COVID-19, Relative by Population

The following tables show the spread of COVID-19 for a percentage of the population.
The New Cases percentage of "Last 120 days" means that the percentage of people in the skin has become infected. The percentage for the "Last 30 to 7 days" shows the percentage of the population that would still become infected in 120 days according to the growth rate. The Relative Mortality rates from last positive cases are also in percentages (with 7-day shift). This means relative percentage of patients (with 7-day shift) die from COVID-19 infection. [1] The Total Mortality is the ratio of total deaths in COVID-19 to population. An exact description of the calculations can be found at the bottom of this page.
COVID-19 World map by Johns Hopkins University.
Last actualisation from "WHO" and "WorldoMeter": 2021-04-16 11:20
(For some countries, the data from the WHO and from "Our World in Data by Johns Hopkins University" and from "WorldoMeter" are completely different, such as: Israel.)
What can help, is at the bottom of this page. I recommend searching here "Global literature on coronavirus disease" or here "Google Scholar".

COVID-19, Selected Countries by WorldoMeter

Our World in Data, (2 days late data visualization) [CASES][DEATHS], [VACCINATION]
CDCountryNew CasesNew Deaths
AT Austria +2 416 (+2 678) +30 (+34)
CZ Czechia +3 238 (+3 710) +38 (+80)
DE Germany +0 (+30 634) +0 (+328)
HU Hungary +5 216 (+5 307) +241 (+256)
PL Poland +17 847 (+21 129) +595 (+683)
SK Slovakia, [gov], [okr]+636 (+843) +93 (+79)

COVID-19, New Cases in Regions (incidence rate)

N.RegionPopulationLast 120 daysLast 30 daysLast 7 days
1 South America 436 805 957 2.49% 3.27% 3.70%
2 Europe 755 994 592 2.89% 3.06% 2.80%
3 North America 592 314 330 2.86% 1.58% 1.78%
4 Asia 4 661 134 894 0.28% 0.51% 0.75%
5 Africa 1 365 078 505 0.15% 0.10% 0.10%
6 Australia/Oceania 43 192 513 0.03% 0.07% 0.06%

COVID-19, Relative Mortality rate from positive cases in Regions

N.RegionPopulationMortality last
120 days
Mortality last
30 days
New positive cases
last 30 days
1 South America 436 805 957 2.56% 4.11% 3.27%
2 Africa 1 365 078 505 2.95% 3.28% 0.10%
3 North America 592 314 330 2.08% 2.74% 1.58%
4 Europe 755 994 592 2.21% 2.22% 3.06%
5 Asia 4 661 134 894 1.20% 1.14% 0.51%
6 Australia/Oceania 43 192 513 1.05% (1.02)% (0.07)%

COVID-19, Total Mortality from population of Regions

N.RegionPopulationDeaths1000*Deaths/Pop.
1 South America 436 805 957 609 552 1.395
2 North America 592 314 330 822 699 1.389
3 Europe 755 994 592 968 673 1.281
4 Asia 4 661 134 894 450 243 0.097
5 Africa 1 365 078 505 117 345 0.086
6 Australia/Oceania 43 192 513 1 302 0.030

