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": 2021-05-13 10:13
(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 +0 (+1 194) +0 (+15)
CZ Czechia +1 261 (+1 688) +13 (+29)
DE Germany +0 (+13 833) +0 (+252)
HU Hungary +1 416 (+905) +82 (+96)
PL Poland +0 (+4 255) +0 (+343)
SK Slovakia, [gov], [okr]+0 (+404) +0 (+19)

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

N.RegionPopulationLast 120 daysLast 30 daysLast 7 days
1 South America 437 067 986 2.78% 3.35% 3.27%
2 Europe 756 027 621 2.54% 2.08% 1.47%
3 North America 592 654 653 2.05% 1.41% 1.18%
4 Asia 4 663 614 701 0.50% 1.14% 1.07%
5 Africa 1 367 452 974 0.12% 0.09% 0.08%
6 Australia/Oceania 43 229 630 0.04% 0.05% 0.07%

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 437 067 986 2.79% 4.13% 3.35%
2 Africa 1 367 452 974 2.92% (3.88)% (0.09)%
3 Europe 756 027 621 2.15% 2.61% 2.08%
4 North America 592 654 653 2.25% 2.11% 1.41%
5 Australia/Oceania 43 229 630 1.06% (1.87)% (0.05)%
6 Asia 4 663 614 701 1.11% 1.26% 1.14%

COVID-19, Total Mortality from population of Regions

N.RegionPopulationDeaths1000*Deaths/Pop.
1 South America 437 067 986 713 402 1.632
2 North America 592 654 653 854 113 1.441
3 Europe 756 027 621 1 042 832 1.379
4 Asia 4 663 614 701 574 726 0.123
5 Africa 1 367 452 974 125 695 0.092
6 Australia/Oceania 43 229 630 1 356 0.031

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 3 484 110 5.69% 9.24% 8.95%
2 North America CR Costa Rica 5 134 155 1.84% 3.95% 5.57%
3 South America AR Argentina 45 552 720 3.20% 5.77% 5.35%
4 Europe LT Lithuania 2 689 386 3.52% 5.11% 5.21%
5 Europe NL Netherlands 17 167 573 4.05% 5.15% 4.61%
6 Europe SE Sweden 10 153 515 5.07% 5.85% 4.40%
7 South America PY Paraguay 7 207 867 2.58% 3.73% 3.77%
8 South America CO Colombia 51 349 445 2.40% 3.86% 3.76%
9 Asia GE Georgia 3 982 555 2.09% 3.65% 3.75%
10 Asia NP Nepal 29 586 485 0.53% 1.92% 3.64%
11 South America BR Brazil 213 860 730 3.35% 3.38% 3.44%
12 South America CL Chile 19 257 458 3.17% 3.69% 3.34%
13 Europe HR Croatia 4 083 381 3.09% 5.31% 3.22%
14 Asia KW Kuwait 4 324 826 3.07% 3.65% 3.03%
15 Europe SI Slovenia 2 079 185 5.08% 3.95% 3.03%
16 Europe FR France 65 398 090 4.54% 4.53% 2.94%
17 Europe GR Greece 10 379 054 2.15% 2.79% 2.90%
18 Asia IN India 1 391 679 242 0.92% 2.77% 2.79%
19 Asia MN Mongolia 3 323 621 1.36% 3.71% 2.58%
20 Asia TR Turkey 85 119 750 3.20% 5.49% 2.35%
21 Asia IR Iran 84 917 119 1.66% 2.89% 2.35%
22 North America CA Canada 38 027 004 1.66% 2.50% 2.27%
23 South America PE Peru 33 366 534 2.47% 2.55% 2.16%
24 Europe BE Belgium 11 633 100 3.02% 3.11% 2.08%
25 Asia QA Qatar 2 807 805 2.35% 3.01% 1.98%
26 Europe DK Denmark 5 809 788 1.34% 1.64% 1.97%
27 Asia AE Arab Emirates 9 993 152 3.05% 2.20% 1.96%
28 Europe DE Germany 84 014 947 1.91% 2.57% 1.81%
29 South America BO Bolivia 11 809 861 1.25% 1.37% 1.74%
30 Europe IT Italy 60 385 062 3.03% 2.33% 1.72%
31 Asia OM Oman 5 217 765 1.38% 2.39% 1.61%
