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": 2021-04-14 22:57
(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 +2 942 (+2 313) +31 (+42)
CZ Czechia +5 033 (+3 870) +38 (+74)
DE Germany +32 546 (+10 772) +405 (+298)
HU Hungary +3 597 (+2 837) +285 (+272)
PL Poland +21 283 (+13 210) +803 (+645)
SK Slovakia, [gov], [okr]+1 069 (+870) +82 (+86)

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

N.RegionPopulationLast 120 daysLast 30 daysLast 7 days
1 South America 436 786 545 2.46% 3.15% 3.88%
2 Europe 755 992 143 2.89% 3.00% 2.74%
3 North America 592 289 123 2.91% 1.53% 1.74%
4 Asia 4 660 951 202 0.27% 0.49% 0.76%
5 Africa 1 364 902 618 0.15% 0.10% 0.11%
6 Australia/Oceania 43 189 762 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 436 786 545 2.56% 4.20% 3.15%
2 Africa 1 364 902 618 2.94% 3.42% 0.10%
3 North America 592 289 123 2.07% 2.80% 1.53%
4 Europe 755 992 143 2.23% 2.25% 3.00%
5 Asia 4 660 951 202 1.23% 1.20% 0.49%
6 Australia/Oceania 43 189 762 0.98% (0.86)% (0.07)%

COVID-19, Total Mortality from population of Regions

N.RegionPopulationDeaths1000*Deaths/Pop.
1 North America 592 289 123 819 972 1.384
2 South America 436 786 545 601 464 1.377
3 Europe 755 992 143 960 420 1.270
4 Asia 4 660 951 202 446 409 0.096
5 Africa 1 364 902 618 116 747 0.086
6 Australia/Oceania 43 189 762 1 290 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 3 483 158 3.94% 8.82% 13.26%
2 Asia TR Turkey 85 047 882 2.47% 5.11% 7.81%
3 Europe PL Poland 37 814 097 3.90% 7.44% 6.78%
4 Europe HU Hungary 9 641 101 4.63% 8.61% 6.76%
5 Europe HR Croatia 4 085 376 2.95% 4.58% 6.47%
6 Europe SI Slovenia 2 079 162 6.31% 5.33% 6.03%
7 Europe FR France 65 386 679 4.12% 6.30% 5.86%
8 South America AR Argentina 45 519 891 2.33% 3.20% 5.74%
9 Europe MK North Macedonia 2 083 307 3.32% 5.82% 5.39%
10 Europe NL Netherlands 17 164 578 4.32% 4.80% 4.99%
11 Europe CZ Czechia 10 724 549 9.36% 7.00% 4.68%
12 Asia JO Jordan 10 282 272 4.00% 7.64% 4.66%
13 Europe LT Lithuania 2 692 373 4.84% 3.50% 4.55%
14 South America CL Chile 19 244 471 2.67% 4.10% 4.52%
15 Europe RS Serbia 8 709 738 4.33% 6.06% 4.44%
16 Asia LB Lebanon 6 801 443 5.19% 4.81% 4.41%
17 South America BR Brazil 213 741 192 3.12% 3.97% 4.40%
18 Europe BG Bulgaria 6 907 330 2.85% 5.76% 4.27%
19 Europe BA Bosnia and Herzegovina 3 264 785 2.57% 5.04% 4.15%
20 Asia QA Qatar 2 807 805 1.81% 3.10% 4.10%
21 Asia KW Kuwait 4 319 819 2.40% 3.77% 4.10%
22 Europe UA Ukraine 43 527 334 2.25% 3.86% 4.05%
23 Europe SE Sweden 10 148 535 5.12% 5.36% 3.79%
24 South America PY Paraguay 7 200 926 2.00% 3.27% 3.63%
25 Asia AM Armenia 2 967 583 1.91% 3.56% 3.60%
26 Europe AT Austria 9 046 612 2.83% 3.89% 3.58%
27 South America PE Peru 33 330 131 2.01% 2.95% 3.28%
28 South America CO Colombia 51 306 497 2.20% 1.98% 3.21%
29 Asia IR Iran 84 831 939 1.18% 1.75% 3.15%
30 Europe IT Italy 60 392 079 3.21% 3.79% 3.10%
31 Europe GR Greece 10 383 068 1.69% 3.04% 3.07%