COVID-19, New Cases in Countries

Filtered where Population more then 2 million, [All Countries] | Compare with [CASES]
N.RegionCDCountryPopulationLast 120 daysLast 30 daysLast 7 days
1 South America UY Uruguay 3 483 224 4.12% 9.30% 13.22%
2 Asia TR Turkey 85 052 838 2.54% 5.53% 8.00%
3 Europe HU Hungary 9 640 967 4.67% 8.70% 6.47%
4 Europe FR France 65 387 466 4.19% 6.55% 6.45%
5 Europe HR Croatia 4 085 238 2.97% 4.80% 6.35%
6 Europe SE Sweden 10 148 878 5.29% 6.31% 6.04%
7 Europe PL Poland 37 813 871 3.94% 7.44% 5.99%
8 South America AR Argentina 45 522 155 2.40% 3.53% 5.80%
9 Europe SI Slovenia 2 079 164 6.23% 5.48% 5.37%
10 Europe NL Netherlands 17 164 784 4.31% 4.92% 5.11%
11 Europe MK North Macedonia 2 083 306 3.33% 5.97% 5.03%
12 Europe LT Lithuania 2 692 167 4.67% 3.66% 4.61%
13 South America CL Chile 19 245 367 2.73% 4.18% 4.49%
14 Europe RS Serbia 8 709 547 4.27% 5.91% 4.30%
15 Asia QA Qatar 2 807 805 1.87% 3.24% 4.15%
16 Asia KW Kuwait 4 320 165 2.46% 3.79% 4.05%
17 Asia JO Jordan 10 282 825 4.00% 7.15% 3.95%
18 South America BR Brazil 213 749 436 3.14% 4.04% 3.91%
19 Asia LB Lebanon 6 801 277 5.20% 4.79% 3.87%
20 Europe UA Ukraine 43 525 906 2.27% 3.97% 3.84%
21 South America PR Puerto Rico 3 193 694 1.69% 1.80% 3.83%
22 Europe CZ Czechia 10 724 658 9.27% 6.34% 3.82%
23 Europe BG Bulgaria 6 907 046 2.85% 5.51% 3.82%
24 South America PY Paraguay 7 201 405 2.04% 3.39% 3.66%
25 Europe BA Bosnia and Herzegovina 3 264 674 2.59% 5.06% 3.62%
26 South America CO Colombia 51 309 459 2.22% 2.19% 3.56%
27 Asia AM Armenia 2 967 613 1.90% 3.63% 3.43%
28 South America PE Peru 33 332 642 2.06% 3.05% 3.35%
29 Asia IR Iran 84 837 813 1.22% 1.91% 3.27%
30 Europe GR Greece 10 382 792 1.74% 3.24% 3.27%
31 Europe AT Austria 9 046 892 2.82% 3.89% 3.26%
32 Asia MN Mongolia 3 319 723 0.54% 1.77% 3.18%
33 Europe IT Italy 60 391 595 3.21% 3.76% 3.08%
34 Europe DE Germany 83 995 236 2.02% 2.43% 3.02%
35 Asia OM Oman 5 208 154 0.96% 2.20% 2.85%
36 Europe BE Belgium 11 629 388 2.75% 4.07% 2.83%
37 North America CA Canada 38 002 618 1.61% 1.84% 2.71%
38 Asia GE Georgia 3 983 117 2.31% 1.73% 2.64%
39 Asia AZ Azerbaijan 10 211 103 1.09% 2.06% 2.52%
40 North America US USA 332 532 050 4.41% 2.28% 2.52%
41 Europe RO Romania 19 136 146 2.34% 3.17% 2.39%
42 Europe CH Switzerland 8 704 466 2.61% 2.41% 2.33%
43 Asia AE Arab Emirates 9 984 338 3.03% 2.45% 2.28%
44 Asia IQ Iraq 40 928 986 0.92% 1.84% 2.24%
45 North America CR Costa Rica 5 130 727 1.41% 1.28% 2.21%
46 Europe MD Moldova 4 026 555 2.83% 3.69% 2.14%
47 Europe ES Spain 46 769 075 3.40% 1.61% 1.87%
48 Africa TN Tunisia 11 915 894 1.38% 1.23% 1.81%
49 Africa BW Botswana 2 388 828 1.31% 1.67% 1.70%
50 Europe SK Slovakia 5 461 730 4.31% 2.36% 1.63%
51 Europe NO Norway 5 454 619 1.18% 1.81% 1.53%
52 Europe BY Belarus 9 446 869 1.84% 1.52% 1.49%
53 Europe DK Denmark 5 808 288 1.94% 1.43% 1.38%
54 Asia KZ Kazakhstan 18 953 268 0.75% 1.13% 1.38%
55 South America EC Ecuador 17 853 392 0.85% 1.18% 1.33%
56 Asia IN India 1 390 679 153 0.30% 0.76% 1.25%
57 Asia PH Philippines 110 727 033 0.41% 0.98% 1.17%
58 Africa LY Libya 6 944 698 1.12% 1.35% 1.09%
59 North America CU Cuba 11 321 156 0.71% 0.97% 1.04%
60 South America BO Bolivia 11 798 112 1.16% 0.85% 1.02%
61 Europe AL Albania 2 875 315 2.73% 1.48% 0.97%
62 North America JM Jamaica 2 971 357 1.05% 1.53% 0.94%
63 Europe IE Ireland 4 981 160 3.32% 1.18% 0.92%
64 North America PA Panama 4 368 082 3.72% 1.03% 0.85%
65 Europe FI Finland 5 547 512 0.93% 1.12% 0.77%
66 North America HN Honduras 10 028 249 0.82% 0.74% 0.76%
67 Africa NA Namibia 2 576 989 1.12% 0.73% 0.75%
68 Europe RU Russia 145 983 862 1.32% 0.73% 0.71%
69 Asia MY Malaysia 32 691 886 0.86% 0.52% 0.66%
70 North America GT Guatemala 18 175 404 0.42% 0.50% 0.65%
71 Europe PT Portugal 10 173 212 4.63% 0.57% 0.63%
72 Asia PS Palestine 5 194 206 0.03% 0.13% 0.57%
73 North America DO Dominican R. 10 933 492 0.94% 0.47% 0.57%
74 Africa GA Gabon 2 266 822 0.51% 0.71% 0.53%