32 Europe UA Ukraine 43 506 626 2.31% 2.42% 1.53%
33 Asia MY Malaysia 32 722 476 0.95% 1.11% 1.51%
34 North America US USA 332 674 970 3.02% 1.89% 1.51%
35 Europe RS Serbia 8 706 962 3.90% 2.68% 1.50%
36 Asia IQ Iraq 40 995 731 1.28% 1.90% 1.49%
37 Europe CH Switzerland 8 709 128 2.13% 2.32% 1.47%
38 Europe AT Austria 9 050 676 2.73% 2.40% 1.46%
39 Europe CZ Czechia 10 726 119 7.41% 2.42% 1.41%
40 Europe BY Belarus 9 446 640 1.65% 1.46% 1.39%
41 Europe PL Poland 37 810 825 3.83% 2.70% 1.38%
42 Europe HU Hungary 9 639 165 4.66% 2.79% 1.34%
43 Europe ES Spain 46 770 406 2.97% 1.84% 1.33%
44 Europe BG Bulgaria 6 903 209 2.94% 2.18% 1.24%
45 Africa TN Tunisia 11 925 011 1.33% 1.72% 1.22%
46 Asia LK Sri Lanka 21 490 644 0.39% 0.69% 1.20%
47 South America PR Puerto Rico 3 193 694 1.64% 2.70% 1.19%
48 North America CU Cuba 11 320 648 0.92% 1.13% 1.17%
49 North America PA Panama 4 373 096 1.99% 0.90% 1.12%
50 South America EC Ecuador 17 873 167 1.02% 1.29% 1.11%
51 Asia AM Armenia 2 968 021 1.92% 2.18% 1.09%
52 Asia LB Lebanon 6 799 033 4.52% 2.15% 1.05%
53 North America HN Honduras 10 039 879 0.93% 1.04% 1.03%
54 Europe MK North Macedonia 2 083 300 3.23% 2.37% 1.02%
55 Europe BA Bosnia and Herzegovina 3 263 177 2.62% 2.10% 1.01%
56 Europe IE Ireland 4 985 227 1.97% 1.02% 1.01%
57 Europe NO Norway 5 457 742 1.12% 1.02% 0.97%
58 Asia JO Jordan 10 290 291 4.01% 2.06% 0.90%
59 Africa BW Botswana 2 392 337 1.38% 1.06% 0.85%
60 Africa NA Namibia 2 580 388 0.84% 0.73% 0.79%
61 North America DO Dominican R. 10 941 511 0.81% 0.57% 0.77%
62 Asia AZ Azerbaijan 10 217 838 1.01% 1.61% 0.73%
63 Asia KZ Kazakhstan 18 969 833 0.98% 1.49% 0.73%
64 Europe RO Romania 19 126 731 2.05% 1.26% 0.71%
65 Europe SK Slovakia 5 461 924 3.25% 1.13% 0.70%
66 Asia PH Philippines 110 834 638 0.57% 0.87% 0.69%
67 Asia KG Kyrgyzstan 6 616 606 0.26% 0.55% 0.61%
68 Europe RU Russia 145 988 463 0.98% 0.68% 0.59%
69 Europe MD Moldova 4 025 866 2.56% 1.13% 0.58%
70 Asia JP Japan 126 142 014 0.28% 0.46% 0.53%
71 North America GT Guatemala 18 199 887 0.50% 0.73% 0.51%
72 Africa LY Libya 6 951 592 1.08% 0.71% 0.50%
73 South America VE Venezuela 28 366 348 0.33% 0.50% 0.50%
74 Europe FI Finland 5 548 145 0.91% 0.50% 0.45%
75 North America JM Jamaica 2 972 308 1.12% 0.56% 0.42%
76 Europe GB United Kingdom 68 193 314 1.77% 0.40% 0.40%
77 Africa GA Gabon 2 270 706 0.62% 0.55% 0.40%
78 Europe PT Portugal 10 171 028 3.38% 0.50% 0.40%
79 Africa ZA South Africa 59 951 541 0.57% 0.29% 0.39%
80 Asia TH Thailand 69 951 033 0.11% 0.32% 0.34%
81 Asia SA Saudi Arabia 35 279 497 0.19% 0.34% 0.34%
82 Asia KH Cambodia 16 917 798 0.12% 0.38% 0.31%
83 Australia/Oceania PG Papua New Guinea 9 092 401 0.13% 0.19% 0.30%
84 Africa CM Cameroon 27 111 439 0.17% 0.26% 0.26%
85 Europe AL Albania 2 875 084 2.34% 0.46% 0.25%
86 Asia ID Indonesia 276 011 839 0.32% 0.23% 0.23%
87 North America MX Mexico 130 098 135 0.64% 0.27% 0.22%
88 Asia PK Pakistan 224 570 836 0.16% 0.25% 0.20%