32 Europe DE Germany 83 993 775 2.03% 2.28% 2.95%
33 South America PR Puerto Rico 3 193 694 1.66% 1.59% 2.93%
34 Asia OM Oman 5 207 442 0.92% 2.11% 2.90%
35 Europe BE Belgium 11 629 113 2.72% 4.11% 2.90%
36 Asia MN Mongolia 3 319 434 0.48% 1.56% 2.73%
37 Europe MD Moldova 4 026 606 2.87% 3.81% 2.66%
38 Europe RO Romania 19 136 844 2.37% 3.20% 2.63%
39 Asia AZ Azerbaijan 10 210 604 1.11% 1.93% 2.61%
40 North America CR Costa Rica 5 130 473 1.44% 1.34% 2.52%
41 North America US USA 332 521 463 4.48% 2.21% 2.44%
42 North America CA Canada 38 000 812 1.60% 1.71% 2.43%
43 Europe CH Switzerland 8 704 121 2.67% 2.37% 2.41%
44 Asia GE Georgia 3 983 159 2.42% 1.61% 2.40%
45 Asia AE Arab Emirates 9 983 685 3.02% 2.45% 2.29%
46 Asia IQ Iraq 40 924 041 0.89% 1.79% 2.24%
47 Africa TN Tunisia 11 915 218 1.37% 1.12% 1.73%
48 Europe SK Slovakia 5 461 715 4.37% 2.51% 1.68%
49 Europe BY Belarus 9 446 886 1.86% 1.51% 1.59%
50 Europe NO Norway 5 454 387 1.15% 1.90% 1.47%
51 Asia KZ Kazakhstan 18 952 041 0.73% 1.07% 1.39%
52 Europe DK Denmark 5 808 176 2.07% 1.43% 1.37%
53 Asia IN India 1 390 605 073 0.29% 0.72% 1.34%
54 Africa BW Botswana 2 388 568 1.30% 1.57% 1.24%
55 Africa LY Libya 6 944 187 1.12% 1.40% 1.21%
56 South America EC Ecuador 17 851 927 0.83% 1.07% 1.18%
57 South America BO Bolivia 11 797 242 1.15% 0.82% 1.13%
58 Asia PH Philippines 110 719 062 0.39% 0.95% 1.11%
59 Europe AL Albania 2 875 332 2.77% 1.57% 1.07%
60 North America CU Cuba 11 321 194 0.70% 0.95% 1.01%
61 Europe IE Ireland 4 980 858 3.32% 1.21% 0.98%
62 North America PA Panama 4 367 710 3.80% 1.03% 0.94%
63 North America JM Jamaica 2 971 286 1.05% 1.67% 0.90%
64 North America HN Honduras 10 027 388 0.82% 0.74% 0.90%
65 Europe ES Spain 46 768 976 3.34% 1.26% 0.86%
66 Europe FI Finland 5 547 466 0.93% 1.14% 0.81%
67 North America GT Guatemala 18 173 591 0.42% 0.49% 0.78%
68 Europe RU Russia 145 983 521 1.34% 0.73% 0.70%
69 Europe PT Portugal 10 173 374 4.69% 0.56% 0.69%
70 North America DO Dominican R. 10 932 898 0.95% 0.46% 0.64%
71 Asia PS Palestine 5 193 556 0.04% 0.15% 0.63%
72 Africa NA Namibia 2 576 737 1.13% 0.73% 0.59%
73 Africa GA Gabon 2 266 534 0.50% 0.70% 0.58%
74 Asia MY Malaysia 32 689 620 0.85% 0.49% 0.56%
75 South America VE Venezuela 28 372 704 0.24% 0.43% 0.51%
76 Asia BD Bangladesh 165 971 278 0.12% 0.34% 0.47%
77 North America MX Mexico 129 990 868 0.80% 0.37% 0.43%
78 Europe GB United Kingdom 68 165 116 3.62% 0.70% 0.43%
79 Asia KG Kyrgyzstan 6 608 070 0.20% 0.24% 0.36%
80 Asia JP Japan 126 172 557 0.26% 0.20% 0.31%
81 North America SV El Salvador 6 511 822 0.38% 0.28% 0.31%
82 Asia SA Saudi Arabia 35 236 508 0.11% 0.20% 0.29%
83 Asia PK Pakistan 224 230 425 0.13% 0.22% 0.26%
84 Australia/Oceania PG Papua New Guinea 9 078 952 0.09% 0.30% 0.26%
85 Asia IL Israel 9 197 590 5.17% 0.68% 0.24%
86 Africa KE Kenya 54 692 071 0.10% 0.25% 0.23%
87 Africa ZA South Africa 59 892 296 1.16% 0.21% 0.22%
88 Asia ID Indonesia 275 782 804 0.35% 0.23% 0.22%