75 South America VE Venezuela 28 372 266 0.25% 0.45% 0.49%
76 Asia BD Bangladesh 165 980 237 0.13% 0.35% 0.43%
77 Europe GB United Kingdom 68 167 060 3.53% 0.66% 0.41%
78 Asia KG Kyrgyzstan 6 608 658 0.20% 0.26% 0.41%
79 North America MX Mexico 129 998 266 0.79% 0.38% 0.38%
80 North America SV El Salvador 6 512 000 0.38% 0.26% 0.35%
81 Asia JP Japan 126 170 451 0.26% 0.21% 0.32%
82 Asia SA Saudi Arabia 35 239 473 0.12% 0.22% 0.31%
83 Africa CM Cameroon 27 062 544 0.13% 0.34% 0.28%
84 Asia IL Israel 9 197 590 5.13% 0.62% 0.26%
85 Asia PK Pakistan 224 253 902 0.13% 0.23% 0.26%
86 Australia/Oceania PG Papua New Guinea 9 079 879 0.09% 0.30% 0.26%
87 Africa ZA South Africa 59 896 382 1.13% 0.21% 0.23%
88 Asia ID Indonesia 275 798 600 0.35% 0.23% 0.23%
89 Africa MG Madagascar 28 247 452 0.04% 0.13% 0.23%
90 Asia TH Thailand 69 938 149 0.05% 0.07% 0.20%
91 Africa KE Kenya 54 698 558 0.10% 0.25% 0.19%
92 Africa ET Ethiopia 117 190 341 0.10% 0.20% 0.19%
93 Africa MA Morocco 37 254 502 0.26% 0.16% 0.18%
94 Asia NP Nepal 29 547 776 0.11% 0.09% 0.17%
95 Asia KH Cambodia 16 900 725 0.03% 0.09% 0.16%
96 Asia KR South Korea 51 304 013 0.13% 0.12% 0.15%
97 Africa TG Togo 8 430 534 0.11% 0.20% 0.15%
98 Asia LK Sri Lanka 21 484 036 0.29% 0.14% 0.13%
99 Africa ML Mali 20 706 145 0.03% 0.07% 0.12%
100 Africa EG Egypt 103 847 468 0.09% 0.08% 0.09%
101 Africa SO Somalia 16 239 481 0.05% 0.09% 0.09%
102 Africa GM Gambia 2 469 682 0.08% 0.11% 0.09%
103 Africa ZM Zambia 18 785 992 0.38% 0.11% 0.09%
104 Africa GN Guinea 13 411 064 0.06% 0.10% 0.08%
105 Asia SY Syria 17 833 162 0.06% 0.09% 0.08%
106 Africa RW Rwanda 13 203 279 0.13% 0.11% 0.08%
107 Asia UZ Uzbekistan 33 850 137 0.03% 0.06% 0.08%
108 Africa BI Burundi 12 167 109 0.02% 0.03% 0.06%
109 Asia SG Singapore 5 886 569 0.04% 0.04% 0.05%
110 Africa ER Eritrea 3 584 978 0.08% 0.05% 0.04%
111 Africa AO Angola 33 663 530 0.02% 0.03% 0.04%
112 Africa ZW Zimbabwe 15 032 836 0.17% 0.02% 0.04%
113 Africa DZ Algeria 44 471 990 0.06% 0.03% 0.04%
114 Africa SN Senegal 17 088 723 0.13% 0.06% 0.04%
115 Africa GW Guinea-Bissau 2 004 390 0.06% 0.05% 0.04%
116 Asia YE Yemen 30 340 231 0.01% 0.04% 0.03%
117 Africa MZ Mozambique 31 939 216 0.16% 0.05% 0.03%
118 Asia AF Afghanistan 39 614 554 0.02% 0.02% 0.03%
119 Africa MR Mauritania 4 745 164 0.15% 0.05% 0.03%
120 Africa GH Ghana 31 580 972 0.12% 0.04% 0.02%
121 Africa CF Central African R. 4 895 636 0.01% 0.04% 0.02%
122 Africa CI Ivory Coast 26 889 052 0.09% 0.11% 0.02%
123 Africa SS South Sudan 11 296 903 0.06% 0.03% 0.01%
124 Africa MW Malawi 19 516 315 0.14% 0.02% 0.01%
125 Africa BJ Benin 12 371 351 0.04% 0.04% 0.01%
126 Africa BF Burkina Faso 21 350 969 0.04% 0.01% 0.01%
127 Australia/Oceania NZ New Zealand 5 002 100 0.01% 0.01% 0.01%
128 North America NI Nicaragua 6 686 515 0.01% 0.01% 0.01%
129 Africa SD Sudan 44 650 323 0.02% 0.02% 0.01%
130 North America HT Haiti 11 511 792 0.03% 0.01% 0.01%
131 Africa UG Uganda 46 867 258 0.03% 0.01% 0.01%
132 Australia/Oceania AU Australia 25 732 506 0.01% 0.01% 0.01%
133 Africa TD Chad 16 793 808 0.02% 0.01% 0.01%
134 Africa SL Sierra Leone 8 104 071 0.02% 0.00% 0.01%
135 Africa CD DR Congo 91 685 378 0.02% 0.01% 0.01%
136 Africa NG Nigeria 210 134 738 0.04% 0.01% 0.00%
137 Africa NE Niger 24 888 432 0.01% 0.00% 0.00%
138 Africa LS Lesotho 2 155 620 0.39% 0.03% 0.00%
139 Asia VN Vietnam 98 027 872 0.00% 0.00% 0.00%
140 Africa LR Liberia 5 150 686 0.01% 0.00% 0.00%
141 Asia HK Hong Kong 7 544 866 0.00% 0.00% 0.00%
142 Asia TW Taiwan 23 850 790 0.00% 0.00% 0.00%
143 Asia MM Myanmar 54 697 119 0.06% 0.00% 0.00%
144 Asia LA Laos 7 358 471 0.00% 0.00% 0.00%
145 Africa TZ Tanzania 61 062 542 0.00% 0.00% 0.00%
146 Asia KP North Korea 25 660 000 0.00% 0.00% 0.00%
147 Europe TM Turkmenistan 6 118 000 0.00% 0.00% 0.00%
148 Asia TJ Tajikistan 9 705 323 0.00% 0.00% 0.00%
149 Asia CN China 1 439 323 776 0.00% 0.00% 0.00%
150 Africa CG Congo 5 624 301 0.07% 0.05% 0.00%

