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": 2021-05-08 10:01
(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 (+1 333) +0 (+18)
CZ Czechia +1 631 (+1 572) +15 (+36)
DE Germany +0 (+17 550) +0 (+245)
HU Hungary +1 376 (+1 541) +101 (+106)
PL Poland +0 (+6 047) +0 (+453)
SK Slovakia, [gov], [okr]+0 (+362) +0 (+26)

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

N.RegionPopulationLast 120 daysLast 30 daysLast 7 days
1 South America 437 019 460 2.76% 3.48% 3.19%
2 Europe 756 021 509 2.63% 2.32% 1.63%
3 North America 592 591 628 2.24% 1.52% 1.24%
4 Asia 4 663 155 475 0.46% 1.07% 1.13%
5 Africa 1 367 013 259 0.12% 0.09% 0.08%
6 Australia/Oceania 43 222 757 0.04% 0.05% 0.03%

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 437 019 460 2.78% 4.10% 3.48%
2 Africa 1 367 013 259 2.94% (3.64)% (0.09)%
3 Europe 756 021 509 2.17% 2.53% 2.32%
4 North America 592 591 628 2.22% 2.16% 1.52%
5 Australia/Oceania 43 222 757 1.04% (1.42)% (0.05)%
6 Asia 4 663 155 475 1.11% 1.20% 1.07%

COVID-19, Total Mortality from population of Regions

N.RegionPopulationDeaths1000*Deaths/Pop.
1 South America 437 019 460 697 137 1.595
2 North America 592 591 628 849 204 1.433
3 Europe 756 021 509 1 032 230 1.365
4 Asia 4 663 155 475 546 608 0.117
5 Africa 1 367 013 259 124 217 0.091
6 Australia/Oceania 43 222 757 1 347 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 3 483 946 5.44% 10.28% 8.28%
2 Europe LT Lithuania 2 689 901 3.57% 5.02% 5.24%
3 South America AR Argentina 45 547 060 3.11% 5.85% 5.24%
4 Europe NL Netherlands 17 167 057 4.06% 5.29% 4.98%
5 North America CR Costa Rica 5 133 520 1.71% 3.36% 4.86%
6 Europe SE Sweden 10 152 657 5.12% 6.32% 4.82%
7 Europe HR Croatia 4 083 725 3.08% 5.87% 4.33%
8 Asia GE Georgia 3 982 659 2.13% 3.46% 4.03%
9 Europe SI Slovenia 2 079 181 5.31% 4.45% 3.95%
10 Asia MN Mongolia 3 322 899 1.26% 3.74% 3.77%
11 South America CO Colombia 51 342 040 2.40% 3.77% 3.68%
12 Asia TR Turkey 85 107 359 3.17% 6.41% 3.58%
13 Asia KW Kuwait 4 323 963 3.01% 3.84% 3.56%
14 South America PY Paraguay 7 206 670 2.50% 3.71% 3.52%
15 South America BR Brazil 213 840 120 3.34% 3.57% 3.35%
16 South America CL Chile 19 255 219 3.15% 4.00% 3.34%
17 Europe FR France 65 396 123 4.55% 5.01% 3.33%
18 Asia NP Nepal 29 579 317 0.39% 1.34% 3.15%
19 Asia IN India 1 391 494 040 0.80% 2.46% 2.87%
20 Asia QA Qatar 2 807 805 2.31% 3.40% 2.70%
21 Asia IR Iran 84 902 433 1.60% 3.03% 2.59%
22 North America CA Canada 38 022 488 1.68% 2.57% 2.44%
23 Europe BE Belgium 11 632 413 3.00% 3.28% 2.31%
24 Europe DE Germany 84 011 297 1.96% 2.76% 2.23%
25 Europe GR Greece 10 379 746 2.07% 2.82% 2.16%
26 Asia AE Arab Emirates 9 991 520 3.11% 2.27% 2.14%
27 South America PE Peru 33 360 257 2.42% 2.79% 2.02%
28 Asia OM Oman 5 215 985 1.33% 2.60% 2.00%
29 Europe IT Italy 60 386 272 3.10% 2.60% 1.99%
30 Europe AT Austria 9 049 975 2.77% 2.74% 1.86%
31 Europe CZ Czechia 10 725 848 7.78% 2.85% 1.82%