89 Africa CM Cameroon 27 058 922 0.12% 0.27% 0.22%
90 Asia KH Cambodia 16 899 460 0.03% 0.08% 0.20%
91 Africa ET Ethiopia 117 174 733 0.10% 0.20% 0.20%
92 Africa MG Madagascar 28 243 550 0.04% 0.11% 0.20%
93 Africa MA Morocco 37 252 112 0.27% 0.15% 0.18%
94 Africa TG Togo 8 429 471 0.10% 0.19% 0.16%
95 Asia NP Nepal 29 544 908 0.11% 0.08% 0.15%
96 Asia KR South Korea 51 303 772 0.13% 0.12% 0.15%
97 Asia TH Thailand 69 937 195 0.05% 0.05% 0.15%
98 Asia LK Sri Lanka 21 483 547 0.29% 0.14% 0.14%
99 Africa ML Mali 20 702 944 0.03% 0.07% 0.13%
100 Africa ZM Zambia 18 783 167 0.38% 0.12% 0.11%
101 Africa RW Rwanda 13 201 519 0.13% 0.10% 0.10%
102 Africa EG Egypt 103 836 905 0.09% 0.08% 0.09%
103 Asia SY Syria 17 830 832 0.06% 0.09% 0.08%
104 Africa GN Guinea 13 409 110 0.06% 0.10% 0.08%
105 Africa SD Sudan 44 644 713 0.03% 0.03% 0.07%
106 Asia UZ Uzbekistan 33 847 485 0.03% 0.06% 0.07%
107 Africa SO Somalia 16 237 049 0.05% 0.09% 0.07%
108 Africa GM Gambia 2 469 309 0.07% 0.11% 0.07%
109 Africa CF Central African R. 4 895 176 0.01% 0.04% 0.07%
110 Africa ER Eritrea 3 584 710 0.08% 0.05% 0.05%
111 Asia SG Singapore 5 886 318 0.04% 0.04% 0.05%
112 Africa BI Burundi 12 165 166 0.02% 0.03% 0.04%
113 Africa ZW Zimbabwe 15 031 654 0.17% 0.02% 0.04%
114 Africa AO Angola 33 657 917 0.02% 0.03% 0.04%
115 Africa SN Senegal 17 086 304 0.13% 0.06% 0.04%
116 Africa DZ Algeria 44 467 657 0.06% 0.03% 0.04%
117 Africa MR Mauritania 4 744 494 0.15% 0.05% 0.03%
118 Africa MZ Mozambique 31 934 412 0.16% 0.05% 0.03%
119 Asia YE Yemen 30 336 633 0.01% 0.04% 0.03%
120 Africa BJ Benin 12 369 609 0.04% 0.03% 0.03%
121 Africa CI Ivory Coast 26 885 472 0.09% 0.11% 0.03%
122 Asia AF Afghanistan 39 609 752 0.02% 0.01% 0.02%
123 Australia/Oceania NZ New Zealand 5 002 100 0.01% 0.01% 0.02%
124 Africa SS South Sudan 11 296 186 0.06% 0.03% 0.02%
125 Africa MW Malawi 19 513 605 0.14% 0.02% 0.02%
126 Africa GH Ghana 31 577 420 0.12% 0.05% 0.02%
127 Africa GW Guinea-Bissau 2 004 135 0.06% 0.05% 0.02%
128 Africa BF Burkina Faso 21 347 826 0.04% 0.01% 0.01%
129 North America NI Nicaragua 6 686 084 0.01% 0.01% 0.01%
130 North America HT Haiti 11 511 033 0.03% 0.01% 0.01%
131 Africa UG Uganda 46 859 327 0.03% 0.01% 0.01%
132 Africa TD Chad 16 791 222 0.02% 0.01% 0.01%
133 Africa NG Nigeria 210 106 735 0.04% 0.01% 0.01%
134 Africa SL Sierra Leone 8 103 183 0.02% 0.00% 0.01%
135 Australia/Oceania AU Australia 25 730 890 0.01% 0.00% 0.00%
136 Africa CD DR Congo 91 670 432 0.02% 0.01% 0.00%
137 Asia VN Vietnam 98 023 091 0.00% 0.00% 0.00%
138 Africa LS Lesotho 2 155 527 0.40% 0.03% 0.00%
139 Africa LR Liberia 5 150 035 0.01% 0.00% 0.00%
140 Africa NE Niger 24 883 615 0.01% 0.00% 0.00%
141 Asia HK Hong Kong 7 544 535 0.00% 0.00% 0.00%
142 Asia TW Taiwan 23 850 555 0.00% 0.00% 0.00%
143 Asia MM Myanmar 54 695 129 0.06% 0.00% 0.00%
144 Asia LA Laos 7 357 894 0.00% 0.00% 0.00%