COVID-19, Relative Mortality rate from positive cases in Countries

Filtered where Population more then 2 million, [All Countries] | Compare with [DEATHS]! and with [FATALITY RATE]
N.RegionCDCountryPopulationMortality last
120 days
Mortality last
30 days
New positive cases
last 30 days
1 Asia YE Yemen 30 340 231 16.08% (15.31)% (0.04)%
2 Africa SD Sudan 44 650 323 6.46% (12.00)% (0.02)%
3 North America MX Mexico 129 998 266 8.95% 11.99% 0.38%
4 Asia SY Syria 17 833 162 7.67% (8.07)% (0.09)%
5 Africa SO Somalia 16 239 481 7.23% (7.82)% (0.09)%
6 Asia AF Afghanistan 39 614 554 6.13% (6.84)% (0.02)%
7 Africa ZA South Africa 59 896 382 4.09% 6.23% 0.21%
8 Africa EG Egypt 103 847 468 6.36% (6.02)% (0.08)%
9 Africa ZW Zimbabwe 15 032 836 4.76% (5.88)% (0.02)%
10 Europe SK Slovakia 5 461 730 3.90% 5.61% 2.36%
11 Europe BA Bosnia and Herzegovina 3 264 674 5.02% 4.88% 5.06%
12 Europe RU Russia 145 983 862 2.71% 4.20% 0.73%
13 Africa GW Guinea-Bissau 2 004 390 1.80% (4.06)% (0.05)%
14 Africa TN Tunisia 11 915 894 3.47% 3.98% 1.23%
15 South America BR Brazil 213 749 436 2.75% 3.87% 4.04%
16 Africa MW Malawi 19 516 315 3.42% (3.80)% (0.02)%
17 Africa NE Niger 24 888 432 3.54% (3.59)% (0.00)%
18 Europe BG Bulgaria 6 907 046 4.48% 3.38% 5.51%
19 Europe HU Hungary 9 640 967 3.91% 3.26% 8.70%
20 Africa LS Lesotho 2 155 620 3.17% (3.26)% (0.03)%
21 Europe MK North Macedonia 2 083 306 3.14% 3.25% 5.97%
22 Africa TD Chad 16 793 808 2.29% (3.23)% (0.01)%
23 Africa MG Madagascar 28 247 452 2.98% 3.18% 0.13%
24 Africa SN Senegal 17 088 723 3.25% (3.17)% (0.06)%
25 Africa CF Central African R. 4 895 636 2.32% (2.89)% (0.04)%
26 North America HN Honduras 10 028 249 2.35% 2.86% 0.74%
27 South America PE Peru 33 332 642 3.01% 2.85% 3.05%
28 Africa BW Botswana 2 388 828 2.13% 2.81% 1.67%
29 South America CO Colombia 51 309 459 2.51% 2.80% 2.19%
30 North America SV El Salvador 6 512 000 3.43% 2.78% 0.26%
31 Africa DZ Algeria 44 471 990 1.83% (2.77)% (0.03)%
32 Asia ID Indonesia 275 798 600 2.48% 2.70% 0.23%
33 South America PY Paraguay 7 201 405 2.22% 2.67% 3.39%
34 South America EC Ecuador 17 853 392 2.51% 2.64% 1.18%
35 North America NI Nicaragua 6 686 515 2.59% (2.63)% (0.01)%
36 South America BO Bolivia 11 798 112 2.69% 2.61% 0.85%
37 Europe RO Romania 19 136 146 2.59% 2.60% 3.17%
38 Europe GR Greece 10 382 792 3.19% 2.59% 3.24%
39 Africa NA Namibia 2 576 989 1.46% 2.59% 0.73%
40 Europe MD Moldova 4 026 555 2.41% 2.56% 3.69%
41 Europe UA Ukraine 43 525 906 2.39% 2.48% 3.97%
42 Africa ML Mali 20 706 145 3.65% (2.40)% (0.07)%
43 North America GT Guatemala 18 175 404 3.54% 2.40% 0.50%
44 Asia AM Armenia 2 967 613 2.22% 2.16% 3.63%
45 Africa GM Gambia 2 469 682 2.63% 2.11% 0.11%
46 Europe IT Italy 60 391 595 2.54% 2.10% 3.76%
47 Asia PK Pakistan 224 253 902 2.45% 2.02% 0.23%
48 Europe CZ Czechia 10 724 658 1.81% 1.97% 6.34%
49 Europe AL Albania 2 875 315 1.57% 1.94% 1.48%
50 Europe HR Croatia 4 085 238 2.80% 1.93% 4.80%
51 Europe PL Poland 37 813 871 2.56% 1.88% 7.44%
52 Asia KG Kyrgyzstan 6 608 658 1.66% 1.86% 0.26%
53 Asia GE Georgia 3 983 117 1.81% 1.85% 1.73%
54 Africa LY Libya 6 944 698 2.00% 1.83% 1.35%
55 Africa CM Cameroon 27 062 544 1.46% 1.80% 0.34%
56 Asia AZ Azerbaijan 10 211 103 1.63% 1.78% 2.06%
57 Africa AO Angola 33 663 530 2.44% 1.76% 0.03%
58 Europe IE Ireland 4 981 160 1.63% 1.70% 1.18%
59 Asia JP Japan 126 170 451 2.08% 1.68% 0.21%
60 South America AR Argentina 45 522 155 1.77% 1.64% 3.53%
61 South America CL Chile 19 245 367 1.81% 1.59% 4.18%
62 North America US USA 332 532 050 1.65% 1.58% 2.28%
63 Europe PT Portugal 10 173 212 2.25% 1.58% 0.57%
64 South America UY Uruguay 3 483 224 1.32% 1.54% 9.30%
65 Asia LB Lebanon 6 801 277 1.60% 1.52% 4.79%
66 Africa KE Kenya 54 698 558 1.54% 1.49% 0.25%
67 Europe LT Lithuania 2 692 167 1.84% 1.48% 3.66%
68 Africa GH Ghana 31 580 972 1.14% 1.46% 0.04%
69 Asia KH Cambodia 16 900 725 1.14% 1.45% 0.09%
70 North America PA Panama 4 368 082 1.57% 1.45% 1.03%
71 North America JM Jamaica 2 971 357 1.39% 1.44% 1.53%
