.708 NA 27628.01 116.01993
LCA Saint Lucia 181889 10566.0498 9.806530 1815.9384 0.10530 1.58730 1.300000 19.373038 0.745 NA 28439.89 NA
LIE Liechtenstein 37910 165028.2500 17.910000 755.0000 3.46510 8.59195 4.700000 37.756894 0.917 NA NA NA
LKA Sri Lanka 21670000 4102.4814 10.473220 7090.8370 0.95800 2.11550 3.600000 16.238911 0.780 0.584 23936.38 29.09383
LSO Lesotho 2108132 1299.1531 4.901087 1855.4433 0.06760 0.65080 1.300000 35.237078 0.518 0.371 67300.93 NA
LTU Lithuania 2789533 19153.4090 19.705033 1154.8017 4.33700 7.91550 7.300000 31.108741 0.869 0.712 41149.50 NA
LUX Luxembourg 607728 116639.8880 14.183154 2294.6793 3.02660 12.34960 4.800000 38.742076 0.909 0.692 25319.30 68.03818
LVA Latvia 1926542 17860.6172 20.043620 2657.0803 3.19460 4.81550 5.800000 43.015702 0.854 0.724 40947.22 NA
MAC Macau 631636 87208.5359 10.484203 72754.2891 1.56200 7.21410 5.295300 15.075829 NA 0.759 NA NA
MAR Morocco 36029138 3237.8834 7.012905 8277.7186 0.72730 1.09790 1.100000 24.683407 0.676 0.500 29151.04 52.62724
MCO Monaco 38682 185741.2800 33.150000 20629.6758 6.56170 20.26250 13.800000 37.756894 NA NA NA NA
MDA Republic of Moldova 3545883 3227.3122 11.469556 1193.6244 3.20020 4.51130 5.800000 27.319027 0.711 0.580 38891.64 46.71659
MDG Madagascar 26262368 527.5013 2.986717 6923.3060 0.18120 0.10590 0.200000 9.798578 0.521 0.374 48534.17 73.77367
MDV Maldives 515696 10330.6156 3.703345 1000.0000 1.03790 3.95040 4.300000 25.062388 0.719 NA 15334.39 NA
MEX Mexico 126190788 9673.4437 7.223685 8150.6268 2.24780 2.90000 1.500000 20.449238 0.767 0.607 25259.42 26.79982
MHL Marshall Islands 58413 3788.1636 4.240000 1398.1362 0.45540 3.54840 2.700000 57.865036 0.698 NA 37806.23 NA
MKD Republic of North Macedonia 2082958 6083.7189 13.670181 1100.0000 2.87360 3.79170 4.400000 27.869086 0.759 0.534 30829.17 NA
MLI Mali 19077690 899.6599 2.507230 15180.5143 0.13930 0.38200 0.100000 12.498858 0.427 0.317 69095.54 103.69007
MLT Malta 483530 30098.2833 20.349324 3134.4172 3.82600 8.95000 4.700000 34.915014 0.885 0.701 28125.93 NA
MMR Myanmar 53708395 1325.9529 5.784642 7505.3126 0.86400 0.97940 0.900000 16.750981 0.584 0.473 35568.82 28.78576
MNE Montenegro 622345 8844.2370 14.974937 2164.0000 2.33370 5.71790 4.000000 28.940219 0.816 0.615 32166.37 NA
MNG Mongolia 3170208 4121.7324 4.083539 2406.5120 2.88660 3.98460 7.000000 23.815795 0.735 0.626 33454.82 46.27480
MNP Northern Mariana Islands 56882 23258.6759 5.980000 1607.3727 0.44500 7.21410 4.300000 25.421083 NA NA 27138.94 NA
MOZ Mozambique 29495962 498.9572 2.890764 4253.0618 0.07350 0.44360 0.700000 0.020051 0.446 0.361 56426.69 32.65729
MRT Mauritania 4403319 1188.8346 3.141112 6643.5977 0.17900 1.03410 0.400000 22.383506 0.527 0.353 31474.58 26.90134
MUS Mauritius 1265303 11238.6904 11.474173 3510.2040 2.02460 3.38310 3.400000 23.267832 0.796 0.626 31645.39 94.47944
MWI Malawi 18143315 389.3980 2.645435 5751.7148 0.01570 0.25280 1.300000 20.865540 0.485 0.407 48137.77 26.87233
MYS Malaysia 31528585 11373.2330 6.671755 2917.8093 1.51320 4.07180 1.900000 15.931363 0.804 0.622 23253.37 39.81256
NAM Namibia 2448255 5931.4539 3.636032 4045.0419 0.37400 2.77500 2.700000 31.122577 0.645 0.435 39432.37 29.12585
NCL New Caledonia 284060 12579.6000 9.174158 2203.3447 1.98200 7.21410 4.300000 25.421083 NA NA NA NA
NER Niger 22442948 413.9803 2.595008 3646.1203 0.05000 0.31090 0.300000 10.164529 0.377 0.316 61357.01 67.67389
NGA Nigeria 195874740 2028.1820 2.747377 6064.4800 0.38270 1.45240 0.500000 18.478845 0.534 0.342 57098.05 37.91898
NIC Nicaragua 6465513 2028.8948 5.247497 3372.0485 1.00560 1.57750 0.900000 16.728304 0.651 0.531 20145.23 14.31593
NLD Netherlands 17231017 53024.0592 19.196193 2473.9182 3.50670 11.10430 4.700000 37.756894 0.934 0.800 26962.03 58.29695
NOR Norway 5314336 81697.2460 17.049222 967.4282 4.63360 18.12470 3.900000 37.703024 0.954 0.771 25119.19 40.47480
NPL Nepal 28087871 1033.9124 5.727671 30395.6367 0.65070 2.68540 0.300000 19.377208 0.579 0.490 30160.12 NA
NRU Nauru 12704 9888.8936 2.860000 1260.0620 1.23890 6.19470 5.000000 84.972358 NA NA NA NA
NZL New Zealand 4885500 41945.3317 15.652425 2419.3986 3.02520 10.95500 2.800000 30.686560 0.921 0.767 26137.73 52.18328
OMN Oman 4829483 16415.1573 2.392787 921.0000 1.96970 4.30040 1.600000 27.968638 0.834 0.622 19631.57 34.86040
PAK Pakistan 212215030 1482.4031 4.312774 9458.6742 0.97530 0.50040 0.600000 23.631328 0.560 0.389 39350.07 43.47958
PAN Panama 4176873 15575.0726 8.104731 6338.6782 1.56990 1.41000 2.300000 29.647642 0.795 0.532 22459.42 30.68192
PER Peru 31989256 6941.2358 8.088393 6973.2788 1.27000 1.35000 1.600000 20.319493 0.759 0.586 21116.53 75.95754
PHL Philippines 106651922 3102.7134 5.122569 11239.5361 1.27500 0.24000 1.000000 14.896826 0.712 0.548 31452.21 48.42471
PLW Palau 17907 15859.4349 8.500000 143.6626 1.18480 5.26070 4.800000 41.358824 0.814 NA NA NA
PNG Papua New Guinea 8606316 2730.2748 3.445269 1453.8555 0.05290 0.51250 4.024200 19.059287 0.543 0.384 52603.89 36.81704
POL Poland 37978548 15420.9112 17.517817 2254.9327 2.39980 5.72210 6.500000 34.217365 0.872 0.747 32741.56 30.03679
PRI Puerto Rico 3195153 31651.3482 18.674850 5035.0459 1.75000 3.12810 3.320000 29.647642 NA NA 30737.06 64.74040
PRK North Korea 25549819 1700.0000 9.334520 1453.8365 3.67450 4.43820 13.200000 12.562992 NA NA 33645.72 NA
PRT Portugal 10281762 23407.9066 21.953858 6068.3332 3.33560 6.37250 3.400000 41.448742 0.850 0.776 29488.43 40.24497
PRY Paraguay 6956071 5821.8143 6.430215 4219.4013 1.36600 1.16690 1.300000 15.622963 0.724 0.533 23315.58 41.89047
PSE West Bank 4569087 3198.8666 3.133306 6092.4053 0.83700 2.63510 1.200000 8.305154 0.690 0.554 20265.41 55.71990
PYF French Polynesia 277679 14323.8200 8.290508 1425.7722 1.69700 7.21410 4.300000 25.421083 NA NA NA NA
QAT Qatar 2781677 68793.7844 1.370070 14026.9244 0.00080 6.60280 1.200000 27.968638 0.848 0.615 16311.49 27.25396
RKS Kosovo 1845300 4302.2765 7.430000 2610.3152 2.89900 5.71790 4.700000 28.940219 NA NA NA NA
ROU Romania 19473936 12301.1864 18.338701 3235.8430 2.25860 6.09790 6.300000 31.591276 0.816 0.601 38896.25 25.15149
RUS Russian Federation 144478050 11288.8724 14.674708 1445.6886 4.01390 8.62120 8.200000 28.940219 0.824 0.729 41883.81 34.10561
RWA Rwanda 12301939 772.9445 2.938196 7578.3369 0.13460 0.83070 1.600000 18.903174 0.536 0.374 35325.34 42.99572
SAU Saudi Arabia 33699947 23338.9635 3.314088 1389.0000 2.39000 5.70000 2.700000 27.968638 0.857 0.585 20148.10 42.39795
SDN Sudan 41801533 977.2736 3.581161 1407.7720 0.41000 0.83000 0.800000 10.477965 0.508 0.379 35526.64 30.31861
SEN Senegal 15854360 1521.9536 3.086824 9336.1025 0.06920 0.31290 0.300000 18.298812 0.514 0.418 36347.16 44.42331
SGP Singapore 5638676 64581.9440 11.463380 7815.7468 2.30630 7.21410 2.400000 15.205542 0.935 0.884 17207.64 44.55065
SLB Solomon Islands 652858 2137.6907 3.598950 4251.8244 0.19990 2.13070 1.400000 31.921564 0.557 0.443 35356.97 NA
SLE Sierra Leone 7650154 533.9912 2.966556 11207.5462 0.02500 0.99620 0.400000 12.562992 0.438 0.351 61567.32 53.95067
SLV El Salvador 6420744 4058.2524 8.287090 3906.2752 1.56900 2.27800 1.300000 24.895007 0.667 0.501 28276.20 45.12273
SMR San Marino 33785 48494.5514 19.800000 1195.0000 6.14680 8.77680 3.800000 42.198874 NA NA NA 59.16167
SOM Somalia 15008154 314.5442 2.873311 2863.8412 0.02290 0.06110 0.900000 0.000237 NA NA 58321.04 52.37422
SRB Serbia 6982084 7246.7317 18.345793 3148.0612 3.12500 6.12030 5.700000 36.893403 0.799 0.755 36971.30 22.44401
SSD South Sudan 10975920 1119.6500 3.398483 5000.0000 0.06290 0.44360 0.700000 12.562992 0.413 0.302 71068.46 34.94162
STP Sao Tome and Principe 211028 2001.1409 2.925678 8850.2393 0.32000 2.26000 2.900000 12.562992 0.609 NA 29094.90 NA
SUR Suriname 575991 6234.0449 6.906370 1465.1056 1.22650 4.09830 3.100000 12.562992 0.724 NA 32526.55 85.84998
SVK Slovakia 5447011 19442.7058 15.629247 2263.7493 2.46430 9.17270 5.800000 12.562992 0.857 0.694 32009.70 24.65666
SVN Slovenia 2067372 26123.9739 19.606880 1989.1859 2.99530 9.68000 4.600000 12.562992 0.902 0.788 30107.05 64.55297
SWE Sweden 10183175 54608.3602 20.095525 3531.1422 5.39960 11.54340 2.600000 12.562992 0.937 0.800 26039.04 44.21572
SWZ Eswatini 1136191 4145.9741 4.014588 1053.5995 0.07960 2.00000 2.100000 12.562992 0.608 0.409 51280.62 42.09531
SYC Seychelles 96762 16433.9353 7.594546 1074.7065 0.94580 3.26130 3.600000 12.562992 0.801 0.678 29181.51 18.93996
SYR Syrian Arab Republic 16906283 2032.6200 4.503124 5413.9058 1.22000 1.46000 1.500000 12.562992 0.549 NA 35593.37 21.00999
TCA Turks and Caicos Islands 37665 27142.2278 4.760000 1022.4028 1.93815 3.12810 2.900000 12.562992 NA NA NA NA
TCD Chad 15477751 728.3432 2.480519 2021.5796 0.04750 0.36370 0.400000 12.562992 0.401 0.293 71486.42 74.98054
TGO Togo 7889094 679.2571 2.869468 5392.5977 0.04870 0.29800 0.700000 17.886013 0.513 0.413 43432.93 NA
THA Thailand 69428524 7273.5632 11.900893 8827.4164 0.80960 2.96470 2.100000 18.478845 0.765 0.604 27655.14 69.79101
TJK Tajikistan 9100837 826.6215 3.021888 6491.1939 1.70030 5.23300 4.800000 13.747442 0.656 0.533 31468.63 NA
TKM Turkmenistan 5850908 6966.6354 4.426634 3128.8886 2.22480 4.63060 7.400000 28.940219 0.710 NA 31282.38 NA
TLS Timor-Leste 1267972 2035.5339 4.316492 8946.4346 0.71970 1.67010 5.900000 36.356229 0.626 0.434 30155.03 NA
TON Tonga 103197 4364.0156 5.953661 288.8070 0.52230 3.93200 2.600000 31.401971 0.717 0.512 28461.62 NA
TTO Trinidad and Tobago 1389858 17129.9131 10.734954 1675.0000 2.66970 3.50930 3.000000 35.928554 0.799 0.613 32094.99 32.69817
TUN Tunisia 11565204 3447.5079 8.315679 4330.2120 1.27220 2.63510 2.300000 35.463454 0.739 0.508 23924.84 76.62440
TUR Turkey 82319724 9370.1763 8.483213 4156.1038 1.76050 2.63040 2.700000 33.872430 0.807 0.626 23498.33 24.26670
TUV Tuvalu 11508 3700.7107 6.280000 1316.8639 0.91740 3.76150 5.600000 28.251402 NA 0.554 NA NA
TZA United Republic of Tanzania 56318348 1050.6753 2.601299 10824.9011 0.03990 0.41260 0.700000 14.324209 0.528 0.400 42037.92 43.04168
UGA Uganda 42723139 642.7767 1.940987 7829.3496 0.09080 0.63030 0.500000 14.193050 0.528 0.382 42552.68 24.17427
UKR Ukraine 44622516 3095.1736 16.434686 1976.0174 3.00750 7.05760 8.800000 33.423702 0.750 0.647 47426.96 65.52201
URY Uruguay 3449299 17277.9701 14.814520 5556.2719 5.04990 1.92990 2.800000 34.989907 0.808 0.600 29758.62 118.12991
USA United States of America 327167434 62794.5857 15.807654 1719.9951 2.59480 8.55000 2.900000 22.367648 0.920 0.762 30958.02 26.82018
UZB Uzbekistan 32955400 1532.3717 4.419138 14209.0000 2.36850 12.07390 4.000000 17.089524 0.710 NA 29255.64 39.64021
VCT Saint Vincent and the Grenadines 110210 7361.4010 9.589787 1998.8293 0.65870 2.58010 2.600000 26.350707 0.728 NA 33096.44 NA
VEN Venezuela (Bolivarian Republic of) 28870195 16054.4900 7.266987 1619.5830 1.92400 1.12230 0.800000 22.383506 0.726 NA 26598.25 56.34925
VIR Virgin Islands 106977 35938.0244 19.288307 1221.4246 1.64500 3.12810 18.680000 29.647642 NA NA 37279.79 NA
VNM Viet Nam 95540395 2566.5970 7.274978 14689.0954 0.81990 1.43210 2.600000 21.945334 0.693 0.666 25809.19 38.91008
VUT Vanuatu 292680 3123.8934 3.640153 2610.3152 0.17060 1.38530 1.700000 25.632959 0.597 0.471 38792.10 NA
WSM Western Samoa 196130 4183.4079 4.800412 676.0000 0.34090 1.85650 1.000000 27.786397 0.707 NA 25162.69 NA
YEM Yemen 28498687 944.4085 2.876270 10631.8888 0.31040 0.73000 0.700000 12.562992 0.463 0.369 38986.96 57.56986
ZAF South Africa 57779622 6374.0282 5.318005 4269.1238 0.91010 3.51710 2.800000 34.416834 0.705 0.406 43715.12 19.61127
ZMB Zambia 17351822 1539.9002 2.099678 2712.4236 0.09130 0.89250 2.000000 20.808263 0.591 0.396 45609.19 49.65782
ZWE Zimbabwe 14439018 2146.9964 2.939524 2406.2843 0.07630 1.15460 1.700000 26.596372 0.563 0.441 47439.85 32.72253