145 Africa TZ Tanzania 61 053 207 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 9 704 150 0.00% 0.00% 0.00%
149 Asia CN China 1 439 323 776 0.00% 0.00% 0.00%
150 Africa CG Congo 5 623 557 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 Africa SD Sudan 44 644 713 7.53% (18.49)% (0.03)%
2 Asia YE Yemen 30 336 633 16.12% (15.35)% (0.04)%
3 North America MX Mexico 129 990 868 8.92% 12.27% 0.37%
4 Asia SY Syria 17 830 832 7.76% (8.09)% (0.09)%
5 Asia AF Afghanistan 39 609 752 6.35% (7.12)% (0.01)%
6 Africa SO Somalia 16 237 049 6.81% (7.11)% (0.09)%
7 Africa ZA South Africa 59 892 296 4.08% 6.62% 0.21%
8 Africa ZW Zimbabwe 15 031 654 4.74% (6.33)% (0.02)%
9 Africa EG Egypt 103 836 905 6.37% (6.13)% (0.08)%
10 Europe BA Bosnia and Herzegovina 3 264 785 5.05% 5.03% 5.04%
11 Europe SK Slovakia 5 461 715 3.84% 4.89% 2.51%
12 Europe RU Russia 145 983 521 2.68% 4.21% 0.73%
13 Africa TN Tunisia 11 915 218 3.47% 4.05% 1.12%
14 Africa MW Malawi 19 513 605 3.42% (4.02)% (0.02)%
15 Africa GW Guinea-Bissau 2 004 135 1.80% (4.00)% (0.05)%
16 South America BR Brazil 213 741 192 2.74% 3.84% 3.97%
17 Africa SN Senegal 17 086 304 3.24% (3.61)% (0.06)%
18 Europe BG Bulgaria 6 907 330 4.53% 3.44% 5.76%
19 Europe MK North Macedonia 2 083 307 3.15% 3.34% 5.82%
20 Africa CF Central African R. 4 895 176 2.58% (3.31)% (0.04)%
21 Africa MG Madagascar 28 243 550 3.04% 3.29% 0.11%
22 Africa LS Lesotho 2 155 527 3.16% (3.26)% (0.03)%
23 Europe HU Hungary 9 641 101 3.89% 3.19% 8.61%
24 Africa TD Chad 16 791 222 2.26% (3.05)% (0.01)%
25 South America CO Colombia 51 306 497 2.50% 2.82% 1.98%
26 South America PE Peru 33 330 131 3.00% 2.81% 2.95%
27 Africa DZ Algeria 44 467 657 1.82% (2.80)% (0.03)%
28 Europe MD Moldova 4 026 606 2.38% 2.68% 3.81%
29 North America HN Honduras 10 027 388 2.30% 2.68% 0.74%
30 Asia ID Indonesia 275 782 804 2.48% 2.67% 0.23%
31 North America GT Guatemala 18 173 591 3.66% 2.65% 0.49%
32 Africa NE Niger 24 883 615 3.44% (2.65)% (0.00)%
33 North America NI Nicaragua 6 686 084 2.59% (2.63)% (0.01)%
34 South America PY Paraguay 7 200 926 2.20% 2.62% 3.27%
35 Europe UA Ukraine 43 527 334 2.39% 2.59% 3.86%
36 Africa ML Mali 20 702 944 3.94% (2.58)% (0.07)%
37 South America EC Ecuador 17 851 927 2.49% 2.57% 1.07%
38 Europe GR Greece 10 383 068 3.25% 2.55% 3.04%
39 Europe RO Romania 19 136 844 2.56% 2.54% 3.20%
40 South America BO Bolivia 11 797 242 2.67% 2.49% 0.82%
41 Africa NA Namibia 2 576 737 1.40% 2.47% 0.73%
42 Africa BW Botswana 2 388 568 2.05% 2.38% 1.57%
43 North America SV El Salvador 6 511 822 3.39% 2.28% 0.28%
44 Asia AM Armenia 2 967 583 2.24% 2.22% 3.56%
45 Europe IT Italy 60 392 079 2.58% 2.09% 3.79%
46 Asia PK Pakistan 224 230 425 2.47% 2.01% 0.22%
47 Africa GM Gambia 2 469 309 2.59% 2.01% 0.11%
48 Europe AL Albania 2 875 332 1.57% 2.00% 1.57%
49 Africa AO Angola 33 657 917 2.56% (2.00)% (0.03)%
50 Europe CZ Czechia 10 724 549 1.82% 1.99% 7.00%
51 Europe HR Croatia 4 085 376 2.83% 1.99% 4.58%
52 Asia GE Georgia 3 983 159 1.74% 1.98% 1.61%