72 South America VE Venezuela 28 372 266 1.34% 1.42% 0.45%
73 Asia IR Iran 84 837 813 1.37% 1.42% 1.91%
74 Africa MA Morocco 37 254 502 1.94% 1.41% 0.16%
75 North America CR Costa Rica 5 130 727 1.52% 1.40% 1.28%
76 Africa BF Burkina Faso 21 350 969 0.86% 1.39% 0.01%
77 Asia SA Saudi Arabia 35 239 473 1.94% 1.39% 0.22%
78 North America DO Dominican R. 10 933 492 0.98% 1.37% 0.47%
79 Africa RW Rwanda 13 203 279 1.56% 1.33% 0.11%
80 Europe DE Germany 83 995 236 3.24% 1.33% 2.43%
81 Asia TW Taiwan 23 850 790 1.21% 1.32% 0.00%
82 Asia BD Bangladesh 165 980 237 1.61% 1.30% 0.35%
83 Africa ET Ethiopia 117 190 341 1.35% 1.29% 0.20%
84 Africa BJ Benin 12 371 351 1.18% 1.27% 0.04%
85 Africa MR Mauritania 4 745 164 2.73% 1.27% 0.05%
86 Africa CD DR Congo 91 685 378 2.60% 1.24% 0.01%
87 Asia NP Nepal 29 547 776 3.82% 1.23% 0.09%
88 Asia PH Philippines 110 727 033 1.76% 1.20% 0.98%
89 Asia JO Jordan 10 282 825 1.13% 1.20% 7.15%
90 South America PR Puerto Rico 3 193 694 1.70% 1.12% 1.80%
91 Africa MZ Mozambique 31 939 216 1.25% 1.11% 0.05%
92 Europe GB United Kingdom 68 167 060 2.40% 1.11% 0.66%
93 Europe ES Spain 46 769 075 1.60% 1.03% 1.61%
94 Asia KZ Kazakhstan 18 953 268 1.02% 0.99% 1.13%
95 Africa NG Nigeria 210 134 738 0.93% 0.99% 0.01%
96 Asia IL Israel 9 197 590 0.68% 0.98% 0.62%
97 Europe AT Austria 9 046 892 1.86% 0.94% 3.89%
98 Africa ZM Zambia 18 785 992 1.21% 0.94% 0.11%
99 Africa GN Guinea 13 411 064 0.80% 0.93% 0.10%
100 Asia LK Sri Lanka 21 484 036 0.70% 0.93% 0.14%
101 Australia/Oceania PG Papua New Guinea 9 079 879 1.03% 0.91% 0.30%
102 North America HT Haiti 11 511 792 0.53% 0.90% 0.01%
103 Asia OM Oman 5 208 154 0.80% 0.89% 2.20%
104 Europe FR France 65 387 466 1.57% 0.88% 6.55%
105 Europe BY Belarus 9 446 869 0.61% 0.82% 1.52%
106 Europe BE Belgium 11 629 388 1.61% 0.80% 4.07%
107 Europe RS Serbia 8 709 547 0.87% 0.79% 5.91%
108 Asia IN India 1 390 679 153 0.87% 0.78% 0.76%
109 Asia KR South Korea 51 304 013 1.70% 0.72% 0.12%
110 Africa UG Uganda 46 867 258 0.71% 0.70% 0.01%
111 North America CA Canada 38 002 618 1.63% 0.69% 1.84%
112 Africa SS South Sudan 11 296 903 0.73% 0.69% 0.03%
113 Africa ER Eritrea 3 584 978 0.36% 0.67% 0.05%
114 Africa CG Congo 5 624 301 0.92% 0.66% 0.05%
115 Africa GA Gabon 2 266 822 0.60% 0.66% 0.71%
116 Europe SI Slovenia 2 079 164 1.49% 0.64% 5.48%
117 Asia IQ Iraq 40 928 986 0.67% 0.63% 1.84%
118 Africa TG Togo 8 430 534 0.61% 0.61% 0.20%
119 Asia TR Turkey 85 052 838 0.91% 0.61% 5.53%
120 Asia KW Kuwait 4 320 165 0.53% 0.61% 3.79%
121 Asia MM Myanmar 54 697 119 2.17% 0.60% 0.00%
122 Africa CI Ivory Coast 26 889 052 0.59% 0.59% 0.11%
123 Europe CH Switzerland 8 704 466 1.46% 0.47% 2.41%
124 Europe FI Finland 5 547 512 0.78% 0.47% 1.12%
125 Asia QA Qatar 2 807 805 0.25% 0.46% 3.24%
126 North America CU Cuba 11 321 156 0.48% 0.44% 0.97%
127 Africa BI Burundi 12 167 109 0.22% 0.44% 0.03%
128 Europe NL Netherlands 17 164 784 0.88% 0.38% 4.92%
129 Asia MY Malaysia 32 691 886 0.33% 0.35% 0.52%
130 Asia MN Mongolia 3 319 723 0.29% 0.33% 1.77%
131 Australia/Oceania AU Australia 25 732 506 0.14% 0.31% 0.01%
132 Asia UZ Uzbekistan 33 850 137 0.23% 0.31% 0.06%
133 Europe SE Sweden 10 148 878 0.99% 0.27% 6.31%
134 Europe DK Denmark 5 808 288 1.10% 0.26% 1.43%
135 Europe NO Norway 5 454 619 0.49% 0.26% 1.81%
136 Asia AE Arab Emirates 9 984 338 0.31% 0.22% 2.45%
137 Asia TH Thailand 69 938 149 0.14% 0.21% 0.07%
138 Asia SG Singapore 5 886 569 0.04% 0.00% 0.04%
139 Australia/Oceania NZ New Zealand 5 002 100 0.21% 0.00% 0.01%
140 Africa SL Sierra Leone 8 104 071 0.26% 0.00% 0.00%
141 Africa LR Liberia 5 150 686 0.51% 0.00% 0.00%
142 Asia VN Vietnam 98 027 872 0.00% 0.00% 0.00%
143 Asia LA Laos 7 358 471 0.00% 0.00% 0.00%
144 Asia PS Palestine 5 194 206 0.00% 0.00% 0.13%
145 Asia HK Hong Kong 7 544 866 0.00% 0.00% 0.00%
146 Asia KP North Korea 25 660 000 0.00% 0.00% 0.00%
147 Africa TZ Tanzania 61 062 542 0.00% 0.00% 0.00%
148 Europe TM Turkmenistan 6 118 000 0.00% 0.00% 0.00%
149 Asia TJ Tajikistan 9 705 323 0.00% 0.00% 0.00%
150 Asia CN China 1 439 323 776 0.00% 0.00% 0.00%