3 Covid -19 Regression Model Function

setwd("D:/VS/COVIDModel")
covid19_RegModel <- function(SI.date, df_c, df_d, df_r, BaseData_ss, si_source, CountryCodeList, iv_norm, iv_rev, dv, normalization) {
    tryCatch({

        # Stringency
        si <- si_source[c("country_code", SI.date)]
        colnames(si) <- c("country_code", "stringency")

        #-----CovidCases

        data_Cov_ss <- covid_data_day(SI.date, df_c, df_d, df_r)
        data_Cov_ss_g <- covid_data_day_growth(SI.date, df_c, df_d, df_r)

        colnames(CountryCodeList) <- c("country", "country_code")
        data_Cov_ss1 <- merge(data_Cov_ss, CountryCodeList, by = 'country', all.x = T)

        data_Cov_ss2 <- data.frame(country_code = data_Cov_ss1$country_code, deaths = data_Cov_ss1$deaths,
                           confirmed = data_Cov_ss1$confirmed, active = data_Cov_ss1$active,
                           deathsGrowth = data_Cov_ss_g$deathsGrowth,
                           confirmedGrowth = data_Cov_ss_g$confirmedGrowth,
                           recoveredGrowth = data_Cov_ss_g$recoveredGrowth)
        #----- Data Integration
        data_m_si <- merge(si, BaseData_ss, by = "country_code", all.x = T)
        data_m <- merge(data_m_si, data_Cov_ss2, by = "country_code", all.x = T)
        maximumPercent <- data_m$confirmed / data_m$Pop_2018
        
        #data_m$susceptibles = (data_m$Pop_2018 - data_m$confirmed) * 100 / data_m$Pop_2018
        data_m$susceptibles = ((data_m$Pop_2018 * max(na.omit(maximumPercent)))- data_m$confirmed) / (data_m$Pop_2018 * max(na.omit(maximumPercent)))
        data_m$deaths = (data_m$deaths) / data_m$Pop_2018 * 1000000
        data_m$confirmed = (data_m$confirmed) / data_m$Pop_2018 * 1000000
        data_m$active = (data_m$active) / data_m$Pop_2018 * 1000000
        

        #################
        if (normalization == "BoxCox") {
            #---------- Weight Model Preparation 
            data_num <- sapply(data_m[, 2:ncol(data_m)], as.numeric)
            data_num <- cbind(data_m[, 1:2], data_num)
            data_a <- data_num[apply(data_num[, 2:ncol(data_num)] > 0, 1, all),]
            data_a <- na.omit(data_a)
            data_a <- data_a[, -3]

            #--Split Tab for normalization

            data_sp <- data_a[, 2:ncol(data_a)]
            data_sp <- sapply(data_sp, as.numeric)

            #-- Normalize Data

            data_normal <- boxcox(data_sp)
            data_normal <- as.data.frame(data_normal$x.t)
            data_normal <- cbind(data_a["country_code"], data_normal)

        }
        ############################

        ##########################
        if (normalization == "Scaling") {
            data_num <- sapply(data_m[, 2:ncol(data_m)], as.numeric)
            

            #Normalize Data
            data_normal <- scale(data_num)
            data_normal <- cbind(data_m["country_code"], data_normal)
        }
        ##########################

        #iv = append(iv_norm, iv_rev)
        iv = iv_norm
        #str(data_normal)
        df_dv = data.frame(y = data_normal[, dv])
        df_iv = data.frame(data_normal[, iv])
        df_model = cbind(df_dv, df_iv)

        model <- lm(y ~ ., data = df_model)
        model_sum <- summary(model)

        #Model VIF
        model_vif <- car::vif(model)
        
        model_weights <- calc.relimp(model, type = c("lmg"), rela = TRUE)
        #model_weights@lmg

        

        model_results <- list(model_sum, model_weights, model_vif)
        #------- Binning with Weights

        return(model_results)
    },
    error = function(err) {
        return(list(syn = word))
    })
}

4 Pre-COVID19 - Pairwise Risk Assessment

4.1 Pairwise Risk Evaluation Function 

setwd("D:/VS/COVIDModel")
covid19RiskEvaluation_pairwise <- function(SI.date, BaseData_ss, CountryCodeList, iv_norm, iv_rev, imp) {
    tryCatch({

        #Normalize Data
        data_m <- BaseData_ss
        data_num <- sapply(data_m[, 2:ncol(data_m)], as.numeric)
        data_normal <- scale(data_num)
        #data_normal <- as.data.frame(data_normal$x.t)
        data_normal <- cbind(data_m["country_code"], data_normal)

        #------- Binning with Weights

        for (i in iv_norm) {
            #if (colnames(data_normal[i]) == "confirmed") {
            data_normal[i] <- cut(as.numeric(unlist(data_normal[i])), 10, labels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10))
            data_normal[i] <- as.numeric(unlist(data_normal[i])) * as.numeric(imp[i])
        }

        for (i in iv_rev) {
            #if (colnames(data_normal[i]) == "confirmed") {
            data_normal[i] <- cut(as.numeric(unlist(data_normal[i])), 10, labels = c(10, 9, 8, 7, 6, 5, 4, 3, 2, 1))
            data_normal[i] <- as.numeric(unlist(data_normal[i])) * as.numeric(imp[i])
        }

        # --Risk Aggregation

        RiskVariables <- append(c("country_code"), iv_norm)
        RiskVariables <- append(RiskVariables, iv_rev)

        data_Risk <- data_normal[RiskVariables]
        data_Risk <- mutate(data_Risk, RiskValue = rowSums(data_Risk[, -1], na.rm = TRUE))

        data_Risk <- transform(data_Risk, RiskValue = as.numeric(unlist(RiskValue)))
        #data_Risk_ss <- data_Risk[, c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 16, 17)]

        data_Risk$RiskRank <- cut(data_Risk[, "RiskValue"], 10, labels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10))

        Risk <- data_Risk[c("country_code", "RiskValue", "RiskRank")]
        colnames(Risk) <- c("country_code", str_c("RiskValue_", SI.date), str_c("RiskRank_", SI.date))
        return(Risk)
    },
    error = function(err) {
        return(list(syn = word))
    })
}

4.2 Pre-COVID19 Weights Extraction 

setwd("D:/VS/COVIDModel")