89 Asia PS Palestine 5 202 977 0.01% 0.04% 0.16%
90 Asia KR South Korea 51 307 257 0.12% 0.15% 0.14%
91 North America SV El Salvador 6 514 410 0.33% 0.25% 0.14%
92 Asia UZ Uzbekistan 33 885 934 0.05% 0.12% 0.13%
93 Africa EG Egypt 103 990 069 0.09% 0.11% 0.13%
94 Africa CF Central African R. 4 901 838 0.04% 0.11% 0.12%
95 Asia BD Bangladesh 166 101 183 0.15% 0.21% 0.10%
96 Africa CG Congo 5 634 351 0.07% 0.09% 0.10%
97 Africa AO Angola 33 739 304 0.03% 0.07% 0.10%
98 Africa KE Kenya 54 786 136 0.12% 0.13% 0.09%
99 Africa MG Madagascar 28 300 139 0.08% 0.16% 0.08%
100 Africa MA Morocco 37 286 768 0.16% 0.13% 0.08%
101 Asia LA Laos 7 366 263 0.02% 0.07% 0.08%
102 Asia AF Afghanistan 39 679 387 0.02% 0.06% 0.08%
103 Africa MR Mauritania 4 754 211 0.07% 0.06% 0.07%
104 Africa ET Ethiopia 117 401 048 0.12% 0.11% 0.06%
105 Africa ER Eritrea 3 588 600 0.06% 0.04% 0.05%
106 Africa DZ Algeria 44 530 480 0.05% 0.05% 0.05%
107 Africa RW Rwanda 13 227 036 0.12% 0.07% 0.05%
108 Asia IL Israel 9 197 590 3.58% 0.12% 0.05%
109 Asia SG Singapore 5 889 959 0.04% 0.05% 0.04%
110 Africa GN Guinea 13 437 438 0.07% 0.05% 0.04%
111 Asia SY Syria 17 864 615 0.06% 0.07% 0.04%
112 Africa SO Somalia 16 272 317 0.06% 0.05% 0.04%
113 Africa LS Lesotho 2 156 871 0.26% 0.01% 0.03%
114 Africa ZM Zambia 18 824 120 0.33% 0.05% 0.03%
115 Africa TG Togo 8 444 886 0.11% 0.06% 0.03%
116 Africa SN Senegal 17 121 385 0.11% 0.03% 0.02%
117 Africa BI Burundi 12 193 348 0.03% 0.04% 0.02%
118 North America NI Nicaragua 6 692 328 0.01% 0.02% 0.02%
119 Africa CI Ivory Coast 26 937 384 0.08% 0.02% 0.02%
120 North America HT Haiti 11 522 045 0.03% 0.01% 0.02%
121 Africa GH Ghana 31 628 917 0.12% 0.02% 0.02%
122 Africa UG Uganda 46 974 338 0.01% 0.01% 0.01%
123 Africa BJ Benin 12 394 855 0.04% 0.02% 0.01%
124 Africa GM Gambia 2 474 710 0.08% 0.05% 0.01%
125 Africa MZ Mozambique 32 004 065 0.15% 0.02% 0.01%
126 Africa ML Mali 20 749 349 0.03% 0.04% 0.01%
127 Africa ZW Zimbabwe 15 048 804 0.10% 0.03% 0.01%
128 Asia VN Vietnam 98 092 415 0.00% 0.00% 0.01%
129 Africa SD Sudan 44 726 050 0.02% 0.02% 0.01%
130 Asia YE Yemen 30 388 802 0.01% 0.01% 0.01%
131 Australia/Oceania AU Australia 25 754 325 0.01% 0.01% 0.01%
132 Africa TD Chad 16 828 720 0.01% 0.01% 0.01%
133 Africa SL Sierra Leone 8 116 060 0.02% 0.00% 0.01%
134 Africa MW Malawi 19 552 902 0.13% 0.01% 0.01%
135 Asia TW Taiwan 23 853 960 0.00% 0.00% 0.01%
136 Asia MM Myanmar 54 723 983 0.02% 0.00% 0.01%
137 Africa CD DR Congo 91 887 149 0.01% 0.01% 0.01%
138 Africa BF Burkina Faso 21 393 410 0.02% 0.01% 0.00%
139 Africa LR Liberia 5 159 479 0.01% 0.00% 0.00%
140 Australia/Oceania NZ New Zealand 5 002 100 0.01% 0.00% 0.00%
141 Africa SS South Sudan 11 306 582 0.06% 0.01% 0.00%
142 Africa GW Guinea-Bissau 2 007 831 0.06% 0.01% 0.00%
143 Africa NG Nigeria 210 512 785 0.03% 0.00% 0.00%
144 Africa NE Niger 24 953 468 0.01% 0.00% 0.00%
145 Africa TZ Tanzania 61 188 559 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 9 721 165 0.00% 0.00% 0.00%