COVID-19, Total Mortality rate (from population) in Countries

Filtered where Population more then 2 million, [All Countries] | Compare with [DEATHS] ! and with [CUMULATIVE DEATHS]
N.RegionCDCountryPopulationDeaths 1000*Deaths/Pop.
1 Europe CZ Czechia 10 724 658 28 267 2.636
2 Europe HU Hungary 9 640 967 24 762 2.568
3 Europe BA Bosnia and Herzegovina 3 264 674 7 702 2.359
4 Europe BG Bulgaria 6 907 046 14 979 2.169
5 Europe SI Slovenia 2 079 164 4 421 2.126
6 Europe MK North Macedonia 2 083 306 4 357 2.091
7 Europe BE Belgium 11 629 388 23 636 2.032
8 Europe SK Slovakia 5 461 730 10 970 2.009
9 Europe IT Italy 60 391 595 115 937 1.920
10 Europe GB United Kingdom 68 167 060 127 191 1.866
11 South America BR Brazil 213 749 436 362 199 1.694
12 North America US USA 332 532 050 559 133 1.681
13 South America PE Peru 33 332 642 55 826 1.675
14 Europe PT Portugal 10 173 212 16 933 1.665
15 Europe ES Spain 46 769 075 76 882 1.644
16 North America MX Mexico 129 998 266 210 695 1.621
17 Europe PL Poland 37 813 871 61 207 1.619
18 Europe HR Croatia 4 085 238 6 442 1.577
19 Europe FR France 65 387 466 99 441 1.521
20 North America PA Panama 4 368 082 6 179 1.415
21 Europe LT Lithuania 2 692 167 3 737 1.388
22 Europe MD Moldova 4 026 555 5 493 1.364
23 Europe SE Sweden 10 148 878 13 756 1.355
24 Europe RO Romania 19 136 146 25 800 1.348
25 South America CO Colombia 51 309 459 66 862 1.303
26 Asia AM Armenia 2 967 613 3 835 1.292
27 South America CL Chile 19 245 367 24 766 1.287
28 South America AR Argentina 45 522 155 58 557 1.286
29 Europe CH Switzerland 8 704 466 9 800 1.126
30 South America BO Bolivia 11 798 112 12 580 1.066
31 Europe AT Austria 9 046 892 9 560 1.057
32 Asia LB Lebanon 6 801 277 6 809 1.001
33 Asia GE Georgia 3 983 117 3 916 0.983
34 Europe NL Netherlands 17 164 784 16 862 0.982
35 South America EC Ecuador 17 853 392 17 489 0.980
36 Europe IE Ireland 4 981 160 4 820 0.968
37 Europe DE Germany 83 995 236 79 709 0.949
38 Europe UA Ukraine 43 525 906 39 096 0.898
39 Africa ZA South Africa 59 896 382 53 571 0.894
40 Europe GR Greece 10 382 792 9 239 0.890
41 Europe AL Albania 2 875 315 2 335 0.812
42 Africa TN Tunisia 11 915 894 9 553 0.802
43 Asia JO Jordan 10 282 825 8 057 0.783
44 Asia IR Iran 84 837 813 65 680 0.774
45 Europe RU Russia 145 983 862 104 795 0.718
46 South America PY Paraguay 7 201 405 5 048 0.701
47 Asia IL Israel 9 197 590 6 315 0.687
48 South America PR Puerto Rico 3 193 694 2 174 0.681
49 Europe RS Serbia 8 709 547 5 881 0.675
50 North America CA Canada 38 002 618 23 447 0.617
51 North America CR Costa Rica 5 130 727 3 055 0.595
52 North America HN Honduras 10 028 249 4 897 0.488
53 South America UY Uruguay 3 483 224 1 674 0.481
54 Europe DK Denmark 5 808 288 2 449 0.422
55 Africa LY Libya 6 944 698 2 873 0.414
56 Asia TR Turkey 85 052 838 35 031 0.412
57 Asia AZ Azerbaijan 10 211 103 4 045 0.396
58 North America GT Guatemala 18 175 404 7 088 0.390
59 Asia IQ Iraq 40 928 986 14 885 0.364
60 Asia OM Oman 5 208 154 1 821 0.350
61 Asia KW Kuwait 4 320 165 1 428 0.331
62 North America SV El Salvador 6 512 000 2 068 0.318
63 North America DO Dominican R. 10 933 492 3 405 0.311
64 Africa BW Botswana 2 388 828 671 0.281
65 Europe BY Belarus 9 446 869 2 393 0.253
66 Africa MA Morocco 37 254 502 8 927 0.240
67 North America JM Jamaica 2 971 357 697 0.235
68 Asia KG Kyrgyzstan 6 608 658 1 544 0.234
69 Africa NA Namibia 2 576 989 596 0.231
70 Asia KZ Kazakhstan 18 953 268 3 963 0.209
71 Asia SA Saudi Arabia 35 239 473 6 791 0.193
72 Europe FI Finland 5 547 512 881 0.159
73 Asia ID Indonesia 275 798 600 43 073 0.156
74 Asia AE Arab Emirates 9 984 338 1 545 0.155
75 Africa LS Lesotho 2 155 620 315 0.146
76 Asia PH Philippines 110 727 033 15 592 0.141
77 Europe NO Norway 5 454 619 707 0.130
78 Asia QA Qatar 2 807 805 357 0.127
79 Asia IN India 1 390 679 153 173 123 0.125
80 Africa EG Egypt 103 847 468 12 611 0.121
81 Asia NP Nepal 29 547 776 3 066 0.104
82 Africa ZW Zimbabwe 15 032 836 1 550 0.103
83 Africa MR Mauritania 4 745 164 452 0.095
84 Asia SY Syria 17 833 162 1 414 0.079
85 Asia JP Japan 126 170 451 9 500 0.075
86 Africa DZ Algeria 44 471 990 3 144 0.071
87 Asia PK Pakistan 224 253 902 15 864 0.071
88 Africa GM Gambia 2 469 682 170 0.069
89 Africa ZM Zambia 18 785 992 1 230 0.066
90 South America VE Venezuela 28 372 266 1 834 0.065
91 Asia AF Afghanistan 39 614 554 2 537 0.064
92 Africa SN Senegal 17 088 723 1 085 0.064
93 Asia BD Bangladesh 165 980 237 10 081 0.061
94 Asia MM Myanmar 54 697 119 3 206 0.059
95 Africa MW Malawi 19 516 315 1 134 0.058
96 Africa GA Gabon 2 266 822 129 0.057
97 Africa SD Sudan 44 650 323 2 194 0.049
98 Africa KE Kenya 54 698 558 2 424 0.044
99 North America CU Cuba 11 321 156 491 0.043
100 Asia MY Malaysia 32 691 886 1 355 0.041
101 Africa SO Somalia 16 239 481 656 0.040
102 Asia YE Yemen 30 340 231 1 098 0.036
103 Australia/Oceania AU Australia 25 732 506 910 0.035
104 Asia KR South Korea 51 304 013 1 790 0.035
105 Africa CM Cameroon 27 062 544 919 0.034
106 Africa GW Guinea-Bissau 2 004 390 66 0.033
107 Asia LK Sri Lanka 21 484 036 608 0.028
108 Africa ET Ethiopia 117 190 341 3 287 0.028
109 North America NI Nicaragua 6 686 515 180 0.027
110 Africa MZ Mozambique 31 939 216 794 0.025
111 Africa CG Congo 5 624 301 137 0.024
112 Africa GH Ghana 31 580 972 766 0.024
113 Africa RW Rwanda 13 203 279 321 0.024
114 North America HT Haiti 11 511 792 252 0.022
115 Africa ML Mali 20 706 145 421 0.020
116 Asia UZ Uzbekistan 33 850 137 635 0.019
117 Africa MG Madagascar 28 247 452 527 0.019
118 Africa AO Angola 33 663 530 557 0.017
119 Africa LR Liberia 5 150 686 85 0.017
120 Africa CF Central African R. 4 895 636 74 0.015
121 Africa TG Togo 8 430 534 117 0.014
122 Asia MN Mongolia 3 319 723 35 0.011
123 Africa GN Guinea 13 411 064 138 0.010
124 Africa SS South Sudan 11 296 903 114 0.010
125 Africa CI Ivory Coast 26 889 052 271 0.010
126 Africa TD Chad 16 793 808 168 0.010
127 Africa NG Nigeria 210 134 738 2 061 0.010
128 Africa SL Sierra Leone 8 104 071 79 0.010
129 Asia TJ Tajikistan 9 705 323 90 0.009
130 Australia/Oceania PG Papua New Guinea 9 079 879 82 0.009
131 Africa CD DR Congo 91 685 378 745 0.008
132 Africa BJ Benin 12 371 351 96 0.008
133 Africa NE Niger 24 888 432 190 0.008
134 Africa BF Burkina Faso 21 350 969 154 0.007
135 Africa UG Uganda 46 867 258 338 0.007
136 Australia/Oceania NZ New Zealand 5 002 100 26 0.005
137 Asia SG Singapore 5 886 569 30 0.005
138 Africa ER Eritrea 3 584 978 10 0.003
139 Asia PS Palestine 5 194 206 14 0.003
140 Asia KH Cambodia 16 900 725 37 0.002
141 Asia TH Thailand 69 938 149 97 0.001
142 Africa BI Burundi 12 167 109 6 0.001
143 Asia TW Taiwan 23 850 790 11 0.001
144 Asia VN Vietnam 98 027 872 35 0.000
145 Africa TZ Tanzania 61 062 542 21 0.000
146 Asia CN China 1 439 323 776 0 0.000
147 Asia KP North Korea 25 660 000 0 0.000
148 Europe TM Turkmenistan 6 118 000 0 0.000
149 Asia LA Laos 7 358 471 0 0.000
150 Asia HK Hong Kong 7 544 866 0 0.000