53 Europe PL Poland 37 814 097 2.61% 1.90% 7.44%
54 Asia AZ Azerbaijan 10 210 604 1.61% 1.87% 1.93%
55 Asia KG Kyrgyzstan 6 608 070 1.59% 1.79% 0.24%
56 Asia KH Cambodia 16 899 460 1.36% 1.79% 0.08%
57 Asia JP Japan 126 172 557 2.10% 1.77% 0.20%
58 Africa LY Libya 6 944 187 1.97% 1.74% 1.40%
59 Europe PT Portugal 10 173 374 2.26% 1.68% 0.56%
60 South America AR Argentina 45 519 891 1.83% 1.68% 3.20%
61 Europe IE Ireland 4 980 858 1.62% 1.66% 1.21%
62 Europe LT Lithuania 2 692 373 1.82% 1.57% 3.50%
63 Asia LB Lebanon 6 801 443 1.61% 1.56% 4.81%
64 Africa GH Ghana 31 577 420 1.11% 1.56% 0.05%
65 North America CR Costa Rica 5 130 473 1.55% 1.56% 1.34%
66 South America CL Chile 19 244 471 1.80% 1.56% 4.10%
67 Africa KE Kenya 54 692 071 1.56% 1.55% 0.25%
68 North America DO Dominican R. 10 932 898 0.98% 1.54% 0.46%
69 North America US USA 332 521 463 1.64% 1.54% 2.21%
70 Africa CD DR Congo 91 670 432 2.63% 1.52% 0.01%
71 North America PA Panama 4 367 710 1.57% 1.51% 1.03%
72 Africa MA Morocco 37 252 112 1.91% 1.49% 0.15%
73 South America UY Uruguay 3 483 158 1.27% 1.45% 8.82%
74 Africa CM Cameroon 27 058 922 1.40% 1.44% 0.27%
75 Asia SA Saudi Arabia 35 236 508 2.03% 1.44% 0.20%
76 South America VE Venezuela 28 372 704 1.34% 1.43% 0.43%
77 Europe DE Germany 83 993 775 3.31% 1.41% 2.28%
78 Asia IR Iran 84 831 939 1.38% 1.40% 1.75%
79 Asia BD Bangladesh 165 971 278 1.65% 1.36% 0.34%
80 Asia TW Taiwan 23 850 555 1.20% 1.35% 0.00%
81 Africa BF Burkina Faso 21 347 826 0.85% 1.34% 0.01%
82 North America JM Jamaica 2 971 286 1.37% 1.31% 1.67%
83 Africa ET Ethiopia 117 174 733 1.35% 1.28% 0.20%
84 Africa RW Rwanda 13 201 519 1.56% 1.19% 0.10%
85 Asia JO Jordan 10 282 272 1.12% 1.18% 7.64%
86 Africa MZ Mozambique 31 934 412 1.25% 1.16% 0.05%
87 Europe GB United Kingdom 68 165 116 2.41% 1.15% 0.70%
88 Asia PH Philippines 110 719 062 1.76% 1.14% 0.95%
89 Asia NP Nepal 29 544 908 3.67% 1.12% 0.08%
90 Asia KZ Kazakhstan 18 952 041 1.05% 1.09% 1.07%
91 Africa MR Mauritania 4 744 494 2.68% 1.08% 0.05%
92 Africa ZM Zambia 18 783 167 1.21% 1.04% 0.12%
93 Africa NG Nigeria 210 106 735 0.92% 0.97% 0.01%
94 Africa BJ Benin 12 369 609 1.15% 0.97% 0.03%
95 Europe ES Spain 46 768 976 1.60% 0.96% 1.26%
96 Asia IL Israel 9 197 590 0.68% 0.94% 0.68%
97 Europe AT Austria 9 046 612 1.94% 0.92% 3.89%
98 South America PR Puerto Rico 3 193 694 1.66% 0.92% 1.59%
99 Europe FR France 65 386 679 1.60% 0.91% 6.30%
100 Asia OM Oman 5 207 442 0.81% 0.88% 2.11%
101 Asia LK Sri Lanka 21 483 547 0.67% 0.88% 0.14%
102 Africa GN Guinea 13 409 110 0.78% 0.86% 0.10%
103 Asia IN India 1 390 605 073 0.92% 0.85% 0.72%
104 Europe BY Belarus 9 446 886 0.61% 0.82% 1.51%
105 Europe BE Belgium 11 629 113 1.68% 0.82% 4.11%
106 Asia MM Myanmar 54 695 129 2.16% 0.79% 0.00%
107 Europe RS Serbia 8 709 738 0.86% 0.78% 6.06%
108 Asia KR South Korea 51 303 772 1.73% 0.76% 0.12%
109 Australia/Oceania PG Papua New Guinea 9 078 952 0.91% 0.75% 0.30%
