Ending day prediction of nCoV2019
19/08/2020
source('./predict.R')
## [1] "2020-08-18"
## country cum_confirm cum_heal cum_dead inc_r d_rate d_pm
## 1 United States 5613357 2974788 173778 0.79 3.10 525
## 2 Brazil 3363235 2478494 108654 0.69 3.23 511
## 3 Mexico 525733 359347 57023 0.68 10.85 442
## 4 India 2732218 2005215 52280 2.66 1.91 38
## 5 United Kingdom 319197 1918 41369 0.22 12.96 609
## 6 Italy 254235 203968 35400 0.13 13.92 585
## 7 France 219029 84065 30429 0.23 13.89 466
## 8 Spain 382142 196958 28646 6.49 7.50 613
## 9 Peru 541493 370717 26481 1.03 4.89 803
## 10 Iran 347835 300881 19972 0.69 5.74 238
## 11 Russia 932493 742628 15872 0.51 1.70 109
## 12 Colombia 476660 301525 15372 1.78 3.22 302
## 13 South Africa 589886 477671 11982 0.43 2.03 202
## 14 Chile 387502 360385 10513 0.40 2.71 550
## 15 Belgium 78534 18003 9944 0.27 12.66 858
## 16 Germany 227003 206242 9394 0.71 4.14 112
## 17 Canada 122872 109059 9032 0.64 7.35 239
## 18 Indonesia 143043 96306 6277 1.18 4.39 23
## 19 Pakistan 289832 270009 6190 0.21 2.14 28
## 20 Netherlands 63973 322 6175 0.77 9.65 360
## 21 Ecuador 101751 87120 6083 0.21 5.98 345
## 22 Turkey 250542 231971 5996 0.49 2.39 71
## 23 Iraq 180133 128945 5954 1.81 3.31 148
## 24 Argentina 299126 217850 5814 1.55 1.94 129
## 25 Sweden 85219 4971 5790 0.20 6.79 573
## 26 Egypt 96590 60651 5173 0.12 5.36 51
## 27 China 89963 83841 4711 0.06 5.24 3
## 28 Bolivia 101223 37471 4123 0.88 4.07 353
## 29 Bangladesh 282344 162825 3740 1.15 1.32 23
## 30 Saudi Arabia 301323 272911 3470 0.47 1.15 100
## 31 Romania 72208 33135 3074 1.42 4.26 160
## 32 Philippines 169213 112861 2687 2.88 1.59 25
## 33 Guatemala 62944 51530 2389 0.61 3.80 133
## 34 Ukraine 94436 48925 2116 1.74 2.24 48
## 35 Switzerland 38449 33500 1992 0.52 5.18 230
## 36 Poland 57876 39643 1896 1.04 3.28 50
## 37 Panama 82543 55845 1788 0.74 2.17 414
## 38 Portugal 54234 39800 1779 0.00 3.28 174
## 39 Ireland 27313 23364 1774 0.21 6.50 359
## 40 Honduras 50995 7450 1583 0.98 3.10 160
## 41 Kyrgyzstan 42146 34855 1498 0.37 3.55 230
## 42 Dominican Republic 86737 54108 1481 0.50 1.71 137
## 43 Algeria 39025 27347 1379 1.15 3.53 31
## 44 Afghanistan 37599 27166 1375 0.00 3.66 35
## 45 Kazakhstan 103300 84445 1269 0.26 1.23 68
## 46 Japan 57761 42284 1135 2.75 1.96 9
## 47 Nigeria 49485 36834 977 0.85 1.97 5
## 48 Moldova 30377 21220 908 0.64 2.99 225
## 49 Armenia 41846 34982 832 0.35 1.99 281
## 50 Sudan 12485 6398 805 0.60 6.45 18
## 51 Austria 23829 20870 729 1.25 3.06 81
## 52 Israel 95264 71167 698 1.68 0.73 81
## 53 Morocco 43558 29941 681 2.52 1.56 18
## 54 Serbia 29890 27597 681 0.70 2.28 78
## 55 El Salvador 23462 11179 625 2.40 2.66 96
## 56 Denmark 16127 13660 621 0.77 3.85 107
## 57 Belarus 69673 67339 617 0.23 0.89 65
## 58 Oman 83418 77977 597 0.23 0.72 117
## 59 Republika Severna Makedonija 12970 9513 549 1.81 4.23 NA
## 60 Ethiopia 31336 12524 544 4.89 1.74 5
## 61 Bulgaria 14500 9442 512 0.94 3.53 74
## 62 Azerbaijan 34343 32042 508 0.36 1.48 50
## 63 Kuwait 77470 69243 505 0.84 0.65 118
## 64 Bosnia and Herzegovina 16351 10279 495 1.49 3.03 151
## 65 Kenya 30636 17368 487 0.89 1.59 9
## 66 Australia 23773 14929 438 0.91 1.84 17
## 67 Cameroon 18582 16540 403 0.61 2.17 15
## 68 Czech Republic 20202 14622 399 0.95 1.98 37
## 69 United Arab Emirates 64541 57794 364 0.00 0.56 37
## 70 Puerto Rico 26760 2267 335 2.90 1.25 117
## 71 South Korea 15761 13934 306 1.59 1.94 6
## 72 Costa Rica 29084 9233 304 2.17 1.05 60
## 73 Venezuela 34802 23575 288 3.10 0.83 10
## 74 Norway 10089 8857 262 0.84 2.60 48
## 75 Senegal 12305 7767 256 0.56 2.08 15
## 76 Uzbekistan 36352 32062 242 2.36 0.67 7
## 77 Ghana 42653 40567 239 0.00 0.56 8
## 78 Qatar 115368 112088 193 0.25 0.17 67
## 79 Bahrain 47185 43529 174 0.75 0.37 102
## 80 Madagascar 14009 12767 173 0.89 1.23 6
## 81 Paraguay 10135 6210 145 3.51 1.43 20
## 82 Nepal 28257 17580 114 3.73 0.40 4
## 83 Palestinian Territory 17306 9939 113 4.67 0.65 NA
## 84 Côte d'Ivoire 17107 13990 110 0.48 0.64 4
## 85 Singapore 55938 52350 27 0.18 0.05 5
## [1] "Pakistan leading 1 cum_confirm == 0 trimed"
## [1] "Provinces added more than 0 cases in last 1-day:"
## time country province cum_confirm cum_heal cum_dead added
## 1 2020-08-18 China Hong Kong 4560 3677 70 36
## 2 2020-08-18 China Shanghai 842 754 7 14
## 3 2020-08-18 China Jiangsu 664 655 0 3
## 4 2020-08-18 China Shaanxi 356 324 3 2
## 5 2020-08-18 China Guangdong 1721 1687 8 1
## 6 2020-08-18 China Taiwan 486 450 7 1