32 Europe DK Denmark 5 809 511 1.37% 1.55% 1.79%
33 Europe RS Serbia 8 707 441 3.96% 3.18% 1.76%
34 South America PR Puerto Rico 3 193 694 1.70% 2.98% 1.70%
35 Europe HU Hungary 9 639 499 4.70% 3.82% 1.65%
36 Europe CH Switzerland 8 708 265 2.19% 2.50% 1.63%
37 Asia IQ Iraq 40 983 371 1.23% 2.03% 1.62%
38 North America US USA 332 648 503 3.33% 2.07% 1.61%
39 South America BO Bolivia 11 807 685 1.25% 1.26% 1.52%
40 Europe ES Spain 46 770 160 3.18% 1.97% 1.48%
41 Europe PL Poland 37 811 389 3.88% 3.73% 1.46%
42 South America EC Ecuador 17 869 505 1.00% 1.28% 1.44%
43 Asia AM Armenia 2 967 945 1.94% 2.56% 1.44%
44 Europe UA Ukraine 43 510 196 2.32% 2.85% 1.42%
45 Europe BY Belarus 9 446 682 1.69% 1.51% 1.37%
46 Asia LB Lebanon 6 799 448 4.81% 2.70% 1.33%
47 Africa TN Tunisia 11 923 323 1.39% 1.79% 1.31%
48 Europe BG Bulgaria 6 903 920 2.93% 2.61% 1.27%
49 Asia MY Malaysia 32 716 812 0.93% 0.95% 1.24%
50 Asia JO Jordan 10 288 909 4.03% 2.64% 1.21%
51 Europe BA Bosnia and Herzegovina 3 263 454 2.64% 2.63% 1.17%
52 North America HN Honduras 10 037 725 0.92% 1.01% 1.13%
53 Europe MK North Macedonia 2 083 301 3.27% 3.07% 1.09%
54 North America CU Cuba 11 320 742 0.89% 1.11% 1.09%
55 Africa BW Botswana 2 391 688 1.41% 1.12% 1.06%
56 Asia AZ Azerbaijan 10 216 590 1.01% 1.92% 1.06%
57 Asia LK Sri Lanka 21 489 420 0.35% 0.51% 1.05%
58 Europe IE Ireland 4 984 474 2.49% 1.01% 1.04%
59 Asia KZ Kazakhstan 18 966 765 0.94% 1.54% 0.89%
60 Europe SK Slovakia 5 461 888 3.46% 1.33% 0.88%
61 Europe NO Norway 5 457 163 1.14% 1.11% 0.87%
62 North America PA Panama 4 372 167 2.32% 0.87% 0.85%
63 Asia PH Philippines 110 814 711 0.55% 0.97% 0.78%
64 Europe RO Romania 19 128 475 2.12% 1.58% 0.78%
65 Europe RU Russia 145 987 611 1.05% 0.71% 0.68%
66 Europe MD Moldova 4 025 994 2.59% 1.46% 0.67%
67 Asia KG Kyrgyzstan 6 615 134 0.24% 0.51% 0.60%
68 Africa NA Namibia 2 579 758 0.89% 0.70% 0.60%
69 North America GT Guatemala 18 195 353 0.51% 0.78% 0.57%
70 Africa CM Cameroon 27 102 385 0.18% 0.31% 0.55%
71 Africa LY Libya 6 950 315 1.10% 0.83% 0.54%
72 North America DO Dominican R. 10 940 026 0.85% 0.55% 0.53%
73 North America JM Jamaica 2 972 132 1.11% 0.68% 0.49%
74 Europe FI Finland 5 548 028 0.91% 0.57% 0.47%
75 Asia JP Japan 126 147 280 0.29% 0.42% 0.46%
76 South America VE Venezuela 28 367 444 0.31% 0.49% 0.45%
77 Asia KH Cambodia 16 914 637 0.11% 0.36% 0.44%
78 Europe PT Portugal 10 171 433 3.76% 0.54% 0.40%
79 Europe GB United Kingdom 68 188 452 2.10% 0.41% 0.36%
80 Asia TH Thailand 69 948 647 0.10% 0.29% 0.35%
81 Asia SA Saudi Arabia 35 272 085 0.17% 0.33% 0.34%
82 Africa ZA South Africa 59 941 326 0.70% 0.26% 0.32%
83 Europe AL Albania 2 875 127 2.43% 0.61% 0.29%
84 North America SV El Salvador 6 513 963 0.34% 0.28% 0.24%
85 North America MX Mexico 130 079 641 0.68% 0.32% 0.24%
86 Asia PK Pakistan 224 512 144 0.16% 0.27% 0.22%
87 Asia ID Indonesia 275 972 350 0.33% 0.23% 0.22%