149 Asia CN China 1 439 323 776 0.00% 0.00% 0.00%
150 Asia HK Hong Kong 7 549 348 0.00% 0.00% 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 LS Lesotho 2 156 871 2.94% (15.38)% (0.01)%
2 Asia YE Yemen 30 388 802 15.42% (13.63)% (0.01)%
3 Africa SD Sudan 44 726 050 9.26% (11.33)% (0.02)%
4 North America MX Mexico 130 098 135 9.47% 9.77% 0.27%
5 Asia SY Syria 17 864 615 7.89% (8.09)% (0.07)%
6 Europe SK Slovakia 5 461 924 4.63% 7.75% 1.13%
7 Europe BA Bosnia and Herzegovina 3 263 177 5.28% 6.20% 2.10%
8 Africa EG Egypt 103 990 069 6.55% 5.90% 0.11%
9 Africa SO Somalia 16 272 317 6.62% (5.77)% (0.05)%
10 Africa MW Malawi 19 552 902 3.38% (5.41)% (0.01)%
11 Europe HU Hungary 9 639 165 3.95% 5.20% 2.79%
12 North America HT Haiti 11 522 045 1.02% (4.99)% (0.01)%
13 Europe BG Bulgaria 6 903 209 4.38% 4.97% 2.18%
14 Europe MK North Macedonia 2 083 300 3.66% 4.95% 2.37%
15 Europe RO Romania 19 126 731 2.99% 4.81% 1.26%
16 Africa ZA South Africa 59 951 541 4.48% 4.50% 0.29%
17 Asia AF Afghanistan 39 679 387 5.13% (4.38)% (0.06)%
18 Africa TN Tunisia 11 925 011 3.73% 4.24% 1.72%
19 Europe RU Russia 145 988 463 3.30% 4.17% 0.68%
20 South America PE Peru 33 366 534 3.29% 4.09% 2.55%
21 Africa DZ Algeria 44 530 480 2.36% (3.90)% (0.05)%
22 South America BR Brazil 213 860 730 3.13% 3.88% 3.38%
23 North America HN Honduras 10 039 879 2.68% 3.81% 1.04%
24 South America PY Paraguay 7 207 867 2.75% 3.75% 3.73%
25 South America EC Ecuador 17 873 167 2.91% 3.64% 1.29%
26 Europe MD Moldova 4 025 866 2.67% 3.57% 1.13%
27 Europe PL Poland 37 810 825 2.64% 3.36% 2.70%
28 Asia ID Indonesia 276 011 839 2.52% 3.23% 0.23%
29 Europe GR Greece 10 379 054 2.79% 3.01% 2.79%
30 Europe UA Ukraine 43 506 626 2.69% 2.92% 2.42%
31 Africa SN Senegal 17 121 385 3.11% (2.89)% (0.03)%
32 Africa NA Namibia 2 580 388 1.87% 2.86% 0.73%
33 North America JM Jamaica 2 972 308 1.53% 2.78% 0.56%
34 South America CO Colombia 51 349 445 2.62% 2.73% 3.86%
35 Africa ML Mali 20 749 349 3.03% (2.66)% (0.04)%
36 Africa KE Kenya 54 786 136 1.92% 2.65% 0.13%
37 Africa ZW Zimbabwe 15 048 804 4.86% (2.60)% (0.03)%
38 North America SV El Salvador 6 514 410 3.23% 2.53% 0.25%
39 Asia AM Armenia 2 968 021 2.33% 2.52% 2.18%
40 Asia PK Pakistan 224 570 836 2.42% 2.50% 0.25%
41 South America BO Bolivia 11 809 861 2.65% 2.40% 1.37%
42 Europe IT Italy 60 385 062 2.31% 2.28% 2.33%
43 Europe AL Albania 2 875 084 1.65% 2.21% 0.46%
44 North America GT Guatemala 18 199 887 2.89% 2.12% 0.73%
45 Europe HR Croatia 4 083 381 2.51% 2.12% 5.31%
46 Africa BF Burkina Faso 21 393 410 1.16% (2.12)% (0.01)%
47 Europe CZ Czechia 10 726 119 1.87% 2.05% 2.42%
48 North America NI Nicaragua 6 692 328 2.41% (2.01)% (0.02)%
49 Africa TD Chad 16 828 720 2.50% 1.98% 0.01%
50 South America UY Uruguay 3 484 110 1.59% 1.97% 9.24%
51 Africa MZ Mozambique 32 004 065 1.24% 1.92% 0.02%
52 Africa AO Angola 33 739 304 2.27% 1.89% 0.07%