110 Africa ER Eritrea 3 584 710 0.37% 0.72% 0.05%
111 Africa UG Uganda 46 859 327 0.64% 0.72% 0.01%
112 North America CA Canada 38 000 812 1.66% 0.71% 1.71%
113 Africa GA Gabon 2 266 534 0.60% 0.71% 0.70%
114 Europe SI Slovenia 2 079 162 1.56% 0.69% 5.33%
115 Africa CG Congo 5 623 557 0.86% 0.66% 0.05%
116 Africa SS South Sudan 11 296 186 0.73% 0.66% 0.03%
117 Asia IQ Iraq 40 924 041 0.68% 0.65% 1.79%
118 Asia KW Kuwait 4 319 819 0.53% 0.63% 3.77%
119 Asia TR Turkey 85 047 882 0.93% 0.62% 5.11%
120 Africa TG Togo 8 429 471 0.61% 0.62% 0.19%
121 Africa CI Ivory Coast 26 885 472 0.58% 0.60% 0.11%
122 Europe FI Finland 5 547 466 0.80% 0.52% 1.14%
123 Europe CH Switzerland 8 704 121 1.50% 0.50% 2.37%
124 Africa BI Burundi 12 165 166 0.22% 0.44% 0.03%
125 Asia QA Qatar 2 807 805 0.23% 0.42% 3.10%
126 North America CU Cuba 11 321 194 0.47% 0.42% 0.95%
127 Europe NL Netherlands 17 164 578 0.90% 0.39% 4.80%
128 North America HT Haiti 11 511 033 0.50% 0.37% 0.01%
129 Asia MY Malaysia 32 689 620 0.33% 0.33% 0.49%
130 Asia UZ Uzbekistan 33 847 485 0.22% 0.31% 0.06%
131 Asia TH Thailand 69 937 195 0.14% 0.29% 0.05%
132 Asia MN Mongolia 3 319 434 0.25% 0.27% 1.56%
133 Europe DK Denmark 5 808 176 1.08% 0.26% 1.43%
134 Europe NO Norway 5 454 387 0.51% 0.26% 1.90%
135 Europe SE Sweden 10 148 535 1.02% 0.26% 5.36%
136 Asia AE Arab Emirates 9 983 685 0.31% 0.22% 2.45%
137 Asia SG Singapore 5 886 318 0.04% 0.00% 0.04%
138 Australia/Oceania NZ New Zealand 5 002 100 0.22% 0.00% 0.01%
139 Australia/Oceania AU Australia 25 730 890 0.07% 0.00% 0.00%
140 Africa SL Sierra Leone 8 103 183 0.26% 0.00% 0.00%
141 Africa LR Liberia 5 150 035 0.52% 0.00% 0.00%
142 Asia VN Vietnam 98 023 091 0.00% 0.00% 0.00%
143 Asia LA Laos 7 357 894 0.00% 0.00% 0.00%
144 Asia PS Palestine 5 193 556 0.00% 0.00% 0.15%
145 Asia HK Hong Kong 7 544 535 0.00% 0.00% 0.00%
146 Africa TZ Tanzania 61 053 207 0.00% 0.00% 0.00%
147 Asia KP North Korea 25 660 000 0.00% 0.00% 0.00%
148 Europe TM Turkmenistan 6 118 000 0.00% 0.00% 0.00%
149 Asia TJ Tajikistan 9 704 150 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 10 724 549 28 076 2.618
2 Europe HU Hungary 9 641 101 24 265 2.517
3 Europe BA Bosnia and Herzegovina 3 264 785 7 563 2.317
4 Europe BG Bulgaria 6 907 330 14 746 2.135
5 Europe SI Slovenia 2 079 162 4 416 2.124
6 Europe MK North Macedonia 2 083 307 4 295 2.062
7 Europe BE Belgium 11 629 113 23 566 2.026
8 Europe SK Slovakia 5 461 715 10 712 1.961
9 Europe IT Italy 60 392 079 115 081 1.906
10 Europe GB United Kingdom 68 165 116 127 138 1.865
11 North America US USA 332 521 463 557 419 1.676
12 South America BR Brazil 213 741 192 356 824 1.669
13 Europe PT Portugal 10 173 374 16 924 1.664
14 South America PE Peru 33 330 131 55 162 1.655
15 Europe ES Spain 46 768 976 76 310 1.632
16 North America MX Mexico 129 990 868 209 930 1.615
17 Europe PL Poland 37 814 097 59 929 1.585
18 Europe HR Croatia 4 085 376 6 399 1.566
19 Europe FR France 65 386 679 98 802 1.511