A = t(matrix(c(1, 1/2, 1/3, 1/4, 1/5, 1/5, 1/5, 1/5, 1/5, 2, 1, 1/2, 1/3, 1/4, 1/4, 1/4, 1/4, 1/4, 3,
    2, 1, 1/2, 1/3, 1/3, 1/3, 1/3, 1/3, 4, 3, 2, 1, 1/2, 1/2, 1/2, 1/2, 1/2, 5, 4, 3, 2, 1,
    1, 1, 1, 1, 5, 4, 3, 2, 1, 1, 1, 1, 1, 5, 4, 3, 2, 1, 1, 1, 1, 1, 5, 4, 3, 2, 1, 1, 1, 1, 1,
    5, 4, 3, 2, 1, 1, 1, 1, 1), nrow = 9, ncol = 9))

colnames(A) = c("PopDen_Avg", "Pop_2018", "Expenditure", "GDP_PC", "DALYs", "Nurses", "Physician", "HospitalBeds", "A65abp_2018")
rownames(A) = c("PopDen_Avg", "Pop_2018", "Expenditure", "GDP_PC", "DALYs", "Nurses", "Physician", "HospitalBeds", "A65abp_2018"); 



B = t(matrix(c(1, 1 / 2, 1 / 3, 1 / 4, 1 / 5, 1 / 5, 1 / 5, 1 / 5, 1 / 5, 2, 1, 1 / 2, 1 / 3, 1 / 4, 1 / 4, 1 / 4, 1 / 4, 1 / 4, 3,
    2, 1, 1 / 2, 1 / 3, 1 / 3, 1 / 3, 1 / 3, 1 / 3, 4, 3, 2, 1, 1 / 2, 1 / 2, 1 / 2, 1 / 2, 1 / 2, 5, 4, 3, 2, 1,
    1, 1, 1, 1, 5, 4, 3, 2, 1, 1, 1, 1, 1, 5, 4, 3, 2, 1, 1, 1, 1, 1, 5, 4, 3, 2, 1, 1, 1, 1, 1,
    5, 4, 3, 2, 1, 1, 1, 1, 1), nrow = 9, ncol = 9))

colnames(B) = c("PopDen_Avg", "Pop_2018", "Expenditure", "GDP_PC", "DALYs", "Nurses", "Physician", "HospitalBeds", "A65abp_2018")
rownames(B) = c("PopDen_Avg", "Pop_2018", "Expenditure", "GDP_PC", "DALYs", "Nurses", "Physician", "HospitalBeds", "A65abp_2018")


criteria = matrix(c(1, 1,1,1), nrow = 2, ncol = 2)

colnames(criteria) = c("A", "B")
rownames(criteria) = c("A", "B")



alternatives = list(A, B)
weight = AHP(criteria, alternatives)
impPre <- weight

kable(A) %>%
     kable_styling("striped", full_width = F, fixed_thead = T) %>%
     row_spec(0) %>%
     scroll_box(width = "910px", height = "440px")
PopDen_Avg Pop_2018 Expenditure GDP_PC DALYs Nurses Physician HospitalBeds A65abp_2018
PopDen_Avg 1 0.5 0.3333333 0.2500000 0.2000000 0.2000000 0.2000000 0.2000000 0.2000000
Pop_2018 2 1.0 0.5000000 0.3333333 0.2500000 0.2500000 0.2500000 0.2500000 0.2500000
Expenditure 3 2.0 1.0000000 0.5000000 0.3333333 0.3333333 0.3333333 0.3333333 0.3333333
GDP_PC 4 3.0 2.0000000 1.0000000 0.5000000 0.5000000 0.5000000 0.5000000 0.5000000
DALYs 5 4.0 3.0000000 2.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
Nurses 5 4.0 3.0000000 2.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
Physician 5 4.0 3.0000000 2.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
HospitalBeds 5 4.0 3.0000000 2.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
A65abp_2018 5 4.0 3.0000000 2.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 
weight <- data.frame(weight)

kable(weight) %>%
     kable_styling("striped", full_width = F, fixed_thead = T) %>%
     row_spec(0) %>%
     scroll_box(width = "910px", height = "440px")
weight
PopDen_Avg 0.0270130
Pop_2018 0.0383154
Expenditure 0.0575581
GDP_PC 0.0905107
DALYs 0.1573205
Nurses 0.1573205
Physician 0.1573205
HospitalBeds 0.1573205
A65abp_2018 0.1573205 
print(str_c("Consistancy Ratio:  ", round(pairwiseConsistencyMeasures(A)$CR, 3)))
## [1] "Consistancy Ratio:  0.007"

4.3 Pre-COVID-19 Risk Evaluation 

    setwd("D:/VS/COVIDModel")
    PreCOVID19_Risk <- covid19RiskEvaluation_pairwise(SI.date = "2020-01-01",
    BaseData_ss = BaseData[, c(1, 3:13)],
    CountryCodeList <- read.csv("./data/country_JHU.csv", stringsAsFactors = FALSE, check.names = FALSE, fileEncoding = "UTF-8-BOM"),
    iv_norm = c("DALYs", "Pop_2018", "A65abp_2018", "PopDen_Avg"),
    iv_rev = c("GDP_PC", "Physician", "Nurses", "HospitalBeds", "Expenditure"),
    imp = impPre)

4.4 Pre-COVID-19 Risk Results 

kable(PreCOVID19_Risk) %>%
     kable_styling("striped", full_width = F, fixed_thead = T) %>%
     row_spec(0) %>%
     scroll_box(width = "910px", height = "440px")
country_code RiskValue_2020-01-01 RiskRank_2020-01-01
ABW 2.049788 2
AFG 1.859515 1
AGO 1.740866 1
ALB 2.216360 2
AND 3.141780 4
ARE 1.967624 2
ARG 2.845642 3
ARM 2.845642 3
ASM 1.801956 1
ATG 2.431239 2
AUS 3.961335 5
AUT 4.615223 6
AZE 3.060521 4
BDI 1.771411 1
BEL 4.243024 6
BEN 1.686840 1
BFA 2.059039 2
BGD 1.464541 1
BGR 4.004444 5
BHR 1.731615 1
BHS 2.149550 2
BIH 3.118079 4
BLR 4.476406 6
BLZ 1.644636 1
BMU 3.836967 5
BOL 1.587078 1
BRA 2.841753 3
BRB 3.275399 4
BRN 2.306871 2
BTN 1.429757 1
BWA 1.587078 1
CAF 2.415885 2
CAN 3.498625 4
CHE 4.723332 6
CHL 2.059039 2
CHN 2.960411 3
CIV 1.744398 1
CMR 1.583546 1
COD 1.871174 1
COG 1.744398 1
COL 1.744398 1
COM 1.587078 1
CPV 1.644636 1
CRI 1.644636 1
CUB 4.104207 5
CYM 2.478641 3
CYP 2.736628 3
CZE 4.252275 6
DEU 4.972068 7
DJI 1.587078 1
DMA 2.531001 3
DNK 4.176214 5
DOM 1.529520 1
DZA 1.901719 2
ECU 1.744398 1
EGY 1.568355 1
ERI 1.686840 1
ESP 3.250794 4
EST 3.837872 5
ETH 1.399212 1
FIN 4.557665 6
FJI 2.116598 2
FRA 3.985941 5
FSM 1.744398 1
GAB 2.001481 2
GBR 3.613742 4
GEO 3.474924 4
GHA 1.671649 1
GIN 1.844161 1
GMB 1.529520 1
GNB 1.844161 1
GNQ 1.529520 1
GRC 3.895430 5
GRD 2.216360 2
GRL 3.928383 5
GTM 1.372199 1
GUM 2.778832 3
GUY 1.587078 1
HND 1.429757 1
HRV 3.747361 5
HTI 1.740866 1
HUN 3.747361 5
IDN 1.410515 1
IMN 2.693519 3
IND 2.031679 2
IRL 3.836967 5
IRN 1.587078 1
IRQ 1.429757 1
ISL 3.961335 5
ISR 2.827139 3
ITA 3.523231 4
JAM 1.801956 1
JOR 1.644636 1
JPN 4.856952 7
KAZ 3.102725 4
KEN 1.541341 1
KGZ 2.588559 3
KHM 1.583546 1
KIR 2.461946 2
KNA 2.149550 2
KOR 3.592448 4
KWT 2.537103 3
LAO 1.429757 1
LBN 2.067329 2
LBR 1.686840 1
LBY 2.373681 2
LCA 1.587078 1
LIE 4.156806 5
LKA 2.001481 2
LSO 2.331476 2
LTU 4.252275 6
LUX 4.290426 6
LVA 3.804919 5
MAC 2.763962 3
MAR 1.456770 1
MCO 7.133113 10
MDA 3.375162 4
MDG 1.686840 1
MDV 1.744398 1
MEX 1.928732 2
MHL 2.131951 2
MKD 2.903200 3
MLI 2.212828 2
MLT 3.995192 5
MMR 1.713853 1
MNE 3.060521 4
MNG 2.531001 3
MNP 2.306871 2
MOZ 1.786603 1
MRT 1.429757 1
MUS 2.531001 3
MWI 1.744398 1
MYS 1.686840 1
NAM 1.959277 2
NCL 2.373681 2
NER 2.001481 2
NGA 1.940034 2
NIC 1.372199 1
NLD 4.085703 5
NOR 4.738686 6
NPL 1.695130 1
NRU 2.304626 2
NZL 3.556183 4
OMN 1.801956 1
PAK 1.967047 2
PAN 1.801956 1
PER 1.587078 1
PHL 1.713853 1
PLW 2.174156 2
PNG 2.216360 2
POL 3.432720 4
PRI 2.993711 3
PRK 3.732007 5
PRT 3.680551 5
PRY 1.529520 1
PSE 1.314641 1
PYF 2.373681 2
QAT 1.943181 2
RKS 2.273918 2
ROU 3.532482 4
RUS 4.200080 5
RWA 1.456770 1
SAU 2.049788 2
SDN 1.372199 1
SEN 1.456770 1
SGP 2.771988 3
SLB 1.644636 1
SLE 2.028494 2
SLV 1.901719 2
SMR 3.928383 5
SOM 1.786603 1
SRB 3.590041 4
SSD 2.158802 2
STP 1.556533 1
SUR 2.001481 2
SVK 3.665198 5
SVN 3.507877 4
SWE 4.070349 5
SWZ 1.844161 1
SYC 2.001481 2
SYR 1.686840 1
TCA 1.777351 1
TCD 2.158802 2
TGO 1.587078 1
THA 2.086053 2
TJK 2.316122 2
TKM 2.745880 3
TLS 2.043848 2
TON 1.801956 1
TTO 2.803438 3
TUN 2.174156 2
TUR 2.273918 2
TUV 1.801956 1
TZA 1.556533 1
UGA 1.556533 1
UKR 4.004444 5
URY 3.118079 4
USA 3.665767 5
UZB 2.872655 3
VCT 2.116598 2
VEN 1.744398 1
VIR 4.252275 6
VNM 1.798425 1
VUT 1.644636 1
WSM 1.487315 1
YEM 1.556533 1
ZAF 2.331476 2
ZMB 1.901719 2
ZWE 1.801956 1 
sp_data <- PreCOVID19_Risk[, c(1, 2)]
    map <- worldPlot(sp_data)
    map
## Scale on map varies by more than 10%, scale bar may be inaccurate