Elhalálozási adatok hozzávetőleges értékei 2018: Abortusz: 56 millió, Rákbetegség: 9,6 millió.

COVID-19, Mi segíthet? - What can help? Above all, active prevention.

* ÚV lámpa (neon) használata a helységekben.
* Azelastine: [1], [2] , [*] nálunk Szlovákiában, mint Allergodil, orr spray ismert. (5ml recept nélkül vásárolható)
* Cistus creticus (Cystus pandalis): [4], [5] nálunk, mint ViroStop ismert, torokspray (de van orrspay és tabletta is)
* Artemisinin + Zinc: [6] egynyáriüröm kivonat, tabletta (Nagyon jó többfajta rákbetegségre is, de konzultálni kell az orvossal, ha más gyógyszereket is szedünk).
* Inosine pranobex: [9]
* Melatonin [10] , Quercetin (Kvercetín) [8] , Fluvoxamine [11] , NAC, N-acetylcysteín
* Ivermectin: [7] , [Ivermectin Triple Therapy Protocol for COVID-19 to Australian GP] , [Prof. Marik] , [SK, konečne] _
Ivermectin statisztikai adatok: [Epidemiologic Analyses on COVID-19 and Ivermectin] , [Dr. Thomas Borody, Australia] , [CZ]
[FLCCC, Ivermectin video], [A sok tesztelés nem segít], [FLCCC, Ivermectin] , [SK] , [Ivermectin, Vitamin D, Melatonin] , [Tanulmányok] , [ivmmeta.com]

Allergodil ViroStop D3 Artemisinin Artemisinin Zinc Melatonin Quercetin Ivermectin Inosine Galmektin

Az aktív prevenció abban van, hogy az Allergodil és a ViroStop meggátolja a vírus elszaporodását az orr és a száj nyálkahártyán. Mindezt "in-vitro" bizonyították. Az Allergodilt elegendő naponta egyszer (reggel) használni prevenciónak (de lehet többször is). A ViroStop-ot érdemes naponta többször is használni. A többi gyógyszer inkább csak akkor kell, ha a vírus mégis valahogyan nagyobb mennyiségben bejutna a szervezetünkbe, akkor az már fel legyen rá készülve. (Természetesen itt nem említek meg olyan alapvető dolgokat, mint a C vitamín, Aspirin, B1 stb.) Sajnos, relatíve kevés tanulmány foglalkozik az aktív megelőzéssel. Statistic Általában bizonyított COVID pozitív betegeken kísérleteznek, viszont a legjobb, ha el sem kapjuk ezt a betegséget, tehát meggátoljuk, hogy bejusson a szervezetünkbe. Az Ivermectint szintén használhatjuk preventíve, nagyon sok orvos már javasolja főleg időseknek. Tatiana Betáková (Szlovák Tudományos Akadémia): "Kérdés az, hogy a vírus továbbra is fog szaporodni a mi nyálkahártyankon, ha be leszünk oltva? Ezt még nem tudjuk, azért az oltás után is javasolva lesz a maszk viselése, hogy másokat ne fertőzzünk meg."
(This information has been compiled based on thousands of scientific studies. Anyone can check this here: [Google Scholar], [FLCCC Alliance] , [Protocol PDF] , [Hatásos gyógymód])
[Az oltás megoldás lesz?], [Mi történt Izraelben? PDF] ([PDF translate]) és [Israel CZ] , [Angliai jelentés] , [USA adatok] , [Furcsa eredmények] , [Agyi karosodások a covid után] , [Németországi adatok]
Mi mindent csináltak rosszul a COVID-19 kapcsán, mert nálunk is az történt, ami az USA-ban: [Link 1. video] vagy [Link 2. video] , [Link 3. cikk] , [DOC. MUDR. TÖRÖK az Ivermectinről] , [Ivermectin tapasztalatok] , [EU adatok a gyógyszerek mellékhatásairól, köztük a COVID vakcinák is]