20 North America PA Panama 4 367 710 6 169 1.412
21 Europe LT Lithuania 2 692 373 3 717 1.381
22 Europe MD Moldova 4 026 606 5 437 1.350
23 Europe SE Sweden 10 148 535 13 657 1.346
24 Europe RO Romania 19 136 844 25 412 1.328
25 South America CO Colombia 51 306 497 66 215 1.291
26 Asia AM Armenia 2 967 583 3 794 1.278
27 South America AR Argentina 45 519 891 57 996 1.274
28 South America CL Chile 19 244 471 24 513 1.274
29 Europe CH Switzerland 8 704 121 9 782 1.124
30 South America BO Bolivia 11 797 242 12 478 1.058
31 Europe AT Austria 9 046 612 9 479 1.048
32 Asia LB Lebanon 6 801 443 6 743 0.991
33 Europe NL Netherlands 17 164 578 16 819 0.980
34 Asia GE Georgia 3 983 159 3 901 0.979
35 South America EC Ecuador 17 851 927 17 347 0.972
36 Europe IE Ireland 4 980 858 4 794 0.963
37 Europe DE Germany 83 993 775 79 151 0.942
38 Africa ZA South Africa 59 892 296 53 431 0.892
39 Europe UA Ukraine 43 527 334 38 225 0.878
40 Europe GR Greece 10 383 068 9 042 0.871
41 Europe AL Albania 2 875 332 2 326 0.809
42 Africa TN Tunisia 11 915 218 9 416 0.790
43 Asia JO Jordan 10 282 272 7 905 0.769
44 Asia IR Iran 84 831 939 65 068 0.767
45 Europe RU Russia 145 983 521 104 000 0.712
46 Asia IL Israel 9 197 590 6 312 0.686
47 South America PY Paraguay 7 200 926 4 916 0.683
48 South America PR Puerto Rico 3 193 694 2 155 0.675
49 Europe RS Serbia 8 709 738 5 811 0.667
50 North America CA Canada 38 000 812 23 364 0.615
51 North America CR Costa Rica 5 130 473 3 056 0.596
52 North America HN Honduras 10 027 388 4 816 0.480
53 South America UY Uruguay 3 483 158 1 524 0.438
54 Europe DK Denmark 5 808 176 2 444 0.421
55 Africa LY Libya 6 944 187 2 832 0.408
56 Asia TR Turkey 85 047 882 34 461 0.405
57 Asia AZ Azerbaijan 10 210 604 3 975 0.389
58 North America GT Guatemala 18 173 591 7 057 0.388
59 Asia IQ Iraq 40 924 041 14 797 0.362
60 Asia OM Oman 5 207 442 1 798 0.345
61 Asia KW Kuwait 4 319 819 1 419 0.329
62 North America SV El Salvador 6 511 822 2 057 0.316
63 North America DO Dominican R. 10 932 898 3 397 0.311
64 Africa BW Botswana 2 388 568 663 0.278
65 Europe BY Belarus 9 446 886 2 373 0.251
66 Africa MA Morocco 37 252 112 8 914 0.239
67 Asia KG Kyrgyzstan 6 608 070 1 534 0.232
68 North America JM Jamaica 2 971 286 686 0.231
69 Africa NA Namibia 2 576 737 581 0.226
70 Asia KZ Kazakhstan 18 952 041 3 963 0.209
71 Asia SA Saudi Arabia 35 236 508 6 773 0.192
72 Europe FI Finland 5 547 466 878 0.158
73 Asia ID Indonesia 275 782 804 42 780 0.155
74 Asia AE Arab Emirates 9 983 685 1 537 0.154
75 Africa LS Lesotho 2 155 527 315 0.146
76 Asia PH Philippines 110 719 062 15 311 0.138
77 Europe NO Norway 5 454 387 705 0.129
78 Asia IN India 1 390 605 073 172 095 0.124
79 Asia QA Qatar 2 807 805 343 0.122
80 Africa EG Egypt 103 836 905 12 526 0.121
81 Asia NP Nepal 29 544 908 3 056 0.103
82 Africa ZW Zimbabwe 15 031 654 1 547 0.103
83 Africa MR Mauritania 4 744 494 451 0.095
84 Asia SY Syria 17 830 832 1 395 0.078