53 Africa MG Madagascar 28 300 139 2.31% 1.87% 0.16%
54 Asia KG Kyrgyzstan 6 616 606 1.96% 1.86% 0.55%
55 Asia BD Bangladesh 166 101 183 1.69% 1.81% 0.21%
56 Asia IL Israel 9 197 590 0.68% 1.80% 0.12%
57 Africa CM Cameroon 27 111 439 1.58% 1.76% 0.26%
58 Asia IR Iran 84 917 119 1.47% 1.73% 2.89%
59 Africa CI Ivory Coast 26 937 384 0.65% 1.73% 0.02%
60 Africa GM Gambia 2 474 710 2.37% 1.71% 0.05%
61 Asia JO Jordan 10 290 291 1.23% 1.71% 2.06%
62 Africa ET Ethiopia 117 401 048 1.44% 1.71% 0.11%
63 Asia LB Lebanon 6 799 033 1.75% 1.70% 2.15%
64 Africa CD DR Congo 91 887 149 1.38% 1.69% 0.01%
65 Africa BW Botswana 2 392 337 2.13% 1.65% 1.06%
66 Africa CF Central African R. 4 901 838 2.05% 1.65% 0.11%
67 Africa LY Libya 6 951 592 1.91% 1.64% 0.71%
68 South America AR Argentina 45 552 720 1.69% 1.61% 5.77%
69 Africa GH Ghana 31 628 917 1.18% 1.54% 0.02%
70 South America CL Chile 19 257 458 1.70% 1.52% 3.69%
71 South America VE Venezuela 28 366 348 1.43% 1.52% 0.50%
72 Asia AZ Azerbaijan 10 217 838 1.74% 1.50% 1.61%
73 Asia GE Georgia 3 982 555 1.83% 1.48% 3.65%
74 Asia NP Nepal 29 586 485 2.40% 1.47% 1.92%
75 Asia MM Myanmar 54 723 983 2.18% 1.44% 0.00%
76 Africa BJ Benin 12 394 855 1.19% 1.40% 0.02%
77 Australia/Oceania PG Papua New Guinea 9 092 401 1.14% 1.40% 0.19%
78 Africa GW Guinea-Bissau 2 007 831 1.71% 1.33% 0.01%
79 Asia PH Philippines 110 834 638 1.54% 1.32% 0.87%
80 Asia JP Japan 126 142 014 1.91% 1.31% 0.46%
81 North America PA Panama 4 373 096 1.63% 1.27% 0.90%
82 Africa MA Morocco 37 286 768 1.94% 1.25% 0.13%
83 Europe IE Ireland 4 985 227 1.84% 1.20% 1.02%
84 Europe DE Germany 84 014 947 2.58% 1.20% 2.57%
85 Asia SA Saudi Arabia 35 279 497 1.37% 1.20% 0.34%
86 North America US USA 332 674 970 1.77% 1.19% 1.89%
87 Africa MR Mauritania 4 754 211 1.97% 1.16% 0.06%
88 North America DO Dominican R. 10 941 511 1.22% 1.15% 0.57%
89 Asia LK Sri Lanka 21 490 644 0.87% 1.13% 0.69%
90 Africa ZM Zambia 18 824 120 1.10% 1.13% 0.05%
91 Europe RS Serbia 8 706 962 0.86% 1.12% 2.68%
92 Africa NE Niger 24 953 468 2.87% 1.12% 0.00%
93 Europe AT Austria 9 050 676 1.34% 1.12% 2.40%
94 North America CR Costa Rica 5 134 155 1.29% 1.09% 3.95%
95 Asia OM Oman 5 217 765 0.94% 1.07% 2.39%
96 Europe LT Lithuania 2 689 386 1.79% 1.06% 5.11%
97 Europe BE Belgium 11 633 100 1.21% 1.04% 3.11%
98 Asia IN India 1 391 679 242 0.96% 0.99% 2.77%
99 North America CU Cuba 11 320 648 0.62% 0.97% 1.13%
100 South America PR Puerto Rico 3 193 694 1.36% 0.95% 2.70%
101 Europe FR France 65 398 090 1.27% 0.91% 4.53%
102 Africa GN Guinea 13 437 438 0.82% 0.91% 0.05%
103 Africa RW Rwanda 13 227 036 1.30% 0.88% 0.07%
104 Asia TH Thailand 69 951 033 0.64% 0.85% 0.32%
105 Europe BY Belarus 9 446 640 0.70% 0.84% 1.46%
106 Africa UG Uganda 46 974 338 0.71% 0.83% 0.01%
107 Africa CG Congo 5 634 351 0.75% 0.82% 0.09%
108 Asia TW Taiwan 23 853 960 1.41% 0.81% 0.00%
109 Europe GB United Kingdom 68 193 314 2.87% 0.78% 0.40%
110 Asia QA Qatar 2 807 805 0.43% 0.75% 3.01%