Egy tudós (specialista a vakcinákra):
[Figyelmezteti a világot a lehetséges következményekre] , [VACCINATION WARNING]
HU: [G. V. Bossche figyelmeztésének rövid kivonata]
SK: [Varovanie od G. V. Bossche v skratke]
[Dr. Tenpenny, mRNA]

Az Európa Tanács (ET) a 2361 (2021) állásfoglalásban úgy határozott, hogy betiltja a tagállamok oltási kötelezettségeinek előírását.
EU-tagállamok kötelesek:
7.3.1 annak biztosítása, hogy az állampolgárok tájékoztatást kapjanak arról, hogy az oltás NEM kötelező, és hogy senkit sem politikai, társadalmi vagy egyéb módon nem kényszerítenek oltásra, hacsak nem akarják
7.3.2 annak biztosítása, hogy senkit ne érjen hátrányos megkülönböztetés, mert esetleges egészségügyi kockázatok miatt nem oltották be, vagy nem oltották be
7.1.5 független kompenzációs programok létrehozása az oltásokkal szemben az aránytalan és az oltásokkal okozott károk megtérítése érdekében

SK: [Pravidelné a celoplošné testovanie?]
2021-02-17
Jeden z najrenomovanejších lekárskych časopisov na svete „The Lancet“ publikuje štúdiu, ktorá ukazuje, že PCR test je na detekciu SARS-CoV-2 nepoužiteľný: I-MASK

"Väčšina ľudí infikovaných SARS-CoV-2 je nákazlivá po dobu 4–8 dní. Všeobecne sa nezistí, že by vzorky obsahovali kultúrne pozitívny (potenciálne nákazlivý) vírus po 9. dni po objavení sa symptómov, pričom väčšina prenosu nastala pred 5. dňom."

Uvedené platí aj pre antigenové aj pre protilátkové testy. Pred nástupom príznakov ochorenia 5 až 8 dní ešte nič nezistia, ale práve v tomto období pacient najviac infikuje svoje okolie. Na základe týchto informácií je úplne zbytočné robiť pravidelné plošné testovanie, ako je to na Slovenku. Zvyšuje sa iba nákaza. Potvrdenia vydané na jeden týždeň (covid negative) sú nanič.
Niektorí ľudia už museli absolvovať 48 testov, aby mohli chodiť do roboty. Neviem ako to "naši odborníci" odôvodňujú, ale je to proti zdravému rozumu a vyhadzovaniu peňazí. Nikde vo svete to takto nerobia, iba na Slovensku. (Asi naši "odborníci" majú patent na rozum.) [Dr. Horáková vrátila štátné vyznamenanie] , [Ivermectin na Slovensku video] , [News]
Čo všetko robili zle "odborníci", lebo to isté, čo sa stalo v USA, stalo sa aj u nás: [Link 1.] alebo [Link 2.] , [Link 3. text]

Je dôležité vedieť, že pacient môže žiadať od lekára liečenie pomocou Ivermectinu (po celom Slovensku aj v nemocniciach) v prípade COVID-19.

Kiszámolt értékek

New Cases, az új esteket százalékos értékei:
case120 = 100 * ws_case_120_days / ws_population
case30 = (120/30) * 100 * ws_case_30_days / ws_population
case7 = (120/7) * 100 * ws_case_7_days / ws_population

Relative Mortality számolása:
mortality120 = 100 * ws_death_120_days / (ws_case_127_days - ws_case_7_days)
mortality30 = 100 * ws_death_30_days / (ws_case_37_days - ws_case_7_days)

Ahol, ws_case_7_days (30,37,120,127), mindig az utolsó leadott jelentéstől kiszámított esetek száma, tehát
- ha Hungary utolsó jelentése 2021-02-10 volt, akkor onnan van számolva neki a 7,30,37,120,127 napos új esetek száma
- ha Szlovákia utolsó jelentése 2021-02-11 volt , akkor onnan van számolva neki a 7,30,37,120,127 napos új esetek száma
Ez azt jelenti, hogy lehet egy napos eltérés Szlovákia es Hungary kiszámolt értékei közt, de ezzel nem igen lehet semmit kezdeni.
Tekintettel arra, hogy a mortalitást 30 napra számolom, az ebből következő eltéres mértéke igen kicsi.
Itt sajnos probléma van USA és JAPAN esetében is, mivel más időzónában vannak, és mindenki máskor adja le a jelentést.
A WHO ezért 1-2 napos késéssel közli az adatokat. Ezen a weboldalon a WorldoMeter-től is aktualizálom az adatokat, melyek néhány ország esetében csak 1 napos vagy fél napos késéssel jönnek.
A kiszámolt értekek szempontjábol viszont ennek nincs nagy jelentősége, mert az eltérés igen kicsi a 30 napos átlagokat illetően.

Nagyon érdekes, ha ezeket az adatokat összehasonlítjuk "Our World in Data" által kiszámolt elhalálozási adatokkal.
Ott ugyanis az összes átlagon felüli elhalálozást veszik, nem csak a COVID-19 betegekét, amiből következtetni lehet a valódi elhalálozás mértékére, ami a COVID-19 kapcsán történik (függetlenül attól, hogy mit mondanak a COVID-19 kimutatások az adott országban). Az eltérő értékeknek több oka is lehet, például kevesebb ember kap színvonalas orvosi ellátást, vagy egyéb okok (mint például a kimutatások pontalansága) stb. Az is nagyon érdekes, ha összehasonlítjuk Izrael mortalitási adatatit más országokéval pl. Szlovákiával, akkor látható, hogy Izreaelben sokkal jobb eredményeket érnek el. Ez a vakcinázást megelőzően is igaz.

OurWorldInData: "https://github.com/owid/covid-19-data/tree/master/public/data", Slovakia: "https://github.com/Institut-Zdravotnych-Analyz/covid19-data"