5 Post-COVID-19 - Regression Analysis

5.1 Post-COVID-19 Risk Evaluation Function 

setwd("D:/VS/COVIDModel")
#-- covid19RiskEvaluation with Regression Function  ------------
covid19RiskEvaluation_Reg <- function(SI.date, df_c, df_d, df_r, BaseData_ss, si_source, CountryCodeList, iv_norm, iv_rev, dv, normalization, imp) {
    tryCatch({

        #
        si <- si_source[c("country_code", SI.date)]
        colnames(si) <- c("country_code", "stringency")
        #-----CovidCases Integration

        data_Cov_ss <- covid_data_day(SI.date, df_c, df_d, df_r)
        data_Cov_ss_g <- covid_data_day_growth(SI.date, df_c, df_d, df_r)

        colnames(CountryCodeList) <- c("country", "country_code")
        data_Cov_ss1 <- merge(data_Cov_ss, CountryCodeList, by = 'country', all.x = T)

        #data_Cov_ss1 <- merge(data_Cov_ss, CountryCodeList, by = 'country', all.x = T)

        data_Cov_ss2 <- data.frame(country_code = data_Cov_ss1$country_code, deaths = data_Cov_ss1$deaths,
                           confirmed = data_Cov_ss1$confirmed, active = data_Cov_ss1$active,
                           deathsGrowth = data_Cov_ss_g$deathsGrowth,
                           confirmedGrowth = data_Cov_ss_g$confirmedGrowth,
                           recoveredGrowth = data_Cov_ss_g$recoveredGrowth)

        #----- Data Integration
        data_m_si <- merge(si, BaseData_ss, by = "country_code", all.x = T)
        data_m <- merge(data_m_si, data_Cov_ss2, by = "country_code", all.x = T)

        data_m$susceptibles = (data_m$Pop_2018 - data_m$confirmed) * 100 / data_m$Pop_2018
        data_m$deaths = (data_m$deaths) / data_m$Pop_2018 * 1000000
        data_m$confirmed = (data_m$confirmed) / data_m$Pop_2018 * 1000000
        data_m$active = (data_m$active) / data_m$Pop_2018 * 1000000

        #################
        if (normalization == "BoxCox") {
            #---------- Weight Model Preparation 
            data_num <- sapply(data_m[, 2:ncol(data_m)], as.numeric)
            data_num <- cbind(data_m[, 1:2], data_num)
            data_a <- data_num[apply(data_num[, 2:ncol(data_num)] > 0, 1, all),]
            data_a <- na.omit(data_a)
            data_a <- data_a[, -3]

            #--Split Tab for normalization

            data_sp <- data_a[, 2:ncol(data_a)]
            data_sp <- sapply(data_sp, as.numeric)

            data_normal <- boxcox(data_sp)
            data_normal <- as.data.frame(data_normal$x.t)
            data_normal <- cbind(data_a["country_code"], data_normal)

        }
        ############################

        ##########################
        if (normalization == "Scaling") {
            data_num <- sapply(data_m[, 2:ncol(data_m)], as.numeric)
      
            data_normal <- scale(data_num)
 
            data_normal <- cbind(data_m["country_code"], data_normal)
        }
        ##########################

        #-- Weights and weighted data Preparation

        #imp <- imp
        #imp@lmg

        #------- Binning with Weights

        for (i in iv_norm) {
            #if (colnames(data_normal[i]) == "confirmed") {
            data_normal[i] <- cut(as.numeric(unlist(data_normal[i])), 10, labels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10))
            data_normal[i] <- as.numeric(unlist(data_normal[i])) * imp[i]
        }

        for (i in iv_rev) {
            #if (colnames(data_normal[i]) == "confirmed") {
            data_normal[i] <- cut(as.numeric(unlist(data_normal[i])), 10, labels = c(10, 9, 8, 7, 6, 5, 4, 3, 2, 1))
            data_normal[i] <- as.numeric(unlist(data_normal[i])) * imp[i]
        }

        # --Risk Aggregation
        iv = append(iv_norm, iv_rev)
        RiskVariables <- append(c("country_code"), iv)
        data_Risk <- data_normal[RiskVariables]
        data_Risk <- mutate(data_Risk, RiskValue = rowSums(data_Risk[, -1], na.rm = TRUE))

        data_Risk <- transform(data_Risk, RiskValue = as.numeric(unlist(RiskValue)))
        #data_Risk_ss <- data_Risk[, c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 16, 17)]

        data_Risk$RiskRank <- cut(data_Risk[, "RiskValue"], 10, labels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10))

        Risk <- data_Risk[c("country_code", "RiskValue", "RiskRank")]
        colnames(Risk) <- c("country_code", str_c("RiskValue_", SI.date), str_c("RiskRank_", SI.date))

        return(Risk)
    },
    error = function(err) {
        return("Please check your data")
    })
}

5.2 Post-COVID-19 Static Model

5.2.1 Static Weights Extraction 

setwd("D:/VS/COVIDModel")
#-- Regression Model Results -------------
#
df_ModelRsquare = NULL
df_ModelWeights = NULL
df_ModelSummary = NULL
df_pValue = NULL
df_vif = NULL

    SI.date = SI.date
    BaseData_ss <- BaseData[, c(1, 3:length(BaseData))]
    # COVID-19 Data Integration
    df_c = read.csv("d:/VS/COVIDModel/Data/COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
                         stringsAsFactors = FALSE)
    df_d = read.csv("d:/VS/COVIDModel/Data/COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
                         stringsAsFactors = FALSE)
    df_r = read.csv("d:/VS/COVIDModel/Data/COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv",
                         stringsAsFactors = FALSE)
    dv = "deaths"

    normalization = "BoxCox"

    #### 

    iv_norm <- c("DALYs", "Pop_2018", "A65abp_2018", "PopDen_Avg", "Physician", "Nurses", "HospitalBeds",  
            "GDP_PC", "Expenditure" , "stringency")

    
    regModel <- covid19_RegModel(SI.date, df_c, df_d, df_r, BaseData_ss, si_source, CountryCodeList, iv_norm, iv_rev, dv, normalization)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
    impPost <- regModel[[2]]$lmg
    

    weights <- data.frame(t(data.frame(regModel[[2]]$lmg)))
    c <- colnames(weights)
    c1 <- NULL
    for (i in 1:length(c)) {
        c2 <- str_c("wt_", c[i])
        c1 <- append(c1, c2)
    }
    colnames(weights) <- c1

    Date <- data.frame(Date = SI.date)
    weights <- cbind(Date, weights)
    df_ModelWeights <- rbind(df_ModelWeights, weights)

    
    p.value <- data.frame(t(data.frame(regModel[[1]]$coefficients[, 4])))
    c <- colnames(p.value)
    c1 <- c("pv_Intercept")
    for (i in 2:length(c)) {
        c2 <- str_c("pv_", c[i])
        c1 <- append(c1, c2)
    }
    colnames(p.value) <- c1
    df_pValue <- rbind(df_pValue, p.value)
    df_ModelSummary <- cbind(df_ModelWeights, df_pValue)

    vif.value <- data.frame(t(data.frame(regModel[[3]])))
    d <- colnames(vif.value)
    #c1 <- c("pv_Intercept")
    d1<- NULL
    for (i in 1:length(d)) {
        d2 <- str_c("vif_", d[i])
        d1 <- append(d1, d2)
    }
    colnames(vif.value) <- d1
    df_vif <- rbind(df_vif, vif.value)
    df_ModelSummary <- cbind(df_ModelSummary, df_vif)

    rsquare <- data.frame(Date = SI.date, RSquare = regModel[[1]]$r.squared)
    df_ModelRsquare <- rbind(df_ModelRsquare, rsquare)
    df_ModelSummary <- cbind(df_ModelSummary, df_ModelRsquare[2])

    print(str_c("Data prepared for ", SI.date, " R-Square = ", regModel[[1]]$r.squared))
## [1] "Data prepared for 2020-05-13 R-Square = 0.685140962280837"
# The Outcome of Regression Model - Weights and P-Values
rownames(df_ModelSummary) <-c()
kable(df_ModelSummary) %>%
    kable_styling("striped", full_width = F, fixed_thead = T) %>%
    row_spec(0) %>%
    scroll_box(width = "910px", height = "440px")
Date wt_DALYs wt_Pop_2018 wt_A65abp_2018 wt_PopDen_Avg wt_Physician wt_Nurses wt_HospitalBeds wt_GDP_PC wt_Expenditure wt_stringency pv_Intercept pv_DALYs pv_Pop_2018 pv_A65abp_2018 pv_PopDen_Avg pv_Physician pv_Nurses pv_HospitalBeds pv_GDP_PC pv_Expenditure pv_stringency vif_DALYs vif_Pop_2018 vif_A65abp_2018 vif_PopDen_Avg vif_Physician vif_Nurses vif_HospitalBeds vif_GDP_PC vif_Expenditure vif_stringency RSquare
2020-05-13 0.0553956 0.0180688 0.1897825 0.0431309 0.1394969 0.159799 0.1014566 0.2204155 0.0400847 0.0323696 0.8707628 0.8328308 0.9282236 0.0082958 0.4604002 0.6528591 0.1599155 0.0992787 0.062661 0.832533 0.0818878 3.135882 1.369651 3.745179 1.524102 6.112762 6.440584 5.897403 8.048334 1.699711 1.285996 0.685141 
weight <- data.frame(impPost)
colnames(weight) <- c("weight")
kable(weight) %>%
     kable_styling("striped", full_width = F, fixed_thead = T) %>%
     row_spec(0) %>%
     scroll_box(width = "910px", height = "440px")
weight
DALYs 0.0553956
Pop_2018 0.0180688
A65abp_2018 0.1897825
PopDen_Avg 0.0431309
Physician 0.1394969
Nurses 0.1597990
HospitalBeds 0.1014566
GDP_PC 0.2204155
Expenditure 0.0400847
stringency 0.0323696

5.2.2 Static Risk Evaluation 

    setwd("D:/VS/COVIDModel")
    PostCOVID19_StaticRisk <- covid19RiskEvaluation_Reg(SI.date = SI.date,
    df_c = read.csv("d:/VS/COVIDModel/Data/COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
                         stringsAsFactors = FALSE),
    df_d = read.csv("d:/VS/COVIDModel/Data/COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
                         stringsAsFactors = FALSE),
    df_r = read.csv("d:/VS/COVIDModel/Data/COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv",
                         stringsAsFactors = FALSE),
    BaseData_ss <- BaseData[, c(1, 3:13)],
    si_source = si_source,

    #country code path 
    CountryCodeList <- read.csv("./data/country_JHU.csv", stringsAsFactors = FALSE, check.names = FALSE, fileEncoding = "UTF-8-BOM"),
    iv_norm = c("DALYs", "Pop_2018", "A65abp_2018", "PopDen_Avg", "Physician", "Nurses", "HospitalBeds"),
    iv_rev = c("GDP_PC", "Expenditure", "stringency"),
    dv = "deaths",
    normalization = "Scaling",
     imp = impPost)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)