88 Asia PS Palestine 5 201 353 0.01% 0.04% 0.18%
89 Africa GA Gabon 2 269 987 0.60% 0.54% 0.18%
90 Africa CG Congo 5 632 490 0.07% 0.08% 0.14%
91 Asia UZ Uzbekistan 33 879 305 0.05% 0.11% 0.14%
92 Asia KR South Korea 51 306 656 0.12% 0.15% 0.14%
93 Africa EG Egypt 103 963 661 0.09% 0.11% 0.13%
94 Asia BD Bangladesh 166 078 786 0.15% 0.27% 0.12%
95 Africa KE Kenya 54 769 918 0.12% 0.16% 0.10%
96 Asia LA Laos 7 364 820 0.02% 0.06% 0.10%
97 Africa MG Madagascar 28 290 383 0.07% 0.18% 0.10%
98 Australia/Oceania PG Papua New Guinea 9 090 082 0.12% 0.18% 0.10%
99 Africa MA Morocco 37 280 793 0.17% 0.15% 0.09%
100 Africa CF Central African R. 4 900 689 0.03% 0.11% 0.09%
101 Africa AO Angola 33 725 272 0.03% 0.06% 0.08%
102 Asia AF Afghanistan 39 667 381 0.02% 0.05% 0.08%
103 Africa MR Mauritania 4 752 536 0.07% 0.06% 0.08%
104 Asia IL Israel 9 197 590 4.00% 0.16% 0.07%
105 Africa DZ Algeria 44 519 649 0.05% 0.05% 0.06%
106 Africa ET Ethiopia 117 362 028 0.11% 0.14% 0.06%
107 Africa RW Rwanda 13 222 636 0.12% 0.08% 0.05%
108 Asia SY Syria 17 858 790 0.06% 0.08% 0.05%
109 Africa SO Somalia 16 266 237 0.06% 0.06% 0.05%
110 Asia SG Singapore 5 889 331 0.04% 0.05% 0.05%
111 Africa GN Guinea 13 432 554 0.06% 0.06% 0.04%
112 Africa ZM Zambia 18 817 059 0.36% 0.06% 0.04%
113 Africa ER Eritrea 3 587 929 0.06% 0.04% 0.03%
114 Africa TG Togo 8 442 228 0.11% 0.08% 0.03%
115 Africa SN Senegal 17 115 337 0.12% 0.03% 0.03%
116 Africa GM Gambia 2 473 779 0.08% 0.06% 0.03%
117 Africa LS Lesotho 2 156 639 0.38% 0.01% 0.02%
118 North America NI Nicaragua 6 691 251 0.01% 0.01% 0.02%
119 Africa SD Sudan 44 712 026 0.02% 0.02% 0.02%
120 Africa BI Burundi 12 188 489 0.03% 0.04% 0.02%
121 Africa ML Mali 20 741 348 0.03% 0.06% 0.02%
122 Africa CI Ivory Coast 26 928 434 0.09% 0.02% 0.02%
123 Africa ZW Zimbabwe 15 045 847 0.13% 0.04% 0.02%
124 North America HT Haiti 11 520 146 0.03% 0.01% 0.02%
125 Africa GH Ghana 31 620 038 0.12% 0.02% 0.02%
126 Africa UG Uganda 46 954 508 0.01% 0.01% 0.01%
127 Africa MZ Mozambique 31 992 056 0.16% 0.02% 0.01%
128 Australia/Oceania NZ New Zealand 5 002 100 0.01% 0.01% 0.01%
129 Africa SS South Sudan 11 304 789 0.06% 0.01% 0.01%
130 Africa BJ Benin 12 390 502 0.04% 0.02% 0.01%
131 Asia YE Yemen 30 379 807 0.01% 0.02% 0.01%
132 Australia/Oceania AU Australia 25 750 285 0.01% 0.01% 0.01%
133 Africa MW Malawi 19 546 127 0.14% 0.01% 0.01%
134 Africa CD DR Congo 91 849 784 0.01% 0.01% 0.01%
135 Africa LR Liberia 5 157 851 0.01% 0.00% 0.01%
136 Africa TD Chad 16 822 255 0.01% 0.01% 0.01%
137 Africa NE Niger 24 941 425 0.01% 0.00% 0.01%
138 Africa BF Burkina Faso 21 385 551 0.03% 0.01% 0.00%
139 Asia VN Vietnam 98 080 462 0.00% 0.00% 0.00%
140 Africa SL Sierra Leone 8 113 840 0.02% 0.00% 0.00%
141 Asia TW Taiwan 23 853 373 0.00% 0.00% 0.00%
142 Asia MM Myanmar 54 719 008 0.03% 0.00% 0.00%
143 Africa GW Guinea-Bissau 2 007 194 0.06% 0.02% 0.00%
144 Africa NG Nigeria 210 442 777 0.03% 0.00% 0.00%