111 Europe ES Spain 46 770 406 1.49% 0.73% 1.84%
112 Asia KZ Kazakhstan 18 969 833 0.91% 0.72% 1.49%
113 Europe SI Slovenia 2 079 185 1.10% 0.71% 3.95%
114 Asia KH Cambodia 16 917 798 0.79% 0.70% 0.38%
115 Europe FI Finland 5 548 145 0.65% 0.69% 0.50%
116 Asia TR Turkey 85 119 750 0.77% 0.68% 5.49%
117 Asia KW Kuwait 4 324 826 0.56% 0.61% 3.65%
118 Asia KR South Korea 51 307 257 1.18% 0.60% 0.15%
119 Asia MY Malaysia 32 722 476 0.40% 0.59% 1.11%
120 North America CA Canada 38 027 004 1.20% 0.58% 2.50%
121 Europe NO Norway 5 457 742 0.42% 0.56% 1.02%
122 Europe PT Portugal 10 171 028 2.22% 0.56% 0.50%
123 Africa ER Eritrea 3 588 600 0.27% 0.55% 0.04%
124 Asia MN Mongolia 3 323 621 0.47% 0.53% 3.71%
125 Asia IQ Iraq 40 995 731 0.60% 0.52% 1.90%
126 Africa TG Togo 8 444 886 0.56% 0.49% 0.06%
127 Africa GA Gabon 2 270 706 0.57% 0.48% 0.55%
128 Asia UZ Uzbekistan 33 885 934 0.31% 0.36% 0.12%
129 Europe CH Switzerland 8 709 128 0.93% 0.33% 2.32%
130 Africa NG Nigeria 210 512 785 0.95% 0.31% 0.00%
131 Africa SS South Sudan 11 306 582 0.74% 0.30% 0.01%
132 Europe NL Netherlands 17 167 573 0.70% 0.27% 5.15%
133 Europe DK Denmark 5 809 788 1.09% 0.26% 1.64%
134 Europe SE Sweden 10 153 515 0.65% 0.25% 5.85%
135 Australia/Oceania AU Australia 25 754 325 0.07% 0.20% 0.01%
136 Asia AE Arab Emirates 9 993 152 0.29% 0.15% 2.20%
137 Asia SG Singapore 5 889 959 0.08% 0.13% 0.05%
138 Asia LA Laos 7 366 263 0.10% 0.10% 0.07%
139 Africa BI Burundi 12 193 348 0.12% 0.00% 0.04%
140 Australia/Oceania NZ New Zealand 5 002 100 0.22% 0.00% 0.00%
141 Africa LR Liberia 5 159 479 0.65% 0.00% 0.00%
142 Africa SL Sierra Leone 8 116 060 0.14% 0.00% 0.00%
143 Asia VN Vietnam 98 092 415 0.00% 0.00% 0.00%
144 Asia PS Palestine 5 202 977 0.00% 0.00% 0.04%
145 Asia KP North Korea 25 660 000 0.00% 0.00% 0.00%
146 Africa TZ Tanzania 61 188 559 0.00% 0.00% 0.00%
147 Europe TM Turkmenistan 6 118 000 0.00% 0.00% 0.00%
148 Asia TJ Tajikistan 9 721 165 0.00% 0.00% 0.00%
149 Asia CN China 1 439 323 776 0.00% 0.00% 0.00%
150 Asia HK Hong Kong 7 549 348 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 HU Hungary 9 639 165 28 970 3.005
2 Europe CZ Czechia 10 726 119 29 800 2.778
3 Europe BA Bosnia and Herzegovina 3 263 177 8 943 2.741
4 Europe BG Bulgaria 6 903 209 17 150 2.484
5 Europe MK North Macedonia 2 083 300 5 151 2.473
6 Europe SI Slovenia 2 079 185 4 622 2.223
7 Europe SK Slovakia 5 461 924 12 096 2.215
8 Europe BE Belgium 11 633 100 24 630 2.117
9 Europe IT Italy 60 385 062 123 544 2.046
10 South America BR Brazil 213 860 730 425 774 1.991
11 South America PE Peru 33 366 534 64 580 1.936
12 Europe GB United Kingdom 68 193 314 127 640 1.872
13 Europe PL Poland 37 810 825 70 679 1.869
14 Europe HR Croatia 4 083 381 7 589 1.859
15 North America US USA 332 674 970 577 655 1.736
16 Europe ES Spain 46 770 406 79 208 1.694
17 North America MX Mexico 130 098 135 219 356 1.686
18 Europe PT Portugal 10 171 028 16 998 1.671