5.2.3 Static Risk Results 

kable(PostCOVID19_StaticRisk) %>%
     kable_styling("striped", full_width = F, fixed_thead = T) %>%
     row_spec(0) %>%
     scroll_box(width = "910px", height = "440px")
country_code RiskValue_2020-05-13 RiskRank_2020-05-13
ABW 2.8859809 5
AFG 1.7963368 3
AGO 1.6128864 3
ALB 2.5496437 4
AND 4.0222982 7
ARE 2.4750328 4
ARG 2.8396077 5
AUS 4.8384578 8
AUT 5.4446295 9
AZE 2.8858823 5
BDI 1.5388893 3
BEL 5.3667184 9
BEN 1.3571472 3
BFA 1.7036582 3
BGD 1.6175125 3
BGR 4.0739798 7
BHR 2.1343612 4
BIH 3.3492808 6
BLZ 1.6174680 3
BMU 5.3734378 9
BOL 1.9844092 4
BRA 3.0362485 5
BRB 3.7552542 6
BRN 2.7385648 5
BWA 1.7775636 3
CAN 4.2945333 7
CHE 6.3620374 10
CHL 2.4190599 4
CHN 2.8559932 5
CIV 1.5497362 3
CMR 1.6375408 3
COD 1.4988046 3
COG 1.5310581 3
COL 1.8943980 3
CPV 1.8383842 3
CRI 2.2051838 4
CUB 4.4514496 7
CYP 3.3944783 6
CZE 4.4852861 8
DEU 5.8770840 10
DJI 1.7105254 3
DMA 2.8147887 5
DNK 5.4846140 9
DOM 2.0025646 4
DZA 2.0536362 4
ECU 1.9066627 3
EGY 1.9907173 4
ESP 4.0709737 7
EST 4.3017770 7
ETH 1.4944657 3
FIN 5.7746423 10
FRA 5.1046942 9
GAB 1.8119815 3
GBR 4.3858844 7
GHA 1.5513984 3
GMB 1.3772521 3
GRC 4.1915591 7
GRL 4.6543304 8
GTM 1.4238739 3
GUM 3.4853788 6
GUY 1.8506780 3
HND 1.4702560 3
HRV 4.3900985 7
HUN 4.2144279 7
IDN 1.6017417 3
IND 1.8448188 3
IRL 4.9682671 8
IRN 1.6970976 3
IRQ 1.4199704 3
ISL 5.5988868 9
ISR 3.7174716 6
ITA 4.6108775 8
JAM 1.9641026 4
JOR 1.9368688 4
JPN 5.7943621 10
KAZ 3.1635168 5
KEN 1.5391338 3
KGZ 2.5514930 4
KOR 4.2250656 7
KWT 2.8764380 5
LAO 1.5810471 3
LBN 2.5007007 4
LBR 1.4619710 3
LBY 2.6164337 5
LKA 2.3334680 4
LUX 5.8576210 10
MAR 1.8016958 3
MDA 3.0661812 5
MDG 1.5004330 3
MEX 2.0802692 4
MLI 1.9131009 3
MMR 1.7019819 3
MNG 2.6925926 5
MOZ 1.4632694 3
MRT 1.3573917 3
MUS 2.6547925 5
MWI 1.4804475 3
MYS 2.1481357 4
NAM 1.7263922 3
NER 1.6234888 3
NGA 1.6015923 3
NIC 1.4495335 3
NLD 5.2422358 9
NOR 6.1681345 10
NZL 4.4094583 7
OMN 2.1532569 4
PAK 1.8479006 3
PAN 2.1398839 4
PER 1.9241430 3
PHL 1.7143717 3
PNG 1.6879102 3
POL 3.8746269 7
PRI 3.6726861 6
PRT 4.2560751 7
PRY 1.7702120 3
PSE 1.4472506 3
QAT 2.9544216 5
ROU 3.9854180 7
RUS 4.1258231 7
RWA 1.5065287 3
SAU 2.3130559 4
SDN 1.2463561 2
SEN 1.4050721 3
SGP 3.7300731 6
SLE 1.7174657 3
SLV 2.0254912 4
SMR 5.3587479 9
SOM 1.4525081 3
SRB 3.7877319 6
SSD 1.6542299 3
SUR 2.3620118 4
SVK 3.8821439 7
SVN 4.3894505 7
SWE 5.3184367 9
SWZ 1.8959504 3
SYC 2.2931501 4
SYR 1.6187664 3
TCD 1.6758382 3
THA 2.4348012 4
TTO 2.9546347 5
TUN 2.2164608 4
TUR 2.2588490 4
TWN 0.0323696 1
TZA 1.4635139 3
UGA 1.3880134 3
UKR 4.0701343 7
URY 3.8562124 7
USA 4.5174007 8
UZB 3.0664218 5
VEN 2.1594477 4
VNM 2.0411460 4
YEM 1.4958835 3
ZAF 2.0957563 4
ZMB 1.6035123 3
ZWE 1.6112275 3 
sp_data <- PostCOVID19_StaticRisk[, c(1, 2)]
map <- worldPlot(sp_data)
map
## Scale on map varies by more than 10%, scale bar may be inaccurate

5.3 Post-COVID-19 Dynamic Model

5.3.1 Dynamic Weights Extraction 

setwd("D:/VS/COVIDModel")
#-- Regression Model Results -------------
#
df_ModelRsquare = NULL
df_ModelWeights = NULL
df_ModelSummary = NULL
df_pValue = NULL
df_vif = NULL

    SI.date = SI.date
    BaseData_ss <- BaseData[, c(1, 3:length(BaseData))]
    # COVID-19 Data Integration
    df_c = read.csv("d:/VS/COVIDModel/Data/COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
                         stringsAsFactors = FALSE)
    df_d = read.csv("d:/VS/COVIDModel/Data/COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
                         stringsAsFactors = FALSE)
    df_r = read.csv("d:/VS/COVIDModel/Data/COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv",
                         stringsAsFactors = FALSE)
    dv = "deaths"

    normalization = "BoxCox"

    #### 

    iv_norm <- c("DALYs", "Pop_2018", "A65abp_2018", "PopDen_Avg", "Physician", "Nurses", "HospitalBeds", "susceptibles" , "active" , "confirmedGrowth" , "deathsGrowth", 
            "GDP_PC", "Expenditure" , "stringency" , "recoveredGrowth" )

    
    regModel <- covid19_RegModel(SI.date, df_c, df_d, df_r, BaseData_ss, si_source, CountryCodeList, iv_norm, iv_rev, dv, normalization)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
    impPost <- regModel[[2]]$lmg
    

    weights <- data.frame(t(data.frame(regModel[[2]]$lmg)))
    c <- colnames(weights)
    c1 <- NULL
    for (i in 1:length(c)) {
        c2 <- str_c("wt_", c[i])
        c1 <- append(c1, c2)
    }
    colnames(weights) <- c1

    Date <- data.frame(Date = SI.date)
    weights <- cbind(Date, weights)
    df_ModelWeights <- rbind(df_ModelWeights, weights)

    
    p.value <- data.frame(t(data.frame(regModel[[1]]$coefficients[, 4])))
    c <- colnames(p.value)
    c1 <- c("pv_Intercept")
    for (i in 2:length(c)) {
        c2 <- str_c("pv_", c[i])
        c1 <- append(c1, c2)
    }
    colnames(p.value) <- c1
    df_pValue <- rbind(df_pValue, p.value)
    df_ModelSummary <- cbind(df_ModelWeights, df_pValue)

    vif.value <- data.frame(t(data.frame(regModel[[3]])))
    d <- colnames(vif.value)
    #c1 <- c("pv_Intercept")
    d1<- NULL
    for (i in 1:length(d)) {
        d2 <- str_c("vif_", d[i])
        d1 <- append(d1, d2)
    }
    colnames(vif.value) <- d1
    df_vif <- rbind(df_vif, vif.value)
    df_ModelSummary <- cbind(df_ModelSummary, df_vif)

    rsquare <- data.frame(Date = SI.date, RSquare = regModel[[1]]$r.squared)
    df_ModelRsquare <- rbind(df_ModelRsquare, rsquare)
    df_ModelSummary <- cbind(df_ModelSummary, df_ModelRsquare[2])

    print(str_c("Data prepared for ", SI.date, " R-Square = ", regModel[[1]]$r.squared))
## [1] "Data prepared for 2020-05-13 R-Square = 0.884346780535134"
# The Outcome of Regression Model - Weights and P-Values
rownames(df_ModelSummary) <-c()
kable(df_ModelSummary) %>%
    kable_styling("striped", full_width = F, fixed_thead = T) %>%
    row_spec(0) %>%
    scroll_box(width = "910px", height = "440px")
Date wt_DALYs wt_Pop_2018 wt_A65abp_2018 wt_PopDen_Avg wt_Physician wt_Nurses wt_HospitalBeds wt_susceptibles wt_active wt_confirmedGrowth wt_deathsGrowth wt_GDP_PC wt_Expenditure wt_stringency wt_recoveredGrowth pv_Intercept pv_DALYs pv_Pop_2018 pv_A65abp_2018 pv_PopDen_Avg pv_Physician pv_Nurses pv_HospitalBeds pv_susceptibles pv_active pv_confirmedGrowth pv_deathsGrowth pv_GDP_PC pv_Expenditure pv_stringency pv_recoveredGrowth vif_DALYs vif_Pop_2018 vif_A65abp_2018 vif_PopDen_Avg vif_Physician vif_Nurses vif_HospitalBeds vif_susceptibles vif_active vif_confirmedGrowth vif_deathsGrowth vif_GDP_PC vif_Expenditure vif_stringency vif_recoveredGrowth RSquare
2020-05-13 0.0263156 0.0099665 0.1038412 0.0232691 0.0711147 0.0803109 0.0520602 0.1491394 0.1989307 0.0418155 0.1096596 0.0937475 0.0220299 0.0135328 0.0042662 0.1015172 0.6255222 0.4589768 0.0007561 0.6044021 0.507399 0.0366399 0.1497271 0.0146218 1.37e-05 0.6223965 0.0019193 0.4896054 0.6197291 0.1123584 0.6842231 3.244279 1.701685 4.475173 1.666843 6.790883 6.789272 6.280662 3.473249 4.011424 3.77267 2.846386 10.60096 1.791639 1.427008 1.584557 0.8843468 
weight <- data.frame(impPost)
colnames(weight) <- c("weight")
kable(weight) %>%
     kable_styling("striped", full_width = F, fixed_thead = T) %>%
     row_spec(0) %>%
     scroll_box(width = "910px", height = "440px")
weight
DALYs 0.0263156
Pop_2018 0.0099665
A65abp_2018 0.1038412
PopDen_Avg 0.0232691
Physician 0.0711147
Nurses 0.0803109
HospitalBeds 0.0520602
susceptibles 0.1491394
active 0.1989307
confirmedGrowth 0.0418155
deathsGrowth 0.1096596
GDP_PC 0.0937475
Expenditure 0.0220299
stringency 0.0135328
recoveredGrowth 0.0042662

5.3.2 Dynamic Risk Evaluation 

    setwd("D:/VS/COVIDModel")
    PostCOVID19_DynamicRisk <- covid19RiskEvaluation_Reg(SI.date = SI.date,
    df_c = read.csv("d:/VS/COVIDModel/Data/COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
                         stringsAsFactors = FALSE),
    df_d = read.csv("d:/VS/COVIDModel/Data/COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
                         stringsAsFactors = FALSE),
    df_r = read.csv("d:/VS/COVIDModel/Data/COVID-19-master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv",
                         stringsAsFactors = FALSE),
    BaseData_ss <- BaseData[, c(1, 3:13)],
    si_source = si_source,

    #country code path 
    CountryCodeList <- read.csv("./data/country_JHU.csv", stringsAsFactors = FALSE, check.names = FALSE, fileEncoding = "UTF-8-BOM"),
    iv_norm = c("DALYs", "Pop_2018", "A65abp_2018", "PopDen_Avg", "Physician", "Nurses", "HospitalBeds", "susceptibles", "active", "confirmedGrowth", "deathsGrowth"),
    iv_rev = c("GDP_PC", "Expenditure", "stringency", "recoveredGrowth"),
    dv = "deaths",
    normalization = "Scaling",
     imp = impPost)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)