145 Asia KP North Korea 25 660 000 0.00% 0.00% 0.00%
146 Africa TZ Tanzania 61 165 222 0.00% 0.00% 0.00%
147 Europe TM Turkmenistan 6 118 000 0.00% 0.00% 0.00%
148 Asia TJ Tajikistan 9 718 231 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 7 548 518 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 Asia YE Yemen 30 379 807 15.62% (14.44)% (0.02)%
2 North America MX Mexico 130 079 641 9.55% 12.49% 0.32%
3 Africa SD Sudan 44 712 026 9.02% (11.01)% (0.02)%
4 Africa LS Lesotho 2 156 639 3.30% (8.89)% (0.01)%
5 Europe SK Slovakia 5 461 888 4.44% 8.10% 1.33%
6 Asia SY Syria 17 858 790 7.82% (7.77)% (0.08)%
7 Africa SO Somalia 16 266 237 6.68% (6.14)% (0.06)%
8 Africa EG Egypt 103 963 661 6.43% 5.86% 0.11%
9 Europe BA Bosnia and Herzegovina 3 263 454 5.21% 5.75% 2.63%
10 Africa MW Malawi 19 546 127 3.45% (5.12)% (0.01)%
11 Asia AF Afghanistan 39 667 381 5.74% (4.88)% (0.05)%
12 Europe HU Hungary 9 639 499 3.96% 4.87% 3.82%
13 Africa ZA South Africa 59 941 326 4.37% 4.77% 0.26%
14 Europe BG Bulgaria 6 903 920 4.39% 4.62% 2.61%
15 Europe MK North Macedonia 2 083 301 3.60% 4.45% 3.07%
16 Europe RU Russia 145 987 611 3.17% 4.28% 0.71%
17 Europe RO Romania 19 128 475 2.93% 4.14% 1.58%
18 South America BR Brazil 213 840 120 3.12% 4.12% 3.57%
19 Africa DZ Algeria 44 519 649 2.35% (4.05)% (0.05)%
20 North America HT Haiti 11 520 146 0.88% (4.03)% (0.01)%
21 Africa TN Tunisia 11 923 323 3.66% 3.97% 1.79%
22 South America PE Peru 33 360 257 3.24% 3.91% 2.79%
23 South America EC Ecuador 17 869 505 2.90% 3.77% 1.28%
24 North America HN Honduras 10 037 725 2.61% 3.71% 1.01%
25 South America PY Paraguay 7 206 670 2.68% 3.69% 3.71%
26 Africa ZW Zimbabwe 15 045 847 4.63% (3.27)% (0.04)%
27 Europe MD Moldova 4 025 994 2.66% 3.21% 1.46%
28 Africa NA Namibia 2 579 758 1.79% 2.97% 0.70%
29 Asia ID Indonesia 275 972 350 2.50% 2.94% 0.23%
30 Europe PL Poland 37 811 389 2.62% 2.92% 3.73%
31 Europe GR Greece 10 379 746 2.80% 2.83% 2.82%
32 Africa SN Senegal 17 115 337 3.20% (2.81)% (0.03)%
33 South America CO Colombia 51 342 040 2.61% 2.69% 3.77%
34 North America JM Jamaica 2 972 132 1.51% 2.68% 0.68%
35 Europe UA Ukraine 43 510 196 2.63% 2.65% 2.85%
36 Africa ML Mali 20 741 348 3.13% (2.54)% (0.06)%
37 South America BO Bolivia 11 807 685 2.66% 2.43% 1.26%
38 Asia AM Armenia 2 967 945 2.28% 2.41% 2.56%
39 Asia PK Pakistan 224 512 144 2.39% 2.38% 0.27%
40 Africa KE Kenya 54 769 918 1.85% 2.33% 0.16%
41 North America NI Nicaragua 6 691 251 2.54% (2.33)% (0.01)%
42 Europe CZ Czechia 10 725 848 1.87% 2.31% 2.85%
43 Europe IT Italy 60 386 272 2.36% 2.31% 2.60%
44 North America GT Guatemala 18 195 353 3.09% 2.31% 0.78%
45 North America SV El Salvador 6 513 963 3.25% 2.29% 0.28%
46 Asia TW Taiwan 23 853 373 1.52% (2.08)% (0.00)%
47 Europe HR Croatia 4 083 725 2.57% 2.05% 5.87%
48 Asia IL Israel 9 197 590 0.68% 2.03% 0.16%
49 Africa CM Cameroon 27 102 385 1.75% 1.99% 0.31%
50 Europe AL Albania 2 875 127 1.63% 1.97% 0.61%