19 Europe FR France 65 398 090 106 335 1.626
20 South America CO Colombia 51 349 445 78 832 1.535
21 Europe RO Romania 19 126 731 29 233 1.528
22 Europe LT Lithuania 2 689 386 4 066 1.512
23 South America AR Argentina 45 552 720 68 317 1.500
24 Europe MD Moldova 4 025 866 5 981 1.486
25 Asia AM Armenia 2 968 021 4 291 1.446
26 North America PA Panama 4 373 096 6 280 1.436
27 South America CL Chile 19 257 458 27 384 1.422
28 Europe SE Sweden 10 153 515 14 253 1.404
29 Europe CH Switzerland 8 709 128 10 080 1.157
30 South America BO Bolivia 11 809 861 13 308 1.127
31 Europe AT Austria 9 050 676 10 171 1.124
32 Asia LB Lebanon 6 799 033 7 549 1.110
33 Asia GE Georgia 3 982 555 4 379 1.099
34 Europe UA Ukraine 43 506 626 47 333 1.088
35 South America EC Ecuador 17 873 167 19 349 1.083
36 Europe GR Greece 10 379 054 11 211 1.080
37 Europe DE Germany 84 014 947 85 632 1.019
38 Europe NL Netherlands 17 167 573 17 399 1.014
39 South America PY Paraguay 7 207 867 7 283 1.010
40 Europe IE Ireland 4 985 227 4 937 0.990
41 Africa TN Tunisia 11 925 011 11 637 0.976
42 South America UY Uruguay 3 484 110 3 215 0.923
43 Africa ZA South Africa 59 951 541 54 968 0.917
44 Asia IR Iran 84 917 119 75 934 0.894
45 Asia JO Jordan 10 290 291 9 180 0.892
46 Europe AL Albania 2 875 084 2 423 0.843
47 Europe RU Russia 145 988 463 114 331 0.783
48 Europe RS Serbia 8 706 962 6 611 0.759
49 South America PR Puerto Rico 3 193 694 2 384 0.747
50 Asia IL Israel 9 197 590 6 379 0.694
51 North America CR Costa Rica 5 134 155 3 456 0.673
52 North America CA Canada 38 027 004 24 734 0.650
53 North America HN Honduras 10 039 879 5 765 0.574
54 Asia TR Turkey 85 119 750 43 821 0.515
55 Asia AZ Azerbaijan 10 217 838 4 726 0.463
56 Africa LY Libya 6 951 592 3 082 0.443
57 Europe DK Denmark 5 809 788 2 499 0.430
58 North America GT Guatemala 18 199 887 7 775 0.427
59 Asia OM Oman 5 217 765 2 148 0.412
60 Asia IQ Iraq 40 995 731 15 855 0.387
61 Asia KW Kuwait 4 324 826 1 669 0.386
62 North America SV El Salvador 6 514 410 2 168 0.333
63 North America DO Dominican R. 10 941 511 3 554 0.325
64 Africa BW Botswana 2 392 337 751 0.314
65 Europe BY Belarus 9 446 640 2 661 0.282
66 North America JM Jamaica 2 972 308 820 0.276
67 Africa NA Namibia 2 580 388 702 0.272
68 Asia KG Kyrgyzstan 6 616 606 1 681 0.254
69 Africa MA Morocco 37 286 768 9 088 0.244
70 Asia KZ Kazakhstan 18 969 833 4 542 0.239
71 Asia SA Saudi Arabia 35 279 497 7 111 0.202
72 Asia QA Qatar 2 807 805 519 0.185
73 Asia IN India 1 391 679 242 254 197 0.183
74 Asia ID Indonesia 276 011 839 47 617 0.172
75 Asia PH Philippines 110 834 638 18 714 0.169
76 Europe FI Finland 5 548 145 930 0.168
77 Asia AE Arab Emirates 9 993 152 1 619 0.162
78 Africa LS Lesotho 2 156 871 319 0.148
79 Asia NP Nepal 29 586 485 4 252 0.144
80 Europe NO Norway 5 457 742 775 0.142
81 Africa EG Egypt 103 990 069 14 091 0.136
82 Africa ZW Zimbabwe 15 048 804 1 579 0.105
83 Africa MR Mauritania 4 754 211 457 0.096