5.3.3 Dynamic Risk Results 

kable(PostCOVID19_DynamicRisk) %>%
     kable_styling("striped", full_width = F, fixed_thead = T) %>%
     row_spec(0) %>%
     scroll_box(width = "910px", height = "440px")
country_code RiskValue_2020-05-13 RiskRank_2020-05-13
ABW 3.2963017 7
AFG 2.7833100 6
AGO 2.6562681 6
ALB 3.1619273 7
AND 3.3142014 7
ARE 3.0827662 6
ARG 3.3487784 7
AUS 4.2568936 9
AUT 4.5781456 9
AZE 3.4233777 7
BDI 2.6127764 5
BEL 4.6348133 10
BEN 2.5178926 5
BFA 2.6827369 6
BGD 2.8218133 6
BGR 3.9599068 8
BHR 3.2033277 7
BIH 3.5734026 7
BLZ 2.6672086 6
BMU 4.4794975 9
BOL 2.9944745 6
BRA 3.5423684 7
BRB 3.7682756 8
BRN 3.1967813 7
BWA 2.7455335 6
CAN 3.8567695 8
CHE 4.8264186 10
CHL 3.1043517 6
CHN 3.3170087 7
CIV 2.8261558 6
CMR 2.8127789 6
COD 2.8518813 6
COG 2.6055155 5
COL 2.8589035 6
CPV 2.7982316 6
CRI 3.1849113 7
CUB 4.1332012 9
CYP 3.6608393 8
CZE 4.1424512 9
DEU 4.6493585 10
DJI 2.6943590 6
DMA 3.2776110 7
DNK 4.5809755 9
DOM 2.8654367 6
DZA 2.8854663 6
ECU 3.0190653 6
EGY 2.8546827 6
ESP 3.8121682 8
EST 4.0329448 8
ETH 2.5794634 5
FIN 4.7476336 10
FRA 4.4847035 9
GAB 2.8792594 6
GBR 4.3035273 9
GHA 2.7789493 6
GMB 2.5701943 5
GRC 4.0053701 8
GRL 4.1498904 9
GTM 2.7593466 6
GUM 1.7135376 4
GUY 2.7965992 6
HND 2.6336784 5
HRV 4.1003483 8
HUN 4.0038191 8
IDN 2.6981490 6
IND 2.9371256 6
IRL 3.9816409 8
IRN 2.7034596 6
IRQ 2.5966856 5
ISL 4.3067317 9
ISR 3.7079598 8
ITA 4.2571098 9
JAM 2.8536918 6
JOR 2.8291204 6
JPN 4.7926950 10
KAZ 3.4435878 7
KEN 2.8437469 6
KGZ 3.1451730 7
KOR 3.9845784 8
KWT 3.4724244 7
LAO 2.6402280 6
LBN 3.1356121 7
LBR 2.5712543 5
LBY 3.1553260 7
LKA 3.0482679 6
LUX 4.2264427 9
MAR 2.7642918 6
MDA 3.4546853 7
MDG 2.7084457 6
MEX 3.0628962 6
MLI 2.8935551 6
MMR 2.7087854 6
MNG 3.2089020 7
MOZ 2.5632659 5
MRT 3.8928199 8
MUS 3.1985902 7
MWI 2.6688526 6
MYS 2.9396719 6
NAM 2.7133070 6
NER 2.6386770 6
NGA 2.6435885 6
NIC 2.5833003 5
NLD 4.5585749 9
NOR 5.1032679 10
NZL 4.0535040 8
OMN 2.9564114 6
PAK 2.7806303 6
PAN 2.7774545 6
PER 3.0276046 6
PHL 2.7107745 6
PNG 2.6838913 6
POL 3.8352633 8
PRI 1.8600401 4
PRT 4.0858367 8
PRY 2.8599187 6
PSE 2.5660447 5
QAT 3.5459230 7
ROU 3.8878946 8
RUS 4.0081912 8
RWA 2.5994224 5
SAU 3.0409886 6
SDN 2.7724778 6
SEN 2.6988374 6
SGP 3.7657178 8
SLE 3.3759895 7
SLV 2.8789155 6
SMR 4.9924114 10
SOM 2.5953451 5
SRB 3.8089159 8
SSD 2.7017726 6
SUR 3.0366161 6
SVK 3.8229787 8
SVN 4.0611657 8
SWE 4.5617159 9
SWZ 2.8110182 6
SYC 2.9959217 6
SYR 2.6634864 6
TCD 2.7055690 6
THA 3.0958482 6
TTO 3.3436449 7
TUN 2.9757855 6
TUR 3.0063019 6
TWN 0.1692741 1
TZA 2.5749171 5
UGA 2.5799307 5
UKR 3.9324549 8
URY 3.8035443 8
USA 4.1771130 9
UZB 3.5093011 7
VEN 2.9274149 6
VNM 2.8941565 6
YEM 2.9592443 6
ZAF 3.0617680 6
ZMB 2.6410782 6
ZWE 2.6581078 6 
sp_data <- PostCOVID19_DynamicRisk[, c(1, 2)]
map <- worldPlot(sp_data)
map
## Scale on map varies by more than 10%, scale bar may be inaccurate

6 Difference of Pre COVID and Post COVID Dynamic Risk

RiskTable <- merge(PreCOVID19_Risk, PostCOVID19_DynamicRisk, by = "country_code", all.both = T)
RiskTable$Pre_Post_Difference <- RiskTable$`RiskValue_2020-05-13`- RiskTable$`RiskValue_2020-01-01`

kable(RiskTable) %>%
     kable_styling("striped", full_width = F, fixed_thead = T) %>%
     row_spec(0) %>%
     scroll_box(width = "910px", height = "440px")
country_code RiskValue_2020-01-01 RiskRank_2020-01-01 RiskValue_2020-05-13 RiskRank_2020-05-13 Pre_Post_Difference
ABW 2.049788 2 3.296302 7 1.2465139
AFG 1.859515 1 2.783310 6 0.9237954
AGO 1.740866 1 2.656268 6 0.9154018
ALB 2.216360 2 3.161927 7 0.9455673
AND 3.141780 4 3.314201 7 0.1724215
ARE 1.967624 2 3.082766 6 1.1151420
ARG 2.845642 3 3.348778 7 0.5031363
AUS 3.961335 5 4.256894 9 0.2955584
AUT 4.615223 6 4.578146 9 -0.0370773
AZE 3.060521 4 3.423378 7 0.3628569
BDI 1.771411 1 2.612776 5 0.8413650
BEL 4.243024 6 4.634813 10 0.3917897
BEN 1.686840 1 2.517893 5 0.8310523
BFA 2.059039 2 2.682737 6 0.6236974
BGD 1.464541 1 2.821813 6 1.3572726
BGR 4.004444 5 3.959907 8 -0.0445372
BHR 1.731615 1 3.203328 7 1.4717130
BIH 3.118079 4 3.573403 7 0.4553236
BLZ 1.644636 1 2.667209 6 1.0225726
BMU 3.836967 5 4.479497 9 0.6425302
BOL 1.587078 1 2.994474 6 1.4073966
BRA 2.841753 3 3.542368 7 0.7006152
BRB 3.275399 4 3.768276 8 0.4928761
BRN 2.306871 2 3.196781 7 0.8899106
BWA 1.587078 1 2.745534 6 1.1584557
CAN 3.498625 4 3.856769 8 0.3581442
CHE 4.723332 6 4.826419 10 0.1030861
CHL 2.059039 2 3.104352 6 1.0453122
CHN 2.960411 3 3.317009 7 0.3565979
CIV 1.744398 1 2.826156 6 1.0817574
CMR 1.583546 1 2.812779 6 1.2292330
COD 1.871174 1 2.851881 6 0.9807074
COG 1.744398 1 2.605516 5 0.8611171
COL 1.744398 1 2.858903 6 1.1145051
CPV 1.644636 1 2.798232 6 1.1535957
CRI 1.644636 1 3.184911 7 1.5402753
CUB 4.104207 5 4.133201 9 0.0289947
CYP 2.736628 3 3.660839 8 0.9242113
CZE 4.252275 6 4.142451 9 -0.1098242
DEU 4.972068 7 4.649358 10 -0.3227097
DJI 1.587078 1 2.694359 6 1.1072812
DMA 2.531001 3 3.277611 7 0.7466099
DNK 4.176214 5 4.580976 9 0.4047616
DOM 1.529520 1 2.865437 6 1.3359170
DZA 1.901719 2 2.885466 6 0.9837474
ECU 1.744398 1 3.019065 6 1.2746669
EGY 1.568355 1 2.854683 6 1.2863281
ESP 3.250794 4 3.812168 8 0.5613742
EST 3.837872 5 4.032945 8 0.1950729
ETH 1.399212 1 2.579463 5 1.1802511
FIN 4.557665 6 4.747634 10 0.1899689
FRA 3.985941 5 4.484704 9 0.4987627
GAB 2.001481 2 2.879259 6 0.8777781
GBR 3.613742 4 4.303527 9 0.6897857
GHA 1.671649 1 2.778949 6 1.1073003
GMB 1.529520 1 2.570194 5 1.0406746
GRC 3.895430 5 4.005370 8 0.1099402
GRL 3.928383 5 4.149890 9 0.2215078
GTM 1.372199 1 2.759347 6 1.3871474
GUM 2.778832 3 1.713538 4 -1.0652948
GUY 1.587078 1 2.796599 6 1.2095213
HND 1.429757 1 2.633678 5 1.2039211
HRV 3.747361 5 4.100348 8 0.3529872
HUN 3.747361 5 4.003819 8 0.2564580
IDN 1.410515 1 2.698149 6 1.2876344
IND 2.031679 2 2.937126 6 0.9054468
IRL 3.836967 5 3.981641 8 0.1446736
IRN 1.587078 1 2.703460 6 1.1163818
IRQ 1.429757 1 2.596686 5 1.1669283
ISL 3.961335 5 4.306732 9 0.3453965
ISR 2.827139 3 3.707960 8 0.8808210
ITA 3.523231 4 4.257110 9 0.7338790
JAM 1.801956 1 2.853692 6 1.0517353
JOR 1.644636 1 2.829120 6 1.1844845
JPN 4.856952 7 4.792695 10 -0.0642571
KAZ 3.102725 4 3.443588 7 0.3408627
KEN 1.541341 1 2.843747 6 1.3024054
KGZ 2.588559 3 3.145173 7 0.5566138
KOR 3.592448 4 3.984578 8 0.3921302
KWT 2.537103 3 3.472424 7 0.9353212
LAO 1.429757 1 2.640228 6 1.2104707
LBN 2.067329 2 3.135612 7 1.0682829
LBR 1.686840 1 2.571254 5 0.8844140
LBY 2.373681 2 3.155326 7 0.7816454
LKA 2.001481 2 3.048268 6 1.0467865
LUX 4.290426 6 4.226443 9 -0.0639829
MAR 1.456770 1 2.764292 6 1.3075214
MDA 3.375162 4 3.454685 7 0.0795234
MDG 1.686840 1 2.708446 6 1.0216054
MEX 1.928732 2 3.062896 6 1.1341642
MLI 2.212828 2 2.893555 6 0.6807272
MMR 1.713853 1 2.708785 6 0.9949321
MNG 2.531001 3 3.208902 7 0.6779009
MOZ 1.786603 1 2.563266 5 0.7766632
MRT 1.429757 1 3.892820 8 2.4630626
MUS 2.531001 3 3.198590 7 0.6675891
MWI 1.744398 1 2.668853 6 0.9244542
MYS 1.686840 1 2.939672 6 1.2528317
NAM 1.959277 2 2.713307 6 0.7540300
NER 2.001481 2 2.638677 6 0.6371957
NGA 1.940034 2 2.643588 6 0.7035542
NIC 1.372199 1 2.583300 5 1.2111011
NLD 4.085703 5 4.558575 9 0.4728718
NOR 4.738686 6 5.103268 10 0.3645817
NZL 3.556183 4 4.053504 8 0.4973205
OMN 1.801956 1 2.956411 6 1.1544549
PAK 1.967047 2 2.780630 6 0.8135830
PAN 1.801956 1 2.777455 6 0.9754980
PER 1.587078 1 3.027605 6 1.4405268
PHL 1.713853 1 2.710774 6 0.9969212
PNG 2.216360 2 2.683891 6 0.4675313
POL 3.432720 4 3.835263 8 0.4025433
PRI 2.993711 3 1.860040 4 -1.1336709
PRT 3.680551 5 4.085837 8 0.4052854
PRY 1.529520 1 2.859919 6 1.3303989
PSE 1.314641 1 2.566045 5 1.2514036
QAT 1.943181 2 3.545923 7 1.6027417
ROU 3.532482 4 3.887895 8 0.3554121
RUS 4.200080 5 4.008191 8 -0.1918888
RWA 1.456770 1 2.599422 5 1.1426521
SAU 2.049788 2 3.040989 6 0.9912008
SDN 1.372199 1 2.772478 6 1.4002786
SEN 1.456770 1 2.698837 6 1.2420670
SGP 2.771988 3 3.765718 8 0.9937296
SLE 2.028494 2 3.375990 7 1.3474951
SLV 1.901719 2 2.878916 6 0.9771966
SMR 3.928383 5 4.992411 10 1.0640288
SOM 1.786603 1 2.595345 5 0.8087423
SRB 3.590041 4 3.808916 8 0.2188753
SSD 2.158802 2 2.701773 6 0.5429707
SUR 2.001481 2 3.036616 6 1.0351348
SVK 3.665198 5 3.822979 8 0.1577812
SVN 3.507877 4 4.061166 8 0.5532887
SWE 4.070349 5 4.561716 9 0.4913666
SWZ 1.844161 1 2.811018 6 0.9668574
SYC 2.001481 2 2.995922 6 0.9944403
SYR 1.686840 1 2.663486 6 0.9766461
TCD 2.158802 2 2.705569 6 0.5467671
THA 2.086053 2 3.095848 6 1.0097956
TTO 2.803438 3 3.343645 7 0.5402070
TUN 2.174156 2 2.975786 6 0.8016298
TUR 2.273918 2 3.006302 6 0.7323838
TZA 1.556533 1 2.574917 5 1.0183843
UGA 1.556533 1 2.579931 5 1.0233979
UKR 4.004444 5 3.932455 8 -0.0719892
URY 3.118079 4 3.803544 8 0.6854653
USA 3.665767 5 4.177113 9 0.5113462
UZB 2.872655 3 3.509301 7 0.6366458
VEN 1.744398 1 2.927415 6 1.1830165
VNM 1.798425 1 2.894156 6 1.0957320
YEM 1.556533 1 2.959244 6 1.4027115
ZAF 2.331476 2 3.061768 6 0.7302918
ZMB 1.901719 2 2.641078 6 0.7393593
ZWE 1.801956 1 2.658108 6 0.8561513 
sp_data <- RiskTable[, c(1, 6)]
map <- worldPlot(sp_data)
map
## Scale on map varies by more than 10%, scale bar may be inaccurate