51 Africa NE Niger 24 941 425 4.36% 1.93% 0.00%
52 Africa GM Gambia 2 473 779 2.39% 1.93% 0.06%
53 South America UY Uruguay 3 483 946 1.56% 1.92% 10.28%
54 Africa BF Burkina Faso 21 385 551 1.06% 1.87% 0.01%
55 Africa AO Angola 33 725 272 2.36% 1.87% 0.06%
56 Asia KG Kyrgyzstan 6 615 134 1.95% 1.84% 0.51%
57 Africa MG Madagascar 28 290 383 2.27% 1.84% 0.18%
58 Asia IR Iran 84 902 433 1.44% 1.72% 3.03%
59 Africa BW Botswana 2 391 688 2.10% 1.67% 1.12%
60 Asia BD Bangladesh 166 078 786 1.68% 1.61% 0.27%
61 Africa CI Ivory Coast 26 928 434 0.64% 1.61% 0.02%
62 Africa CF Central African R. 4 900 689 2.07% 1.60% 0.11%
63 Asia LB Lebanon 6 799 448 1.70% 1.59% 2.70%
64 South America CL Chile 19 255 219 1.71% 1.58% 4.00%
65 South America AR Argentina 45 547 060 1.68% 1.58% 5.85%
66 Asia JO Jordan 10 288 909 1.22% 1.58% 2.64%
67 Africa CD DR Congo 91 849 784 1.30% 1.58% 0.01%
68 Africa LY Libya 6 950 315 1.95% 1.57% 0.83%
69 Africa GH Ghana 31 620 038 1.18% 1.57% 0.02%
70 Africa ET Ethiopia 117 362 028 1.41% 1.54% 0.14%
71 Asia AZ Azerbaijan 10 216 590 1.81% 1.52% 1.92%
72 Africa MZ Mozambique 31 992 056 1.26% 1.50% 0.02%
73 South America VE Venezuela 28 367 444 1.44% 1.48% 0.49%
74 Europe IE Ireland 4 984 474 1.66% 1.45% 1.01%
75 Asia GE Georgia 3 982 659 1.85% 1.42% 3.46%
76 Africa TD Chad 16 822 255 2.40% 1.41% 0.01%
77 Asia PH Philippines 110 814 711 1.55% 1.39% 0.97%
78 Africa MA Morocco 37 280 793 1.96% 1.25% 0.15%
79 Europe DE Germany 84 011 297 2.77% 1.22% 2.76%
80 Asia SA Saudi Arabia 35 272 085 1.40% 1.19% 0.33%
81 Africa GW Guinea-Bissau 2 007 194 1.71% 1.19% 0.02%
82 Europe LT Lithuania 2 689 901 1.87% 1.19% 5.02%
83 Asia NP Nepal 29 579 317 2.68% 1.18% 1.34%
84 North America US USA 332 648 503 1.75% 1.16% 2.07%
85 Asia JP Japan 126 147 280 1.91% 1.14% 0.42%
86 North America PA Panama 4 372 167 1.62% 1.13% 0.87%
87 Asia LK Sri Lanka 21 489 420 0.84% 1.12% 0.51%
88 North America CR Costa Rica 5 133 520 1.35% 1.11% 3.36%
89 Africa CG Congo 5 632 490 1.07% 1.10% 0.08%
90 Europe AT Austria 9 049 975 1.40% 1.10% 2.74%
91 Africa MR Mauritania 4 752 536 1.64% 1.08% 0.06%
92 Europe BE Belgium 11 632 413 1.27% 1.07% 3.28%
93 North America DO Dominican R. 10 940 026 1.14% 1.07% 0.55%
94 Europe RS Serbia 8 707 441 0.86% 1.06% 3.18%
95 Australia/Oceania PG Papua New Guinea 9 090 082 1.08% 1.03% 0.18%
96 Asia MM Myanmar 54 719 008 2.23% 1.03% 0.00%
97 Africa ZM Zambia 18 817 059 1.18% 1.01% 0.06%
98 Asia OM Oman 5 215 985 0.90% 1.01% 2.60%
99 Africa BJ Benin 12 390 502 1.23% 0.97% 0.02%
100 Asia IN India 1 391 494 040 0.94% 0.97% 2.46%
101 Asia KZ Kazakhstan 18 966 765 0.97% 0.93% 1.54%
102 Europe GB United Kingdom 68 188 452 2.71% 0.90% 0.41%
103 South America PR Puerto Rico 3 193 694 1.42% 0.89% 2.98%
104 Africa UG Uganda 46 954 508 0.73% 0.87% 0.01%
105 North America CU Cuba 11 320 742 0.60% 0.86% 1.11%
106 Europe FR France 65 396 123 1.32% 0.86% 5.01%
107 Africa GN Guinea 13 432 554 0.80% 0.82% 0.06%