85 Asia JP Japan 126 172 557 9 469 0.075
86 Africa DZ Algeria 44 467 657 3 138 0.071
87 Asia PK Pakistan 224 230 425 15 636 0.070
88 Africa GM Gambia 2 469 309 168 0.068
89 Africa ZM Zambia 18 783 167 1 227 0.065
90 Asia AF Afghanistan 39 609 752 2 530 0.064
91 South America VE Venezuela 28 372 704 1 798 0.063
92 Africa SN Senegal 17 086 304 1 081 0.063
93 Asia BD Bangladesh 165 971 278 9 918 0.060
94 Asia MM Myanmar 54 695 129 3 206 0.059
95 Africa MW Malawi 19 513 605 1 133 0.058
96 Africa GA Gabon 2 266 534 127 0.056
97 Africa SD Sudan 44 644 713 2 307 0.052
98 Africa KE Kenya 54 692 071 2 394 0.044
99 North America CU Cuba 11 321 194 478 0.042
100 Asia MY Malaysia 32 689 620 1 341 0.041
101 Africa SO Somalia 16 237 049 624 0.038
102 Asia YE Yemen 30 336 633 1 074 0.035
103 Australia/Oceania AU Australia 25 730 890 909 0.035
104 Asia KR South Korea 51 303 772 1 782 0.035
105 Africa GW Guinea-Bissau 2 004 135 66 0.033
106 Africa CM Cameroon 27 058 922 851 0.031
107 Asia LK Sri Lanka 21 483 547 598 0.028
108 Africa ET Ethiopia 117 174 733 3 230 0.028
109 North America NI Nicaragua 6 686 084 180 0.027
110 Africa MZ Mozambique 31 934 412 791 0.025
111 Africa CG Congo 5 623 557 137 0.024
112 Africa GH Ghana 31 577 420 756 0.024
113 Africa RW Rwanda 13 201 519 316 0.024
114 North America HT Haiti 11 511 033 251 0.022
115 Africa ML Mali 20 702 944 416 0.020
116 Asia UZ Uzbekistan 33 847 485 634 0.019
117 Africa MG Madagascar 28 243 550 514 0.018
118 Africa AO Angola 33 657 917 557 0.017
119 Africa LR Liberia 5 150 035 85 0.017
120 Africa CF Central African R. 4 895 176 74 0.015
121 Africa TG Togo 8 429 471 116 0.014
122 Africa GN Guinea 13 409 110 136 0.010
123 Africa SS South Sudan 11 296 186 114 0.010
124 Africa CI Ivory Coast 26 885 472 269 0.010
125 Africa TD Chad 16 791 222 167 0.010
126 Africa NG Nigeria 210 106 735 2 060 0.010
127 Africa SL Sierra Leone 8 103 183 79 0.010
128 Asia TJ Tajikistan 9 704 150 90 0.009
129 Asia MN Mongolia 3 319 434 27 0.008
130 Africa CD DR Congo 91 670 432 745 0.008
131 Australia/Oceania PG Papua New Guinea 9 078 952 71 0.008
132 Africa NE Niger 24 883 615 189 0.008
133 Africa BJ Benin 12 369 609 93 0.007
134 Africa BF Burkina Faso 21 347 826 153 0.007
135 Africa UG Uganda 46 859 327 338 0.007
136 Australia/Oceania NZ New Zealand 5 002 100 26 0.005
137 Asia SG Singapore 5 886 318 30 0.005
138 Asia PS Palestine 5 193 556 22 0.004
139 Africa ER Eritrea 3 584 710 10 0.003
140 Asia KH Cambodia 16 899 460 35 0.002
141 Asia TH Thailand 69 937 195 97 0.001
142 Africa BI Burundi 12 165 166 6 0.001
143 Asia TW Taiwan 23 850 555 11 0.001
144 Asia VN Vietnam 98 023 091 35 0.000
145 Africa TZ Tanzania 61 053 207 21 0.000
146 Asia HK Hong Kong 7 544 535 1 0.000
147 Asia CN China 1 439 323 776 0 0.000
148 Asia KP North Korea 25 660 000 0 0.000
149 Europe TM Turkmenistan 6 118 000 0 0.000
150 Asia LA Laos 7 357 894 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"