7 List of Post-COVID-19 Top Countries with Level of Risk

data_Covid <- covid_data_day(SI.date, df_c, df_d, df_r)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
countryList <- CountryCodeList
colnames(countryList) <- c("country", "country_code")
data_Covid <- merge(data_Covid, countryList, by = 'country', all.x = T)

TopCountries <- merge(data_Covid, RiskTable, by = "country_code", all.both=T)

#----- Data Integration
TopCountries_Base <- merge(TopCountries, BaseData, by = "country_code", all.x = T)

TopCountries_Base <- TopCountries_Base[,c(1:11,13)]
TopCountries_Base$MortalityRate <- round(TopCountries_Base$deaths / TopCountries_Base$Pop_2018 * 1000000,1)

TopCountries_Base <- TopCountries_Base[order(-TopCountries_Base[13]),]


kable(TopCountries_Base) %>%
     kable_styling("striped", full_width = F, fixed_thead = T) %>%
     row_spec(0) %>%
     scroll_box(width = "910px", height = "440px")
country_code country confirmed recovered deaths active RiskValue_2020-01-01 RiskRank_2020-01-01 RiskValue_2020-05-13 RiskRank_2020-05-13 Pre_Post_Difference Pop_2018 MortalityRate
125 SMR San Marino 643 161 41 482 3.928383 5 4.992411 10 1.0640288 33785 1213.6
12 BEL Belgium 53981 13937 8843 40044 4.243024 6 4.634813 10 0.3917897 11422068 774.2
5 AND Andorra 760 576 49 184 3.141780 4 3.314201 7 0.1724215 77006 636.3
48 ESP Spain 228691 140823 27104 87868 3.250794 4 3.812168 8 0.5613742 46723749 580.1
71 ITA Italy 222104 112541 31106 109563 3.523231 4 4.257110 9 0.7338790 60431283 514.7
54 GBR United Kingdom 229705 0 33186 229705 3.613742 4 4.303527 9 0.6897857 66488991 499.1
52 FRA France 175981 57368 27032 118613 3.985941 5 4.484704 9 0.4987627 66987244 403.5
132 SWE Sweden 27909 4971 3460 22938 4.070349 5 4.561716 9 0.4913666 10183175 339.8
102 NLD Netherlands 43217 6 5562 43211 4.085703 5 4.558575 9 0.4728718 17231017 322.8
66 IRL Ireland 23401 19470 1497 3931 3.836967 5 3.981641 8 0.1446736 4853506 308.4
145 USA US 1390406 243430 84119 1146976 3.665767 5 4.177113 9 0.5113462 327167434 257.1
27 CHE Switzerland 30413 27100 1870 3313 4.723332 6 4.826419 10 0.1030861 8516543 219.6
85 LUX Luxembourg 3904 3629 103 275 4.290426 6 4.226443 9 -0.0639829 607728 169.5
26 CAN Canada 73568 35177 5425 38391 3.498625 4 3.856769 8 0.3581442 37058856 146.4
46 ECU Ecuador 30486 3433 2334 27053 1.744398 1 3.019065 6 1.2746669 17084357 136.6
20 BMU Bermuda 121 66 8 55 3.836967 5 4.479497 9 0.6425302 63968 125.1
112 PRT Portugal 28132 3182 1175 24950 3.680551 5 4.085837 8 0.4052854 10281762 114.3
40 DEU Germany 174098 148700 7861 25398 4.972068 7 4.649358 10 -0.3227097 82927922 94.8
43 DNK Denmark 10667 8663 533 2004 4.176214 5 4.580976 9 0.4047616 5797446 91.9
67 IRN Iran 112725 89428 6783 23297 1.587078 1 2.703460 6 1.1163818 81800269 82.9
9 AUT Austria 15997 14304 624 1693 4.615223 6 4.578146 9 -0.0370773 8847037 70.5
108 PER Peru 76306 24324 2169 51982 1.587078 1 3.027605 6 1.4405268 31989256 67.8
22 BRA Brazil 190137 78424 13240 111713 2.841753 3 3.542368 7 0.7006152 209469333 63.2
107 PAN Panama 8944 6067 256 2877 1.801956 1 2.777455 6 0.9754980 4176873 61.3
116 ROU Romania 16002 7961 1036 8041 3.532482 4 3.887895 8 0.3554121 19473936 53.2
87 MDA Moldova 5406 2176 185 3230 3.375162 4 3.454685 7 0.0795234 3545883 52.2
51 FIN Finland 6054 4300 284 1754 4.557665 6 4.747634 10 0.1899689 5518050 51.5
131 SVN Slovenia 1463 260 103 1203 3.507877 4 4.061166 8 0.5532887 2067372 49.8
140 TUR Turkey 143114 101715 3952 41399 2.273918 2 3.006302 6 0.7323838 82319724 48.0
49 EST Estonia 1751 777 61 974 3.837872 5 4.032945 8 0.1950729 1320884 46.2
63 HUN Hungary 3341 1102 430 2239 3.747361 5 4.003819 8 0.2564580 9768785 44.0
103 NOR Norway 8175 32 229 8143 4.738686 6 5.103268 10 0.3645817 5314336 43.1
44 DOM Dominican Republic 11196 3221 409 7975 1.529520 1 2.865437 6 1.3359170 10627165 38.5
18 BIH Bosnia and Herzegovina 2181 1228 120 953 3.118079 4 3.573403 7 0.4553236 3323929 36.1
89 MEX Mexico 40186 26990 4220 13196 1.928732 2 3.062896 6 1.1341642 126190788 33.4
127 SRB Serbia 10295 3824 222 6471 3.590041 4 3.808916 8 0.2188753 6982084 31.8
70 ISR Israel 16548 12232 264 4316 2.827139 3 3.707960 8 0.8808210 8883800 29.7
1 ABW Aruba 101 91 3 10 2.049788 2 3.296302 7 1.2465139 105845 28.3
69 ISL Iceland 1802 1780 10 22 3.961335 5 4.306732 9 0.3453965 353574 28.3
39 CZE Czechia 8269 5047 290 3222 4.252275 6 4.142451 9 -0.1098242 10625695 27.3
23 BRB Barbados 85 65 7 20 3.275399 4 3.768276 8 0.4928761 286641 24.4
62 HRV Croatia 2213 1834 94 379 3.747361 5 4.100348 8 0.3529872 4089400 23.0
111 POL Poland 17204 6410 861 10794 3.432720 4 3.835263 8 0.4025433 37978548 22.7
6 ARE United Arab Emirates 20386 6523 206 13863 1.967624 2 3.082766 6 1.1151420 9630959 21.4
79 KWT Kuwait 11028 3263 82 7765 2.537103 3 3.472424 7 0.9353212 4137309 19.8
28 CHL Chile 34381 14865 346 19516 2.059039 2 3.104352 6 1.0453122 18729160 18.5
117 RUS Russia 242271 48003 2212 194268 4.200080 5 4.008191 8 -0.1918888 144478050 15.3
57 GRC Greece 2760 1374 155 1386 3.895430 5 4.005370 8 0.1099402 10727668 14.4
38 CYP Cyprus 905 449 17 456 2.736628 3 3.660839 8 0.9242113 1189265 14.3
16 BGR Bulgaria 2069 499 96 1570 4.004444 5 3.959907 8 -0.0445372 7024216 13.7
60 GUY Guyana 113 41 10 72 1.587078 1 2.796599 6 1.2095213 779004 12.8
61 HND Honduras 2255 237 123 2018 1.429757 1 2.633678 5 1.2039211 9587522 12.8
21 BOL Bolivia 3148 339 142 2809 1.587078 1 2.994474 6 1.4073966 11353142 12.5
45 DZA Algeria 6253 3058 522 3195 1.901719 2 2.885466 6 0.9837474 42228429 12.4
4 ALB Albania 880 688 31 192 2.216360 2 3.161927 7 0.9455673 2866376 10.8
34 COL Colombia 12930 3133 509 9797 1.744398 1 2.858903 6 1.1145051 49648685 10.3
143 UKR Ukraine 16425 3716 439 12709 4.004444 5 3.932455 8 -0.0719892 44622516 9.8
119 SAU Saudi Arabia 44830 17622 273 27208 2.049788 2 3.040989 6 0.9912008 33699947 8.1
95 MUS Mauritius 332 322 10 10 2.531001 3 3.198590 7 0.6675891 1265303 7.9
7 ARG Argentina 6879 2266 329 4613 2.845642 3 3.348778 7 0.5031363 44494502 7.4
109 PHL Philippines 11618 2251 772 9367 1.713853 1 2.710774 6 0.9969212 106651922 7.2
37 CUB Cuba 1810 1326 79 484 4.104207 5 4.133201 9 0.0289947 11338138 7.0
17 BHR Bahrain 5816 2205 10 3611 1.731615 1 3.203328 7 1.4717130 1569439 6.4
138 TTO Trinidad and Tobago 116 107 8 9 2.803438 3 3.343645 7 0.5402070 1389858 5.8
47 EGY Egypt 10431 2486 556 7945 1.568355 1 2.854683 6 1.2863281 98423595 5.6
144 URY Uruguay 719 545 19 174 3.118079 4 3.803544 8 0.6854653 3449299 5.5