108 Europe BY Belarus 9 446 682 0.68% 0.81% 1.51%
109 Asia KH Cambodia 16 914 637 0.85% 0.80% 0.36%
110 Africa SS South Sudan 11 304 789 0.75% 0.78% 0.01%
111 Asia TH Thailand 69 948 647 0.52% 0.75% 0.29%
112 Europe ES Spain 46 770 160 1.51% 0.74% 1.97%
113 Africa RW Rwanda 13 222 636 1.35% 0.72% 0.08%
114 Asia QA Qatar 2 807 805 0.41% 0.69% 3.40%
115 Europe PT Portugal 10 171 433 2.25% 0.67% 0.54%
116 Europe SI Slovenia 2 079 181 1.17% 0.67% 4.45%
117 Europe FI Finland 5 548 028 0.67% 0.64% 0.57%
118 Asia TR Turkey 85 107 359 0.77% 0.63% 6.41%
119 Asia KW Kuwait 4 323 963 0.56% 0.59% 3.84%
120 North America CA Canada 38 022 488 1.27% 0.58% 2.57%
121 Asia KR South Korea 51 306 656 1.29% 0.56% 0.15%
122 Asia IQ Iraq 40 983 371 0.60% 0.53% 2.03%
123 Asia MY Malaysia 32 716 812 0.38% 0.52% 0.95%
124 Africa ER Eritrea 3 587 929 0.38% 0.52% 0.04%
125 Europe NO Norway 5 457 163 0.47% 0.48% 1.11%
126 Asia MN Mongolia 3 322 899 0.43% 0.48% 3.74%
127 Africa GA Gabon 2 269 987 0.56% 0.46% 0.54%
128 Africa TG Togo 8 442 228 0.58% 0.40% 0.08%
129 Europe CH Switzerland 8 708 265 1.02% 0.34% 2.50%
130 Asia UZ Uzbekistan 33 879 305 0.33% 0.34% 0.11%
131 Africa NG Nigeria 210 442 777 0.95% 0.32% 0.00%
132 Europe DK Denmark 5 809 511 1.17% 0.29% 1.55%
133 Europe NL Netherlands 17 167 057 0.75% 0.28% 5.29%
134 Europe SE Sweden 10 152 657 0.73% 0.27% 6.32%
135 Australia/Oceania AU Australia 25 750 285 0.07% 0.20% 0.01%
136 Asia AE Arab Emirates 9 991 520 0.29% 0.15% 2.27%
137 Asia SG Singapore 5 889 331 0.08% 0.13% 0.05%
138 Asia LA Laos 7 364 820 0.00% 0.00% 0.06%
139 Africa BI Burundi 12 188 489 0.13% 0.00% 0.04%
140 Australia/Oceania NZ New Zealand 5 002 100 0.22% 0.00% 0.01%
141 Africa SL Sierra Leone 8 113 840 0.20% 0.00% 0.00%
142 Africa LR Liberia 5 157 851 0.67% 0.00% 0.00%
143 Asia VN Vietnam 98 080 462 0.00% 0.00% 0.00%
144 Asia PS Palestine 5 201 353 0.00% 0.00% 0.04%
145 Africa TZ Tanzania 61 165 222 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 9 718 231 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 7 548 518 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 9 639 499 28 504 2.957
2 Europe CZ Czechia 10 725 848 29 623 2.762
3 Europe BA Bosnia and Herzegovina 3 263 454 8 790 2.693
4 Europe BG Bulgaria 6 903 920 16 886 2.446
5 Europe MK North Macedonia 2 083 301 5 067 2.432
6 Europe SI Slovenia 2 079 181 4 593 2.209
7 Europe SK Slovakia 5 461 888 11 946 2.187
8 Europe BE Belgium 11 632 413 24 483 2.105
9 Europe IT Italy 60 386 272 122 470 2.028
10 South America BR Brazil 213 840 120 416 616 1.948
11 South America PE Peru 33 360 257 63 272 1.897
12 Europe GB United Kingdom 68 188 452 127 598 1.871
13 Europe PL Poland 37 811 389 69 445 1.837
14 Europe HR Croatia 4 083 725 7 388 1.809
15 North America US USA 332 648 503 574 499 1.727
16 Europe ES Spain 46 770 160 78 792 1.685
17 North America MX Mexico 130 079 641 218 491 1.680
18 Europe PT Portugal 10 171 433 16 989 1.670