84 Asia SY Syria 17 864 615 1 676 0.094
85 Asia JP Japan 126 142 014 11 064 0.088
86 Asia PK Pakistan 224 570 836 19 232 0.086
87 South America VE Venezuela 28 366 348 2 321 0.082
88 Africa DZ Algeria 44 530 480 3 350 0.075
89 Asia BD Bangladesh 166 101 183 12 045 0.072
90 Africa GM Gambia 2 474 710 175 0.071
91 Asia AF Afghanistan 39 679 387 2 721 0.069
92 North America CU Cuba 11 320 648 768 0.068
93 Africa ZM Zambia 18 824 120 1 259 0.067
94 Africa SN Senegal 17 121 385 1 121 0.066
95 Africa GA Gabon 2 270 706 143 0.063
96 Africa MW Malawi 19 552 902 1 153 0.059
97 Asia MM Myanmar 54 723 983 3 211 0.059
98 Asia MN Mongolia 3 323 621 188 0.057
99 Africa SD Sudan 44 726 050 2 446 0.055
100 Africa KE Kenya 54 786 136 2 950 0.054
101 Asia MY Malaysia 32 722 476 1 761 0.054
102 Africa SO Somalia 16 272 317 753 0.046
103 Africa CM Cameroon 27 111 439 1 144 0.042
104 Asia YE Yemen 30 388 802 1 278 0.042
105 Asia LK Sri Lanka 21 490 644 868 0.040
106 Asia KR South Korea 51 307 257 1 891 0.037
107 Australia/Oceania AU Australia 25 754 325 910 0.035
108 Africa ET Ethiopia 117 401 048 3 938 0.034
109 Africa GW Guinea-Bissau 2 007 831 67 0.033
110 North America NI Nicaragua 6 692 328 184 0.028
111 Africa CG Congo 5 634 351 148 0.026
112 Africa MG Madagascar 28 300 139 738 0.026
113 Africa MZ Mozambique 32 004 065 826 0.026
114 Africa RW Rwanda 13 227 036 340 0.026
115 Africa GH Ghana 31 628 917 783 0.025
116 Africa ML Mali 20 749 349 507 0.024
117 North America HT Haiti 11 522 045 269 0.023
118 Asia UZ Uzbekistan 33 885 934 667 0.020
119 Africa CF Central African R. 4 901 838 95 0.019
120 Africa AO Angola 33 739 304 645 0.019
121 Africa LR Liberia 5 159 479 85 0.017
122 Africa TG Togo 8 444 886 125 0.015
123 Australia/Oceania PG Papua New Guinea 9 092 401 130 0.014
124 Africa GN Guinea 13 437 438 151 0.011
125 Africa CI Ivory Coast 26 937 384 292 0.011
126 Africa SS South Sudan 11 306 582 115 0.010
127 Africa TD Chad 16 828 720 172 0.010
128 Africa NG Nigeria 210 512 785 2 066 0.010
129 Africa SL Sierra Leone 8 116 060 79 0.010
130 Asia TJ Tajikistan 9 721 165 90 0.009
131 Africa CD DR Congo 91 887 149 775 0.008
132 Africa BJ Benin 12 394 855 101 0.008
133 Asia KH Cambodia 16 917 798 136 0.008
134 Africa BF Burkina Faso 21 393 410 164 0.008
135 Africa NE Niger 24 953 468 192 0.008
136 Africa UG Uganda 46 974 338 346 0.007
137 Asia TH Thailand 69 951 033 486 0.007
138 Asia SG Singapore 5 889 959 31 0.005
139 Australia/Oceania NZ New Zealand 5 002 100 26 0.005
140 Africa ER Eritrea 3 588 600 12 0.003
141 Asia PS Palestine 5 202 977 8 0.002
142 Africa BI Burundi 12 193 348 6 0.001
143 Asia TW Taiwan 23 853 960 12 0.001
144 Asia VN Vietnam 98 092 415 35 0.000
145 Africa TZ Tanzania 61 188 559 21 0.000
146 Asia LA Laos 7 366 263 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 HK Hong Kong 7 549 348 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) Cistus a Vironal
* 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"