31 CMR Cameroon 2800 1543 136 1257 1.583546 1 2.812779 6 1.2292330 25216237 5.4
74 JPN Japan 16049 8920 678 7129 4.856952 7 4.792695 10 -0.0642571 126529100 5.4
19 BLZ Belize 18 16 2 2 1.644636 1 2.667209 6 1.0225726 383071 5.2
86 MAR Morocco 6512 3131 188 3381 1.456770 1 2.764292 6 1.3075214 36029138 5.2
78 KOR Korea, South 10991 9762 260 1229 3.592448 4 3.984578 8 0.3921302 51635256 5.0
115 QAT Qatar 26539 3143 14 23396 1.943181 2 3.545923 7 1.6027417 2781677 5.0
130 SVK Slovakia 1469 1060 27 409 3.665198 5 3.822979 8 0.1577812 5447011 5.0
104 NZL New Zealand 1497 1411 21 86 3.556183 4 4.053504 8 0.4973205 4885500 4.3
53 GAB Gabon 1004 152 9 852 2.001481 2 2.879259 6 0.8777781 2119275 4.2
82 LBR Liberia 213 101 20 112 1.686840 1 2.571254 5 0.8844140 4818977 4.2
8 AUS Australia 6989 6297 98 692 3.961335 5 4.256894 9 0.2955584 24992369 3.9
139 TUN Tunisia 1032 759 45 273 2.174156 2 2.975786 6 0.8016298 11565204 3.9
64 IDN Indonesia 15438 3287 1028 12151 1.410515 1 2.698149 6 1.2876344 267663435 3.8
81 LBN Lebanon 878 236 26 642 2.067329 2 3.135612 7 1.0682829 6848925 3.8
150 ZAF South Africa 12074 4745 219 7329 2.331476 2 3.061768 6 0.7302918 57779622 3.8
35 CPV Cabo Verde 289 61 2 228 1.644636 1 2.798232 6 1.1535957 543767 3.7
122 SGP Singapore 25346 4809 21 20537 2.771988 3 3.765718 8 0.9937296 5638676 3.7
2 AFG Afghanistan 5226 648 132 4578 1.859515 1 2.783310 6 0.9237954 37172386 3.6
106 PAK Pakistan 35298 8899 761 26399 1.967047 2 2.780630 6 0.8135830 212215030 3.6
10 AZE Azerbaijan 2758 1789 35 969 3.060521 4 3.423378 7 0.3628569 9942334 3.5
97 MYS Malaysia 6779 5281 111 1498 1.686840 1 2.939672 6 1.2528317 31528585 3.5
105 OMN Oman 4019 1289 17 2730 1.801956 1 2.956411 6 1.1544549 4829483 3.5
126 SOM Somalia 1219 130 52 1089 1.786603 1 2.595345 5 0.8087423 15008154 3.5
123 SLE Sierra Leone 387 97 26 290 2.028494 2 3.375990 7 1.3474951 7650154 3.4
29 CHN China 82974 78238 4633 4736 2.960411 3 3.317009 7 0.3565979 1392730000 3.3
41 DJI Djibouti 1268 900 3 368 1.587078 1 2.694359 6 1.1072812 958920 3.1
72 JAM Jamaica 509 113 9 396 1.801956 1 2.853692 6 1.0517353 2934855 3.1
124 SLV El Salvador 1037 374 20 663 1.901719 2 2.878916 6 0.9771966 6420744 3.1
68 IRQ Iraq 3032 1966 115 1066 1.429757 1 2.596686 5 1.1669283 38433600 3.0
136 TCD Chad 372 78 42 294 2.158802 2 2.705569 6 0.5467671 15477751 2.7
14 BFA Burkina Faso 773 592 51 181 2.059039 2 2.682737 6 0.6236974 19751535 2.6
24 BRN Brunei 141 134 1 7 2.306871 2 3.196781 7 0.8899106 428962 2.3
90 MLI Mali 758 412 44 346 2.212828 2 2.893555 6 0.6807272 19077690 2.3
99 NER Niger 860 658 49 202 2.001481 2 2.638677 6 0.6371957 22442948 2.2
120 SDN Sudan 1818 198 90 1620 1.372199 1 2.772478 6 1.4002786 41801533 2.2
33 COG Congo (Brazzaville) 333 53 11 280 1.744398 1 2.605516 5 0.8611171 5244363 2.1
65 IND India 78055 26400 2551 51655 2.031679 2 2.937126 6 0.9054468 1352617328 1.9
77 KGZ Kyrgyzstan 1044 726 12 318 2.588559 3 3.145173 7 0.5566138 6315800 1.9
75 KAZ Kazakhstan 5417 2408 32 3009 3.102725 4 3.443588 7 0.3408627 18276499 1.8
133 SWZ Eswatini 187 48 2 139 1.844161 1 2.811018 6 0.9668574 1136191 1.8
15 BGD Bangladesh 17822 3361 269 14461 1.464541 1 2.821813 6 1.3572726 161356039 1.7
59 GTM Guatemala 1342 121 29 1221 1.372199 1 2.759347 6 1.3871474 17247807 1.7
129 SUR Suriname 10 9 1 1 2.001481 2 3.036616 6 1.0351348 575991 1.7
36 CRI Costa Rica 815 527 8 288 1.644636 1 3.184911 7 1.5402753 4999441 1.6
113 PRY Paraguay 740 182 11 558 1.529520 1 2.859919 6 1.3303989 6956071 1.6
121 SEN Senegal 2105 782 21 1323 1.456770 1 2.698837 6 1.2420670 15854360 1.3
101 NIC Nicaragua 25 7 8 18 1.372199 1 2.583300 5 1.2111011 6465513 1.2
30 CIV Cote d’Ivoire 1912 902 24 1010 1.744398 1 2.826156 6 1.0817574 25069229 1.0
73 JOR Jordan 582 392 9 190 1.644636 1 2.829120 6 1.1844845 9956011 0.9
55 GHA Ghana 5408 514 24 4894 1.671649 1 2.778949 6 1.1073003 29767108 0.8
76 KEN Kenya 737 281 40 456 1.541341 1 2.843747 6 1.3024054 51393010 0.8
100 NGA Nigeria 4971 1070 164 3901 1.940034 2 2.643588 6 0.7035542 195874740 0.8
137 THA Thailand 3017 2844 56 173 2.086053 2 3.095848 6 1.0097956 69428524 0.8
32 COD Congo (Kinshasa) 1169 148 50 1021 1.871174 1 2.851881 6 0.9807074 84068091 0.6
94 MRT Mauritania 15 6 2 9 1.429757 1 3.892820 8 2.4630626 4403319 0.5
25 BWA Botswana 24 17 1 7 1.587078 1 2.745534 6 1.1584557 2254126 0.4
56 GMB Gambia 23 10 1 13 1.529520 1 2.570194 5 1.0406746 2280102 0.4
83 LBY Libya 64 28 3 36 2.373681 2 3.155326 7 0.7816454 6678567 0.4
84 LKA Sri Lanka 915 382 9 533 2.001481 2 3.048268 6 1.0467865 21670000 0.4
114 PSE West Bank and Gaza 375 310 2 65 1.314641 1 2.566045 5 1.2514036 4569087 0.4
141 TZA Tanzania 509 183 21 326 1.556533 1 2.574917 5 1.0183843 56318348 0.4
149 YEM Yemen 70 1 12 69 1.556533 1 2.959244 6 1.4027115 28498687 0.4
151 ZMB Zambia 446 124 7 322 1.901719 2 2.641078 6 0.7393593 17351822 0.4
146 UZB Uzbekistan 2612 2076 11 536 2.872655 3 3.509301 7 0.6366458 32955400 0.3
147 VEN Venezuela 423 220 10 203 1.744398 1 2.927415 6 1.1830165 28870195 0.3
152 ZWE Zimbabwe 37 12 4 25 1.801956 1 2.658108 6 0.8561513 14439018 0.3
13 BEN Benin 327 83 2 244 1.686840 1 2.517893 5 0.8310523 11485048 0.2
96 MWI Malawi 63 24 3 39 1.744398 1 2.668853 6 0.9244542 18143315 0.2
135 SYR Syria 48 29 3 19 1.686840 1 2.663486 6 0.9766461 16906283 0.2
3 AGO Angola 45 14 2 31 1.740866 1 2.656268 6 0.9154018 30809762 0.1
11 BDI Burundi 15 7 1 8 1.771411 1 2.612776 5 0.8413650 11175378 0.1
91 MMR Burma 181 79 6 102 1.713853 1 2.708785 6 0.9949321 53708395 0.1
42 DMA Dominica 16 15 0 1 2.531001 3 3.277611 7 0.7466099 71625 0.0
50 ETH Ethiopia 263 108 5 155 1.399212 1 2.579463 5 1.1802511 109224559 0.0
58 GRL Greenland 11 11 0 0 3.928383 5 4.149890 9 0.2215078 56025 0.0
80 LAO Laos 19 14 0 5 1.429757 1 2.640228 6 1.2104707 7061507 0.0
88 MDG Madagascar 212 107 0 105 1.686840 1 2.708446 6 1.0216054 26262368 0.0
92 MNG Mongolia 42 15 0 27 2.531001 3 3.208902 7 0.6779009 3170208 0.0
93 MOZ Mozambique 104 34 0 70 1.786603 1 2.563266 5 0.7766632 29495962 0.0
98 NAM Namibia 16 11 0 5 1.959277 2 2.713307 6 0.7540300 2448255 0.0
110 PNG Papua New Guinea 8 8 0 0 2.216360 2 2.683891 6 0.4675313 8606316 0.0
118 RWA Rwanda 287 164 0 123 1.456770 1 2.599422 5 1.1426521 12301939 0.0
128 SSD South Sudan 203 2 0 201 2.158802 2 2.701773 6 0.5429707 10975920 0.0
134 SYC Seychelles 11 10 0 1 2.001481 2 2.995922 6 0.9944403 96762 0.0
142 UGA Uganda 139 55 0 84 1.556533 1 2.579931 5 1.0233979 42723139 0.0
148 VNM Vietnam 288 252 0 36 1.798425 1 2.894156 6 1.0957320 95540395 0.0