19 Europe FR France 65 396 123 105 368 1.611
20 Europe RO Romania 19 128 475 28 799 1.506
21 Europe LT Lithuania 2 689 901 4 023 1.496
22 South America CO Colombia 51 342 040 76 468 1.489
23 Europe MD Moldova 4 025 994 5 929 1.473
24 South America AR Argentina 45 547 060 66 474 1.460
25 North America PA Panama 4 372 167 6 255 1.431
26 Asia AM Armenia 2 967 945 4 225 1.423
27 South America CL Chile 19 255 219 27 004 1.402
28 Europe SE Sweden 10 152 657 14 194 1.398
29 Europe CH Switzerland 8 708 265 10 060 1.155
30 Europe AT Austria 9 049 975 10 091 1.115
31 South America BO Bolivia 11 807 685 13 151 1.114
32 Asia LB Lebanon 6 799 448 7 436 1.094
33 Asia GE Georgia 3 982 659 4 263 1.070
34 South America EC Ecuador 17 869 505 19 061 1.067
35 Europe UA Ukraine 43 510 196 46 200 1.062
36 Europe GR Greece 10 379 746 10 910 1.051
37 Europe DE Germany 84 011 297 84 655 1.008
38 Europe NL Netherlands 17 167 057 17 291 1.007
39 Europe IE Ireland 4 984 474 4 921 0.987
40 South America PY Paraguay 7 206 670 6 889 0.956
41 Africa TN Tunisia 11 923 323 11 277 0.946
42 Africa ZA South Africa 59 941 326 54 687 0.912
43 Asia JO Jordan 10 288 909 9 047 0.879
44 Asia IR Iran 84 902 433 74 241 0.874
45 South America UY Uruguay 3 483 946 2 978 0.855
46 Europe AL Albania 2 875 127 2 408 0.838
47 Europe RU Russia 145 987 611 112 622 0.771
48 Europe RS Serbia 8 707 441 6 519 0.749
49 South America PR Puerto Rico 3 193 694 2 348 0.735
50 Asia IL Israel 9 197 590 6 375 0.693
51 North America CR Costa Rica 5 133 520 3 350 0.653
52 North America CA Canada 38 022 488 24 490 0.644
53 North America HN Honduras 10 037 725 5 522 0.550
54 Asia TR Turkey 85 107 359 42 465 0.499
55 Asia AZ Azerbaijan 10 216 590 4 650 0.455
56 Africa LY Libya 6 950 315 3 063 0.441
57 Europe DK Denmark 5 809 511 2 495 0.429
58 North America GT Guatemala 18 195 353 7 695 0.423
59 Asia OM Oman 5 215 985 2 083 0.399
60 Asia IQ Iraq 40 983 371 15 702 0.383
61 Asia KW Kuwait 4 323 963 1 628 0.377
62 North America SV El Salvador 6 513 963 2 146 0.329
63 North America DO Dominican R. 10 940 026 3 517 0.322
64 Africa BW Botswana 2 391 688 734 0.307
65 Europe BY Belarus 9 446 682 2 612 0.277
66 North America JM Jamaica 2 972 132 801 0.270
67 Africa NA Namibia 2 579 758 682 0.264
68 Asia KG Kyrgyzstan 6 615 134 1 649 0.249
69 Africa MA Morocco 37 280 793 9 057 0.243
70 Asia KZ Kazakhstan 18 966 765 4 542 0.239
71 Asia SA Saudi Arabia 35 272 085 7 045 0.200
72 Asia QA Qatar 2 807 805 496 0.177
73 Asia ID Indonesia 275 972 350 46 663 0.169
74 Asia IN India 1 391 494 040 234 088 0.168
75 Europe FI Finland 5 548 028 922 0.166
76 Asia PH Philippines 110 814 711 18 099 0.163
77 Asia AE Arab Emirates 9 991 520 1 607 0.161
78 Africa LS Lesotho 2 156 639 319 0.148
79 Europe NO Norway 5 457 163 767 0.141
80 Africa EG Egypt 103 963 661 13 779 0.133
81 Asia NP Nepal 29 579 317 3 579 0.121
82 Africa ZW Zimbabwe 15 045 847 1 576 0.105
83 Africa MR Mauritania 4 752 536 456 0.096
84 Asia SY Syria 17 858 790 1 639 0.092
85 Asia JP Japan 126 147 280 10 589 0.084
86 Asia PK Pakistan 224 512 144 18 657 0.083
87 South America VE Venezuela 28 367 444 2 245 0.079
88 Africa DZ Algeria 44 519 649 3 315 0.074
89 Asia BD Bangladesh 166 078 786 11 833 0.071
90 Africa GM Gambia 2 473 779 175 0.071
91 Asia AF Afghanistan 39 667 381 2 683 0.068
92 Africa ZM Zambia 18 817 059 1 256 0.067
93 Africa SN Senegal 17 115 337 1 116 0.065
94 North America CU Cuba 11 320 742 713 0.063
95 Africa GA Gabon 2 269 987 142 0.063
96 Africa MW Malawi 19 546 127 1 152 0.059
97 Asia MM Myanmar 54 719 008 3 210 0.059
98 Africa SD Sudan 44 712 026 2 365 0.053
99 Africa KE Kenya 54 769 918 2 865 0.052
100 Asia MY Malaysia 32 716 812 1 632 0.050
101 Africa SO Somalia 16 266 237 745 0.046
102 Asia MN Mongolia 3 322 899 149 0.045
103 Africa CM Cameroon 27 102 385 1 144 0.042
104 Asia YE Yemen 30 379 807 1 270 0.042
105 Asia KR South Korea 51 306 656 1 865 0.036
106 Asia LK Sri Lanka 21 489 420 764 0.036
107 Australia/Oceania AU Australia 25 750 285 910 0.035
108 Africa GW Guinea-Bissau 2 007 194 67 0.033
109 Africa ET Ethiopia 117 362 028 3 840 0.033
110 North America NI Nicaragua 6 691 251 183 0.027
111 Africa CG Congo 5 632 490 148 0.026
112 Africa MZ Mozambique 31 992 056 820 0.026
113 Africa RW Rwanda 13 222 636 338 0.026
114 Africa GH Ghana 31 620 038 783 0.025
115 Africa MG Madagascar 28 290 383 701 0.025
116 Africa ML Mali 20 741 348 497 0.024
117 North America HT Haiti 11 520 146 263 0.023
118 Asia UZ Uzbekistan 33 879 305 662 0.020
119 Africa CF Central African R. 4 900 689 93 0.019
120 Africa AO Angola 33 725 272 628 0.019
121 Africa LR Liberia 5 157 851 85 0.017
122 Africa TG Togo 8 442 228 124 0.015
123 Australia/Oceania PG Papua New Guinea 9 090 082 121 0.013
124 Africa GN Guinea 13 432 554 149 0.011
125 Africa CI Ivory Coast 26 928 434 291 0.011
126 Africa SS South Sudan 11 304 789 116 0.010
127 Africa TD Chad 16 822 255 170 0.010
128 Africa NG Nigeria 210 442 777 2 065 0.010
129 Africa SL Sierra Leone 8 113 840 79 0.010
130 Asia TJ Tajikistan 9 718 231 90 0.009
131 Africa CD DR Congo 91 849 784 772 0.008
132 Africa BJ Benin 12 390 502 100 0.008
133 Africa NE Niger 24 941 425 192 0.008
134 Africa BF Burkina Faso 21 385 551 161 0.007
135 Africa UG Uganda 46 954 508 346 0.007
136 Asia KH Cambodia 16 914 637 114 0.007
137 Asia TH Thailand 69 948 647 382 0.005
138 Asia SG Singapore 5 889 331 31 0.005
139 Australia/Oceania NZ New Zealand 5 002 100 26 0.005
140 Africa ER Eritrea 3 587 929 12 0.003
141 Asia PS Palestine 5 201 353 12 0.002
142 Africa BI Burundi 12 188 489 6 0.001
143 Asia TW Taiwan 23 853 373 12 0.001
144 Asia VN Vietnam 98 080 462 35 0.000
145 Africa TZ Tanzania 61 165 222 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 7 364 820 0 0.000
150 Asia HK Hong Kong 7 548 518 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"