Split off the material on mortality rates into a separate blog post. As is already known, countries do not report or catch every case of Coronavirus. It appears in the case of the United States, the actual number of cases is 3 to 4 times higher than what is reported. For other countries (like Russia), the disparity is even higher. Mortality rates might be a more useful metric for measuring differences between counties, but even these are not consistently reported. On the other hand, they can be checked by doing a comparison of “excess deaths” in 2020 and 2021 compared to previous years. So far I have checked excess deaths and blogged about it for two countries, the United States, which was close to the figure for reported mortality figures; and for Russia, which has an excess death figure way higher than their reported mortality figures. The suspicion is that Russia has been covering up or discouraging reporting of deaths from coronavirus. Anyhow, knowing this is not perfect or consistently reported data, here is a comparative mortality rates between various countries.
First I list the “top ten.” I may have missed a smaller country that I have not been watching. I then list a couple of other European countries. Then I list Canada, Australia and New Zealand. Next is the listing of the four Scandinavian countries. This comparison is interesting as Sweden took a different approach (no lockdown) compared to the other three. Finally, I list some selected East Asian countries. Mortality is calculate as the number reported killed per million population. The higher the figure, they worst they are doing.
Morality rate (people killed per million population) by country:
Country Population Deaths Rate
San Marino 0.034 90 2,647
Brazil 213.4 525,892 2,464
Belgium 11.6 25,194 2,172
Italy 59.2 127,952 2,161
Colombia 51.0 110,019 2,157
Argentina 45.8 96,983 2,118
UK 67.1 128,532 1,915
Mexico 126.0 233,958 1,857
U.S. 332.0 605,944 1,825
Spain 47.4 80,952 1,708
Russia: 146.2
reported: 137,718 942
excess: 460,000 3,146
France 67.4 111,426 1,653
Germany 83.2 91,122 1,095
Canada 38.3 26,344 688
Australia 25.8 910 35
New Zealand 5.1 26 5
Sweden 10.4 14,639 1,408
Denmark 5.8 2,539 438
Norway 5.4 796 147
Finland 5.5 976 177
China 1,411.8 4,848 3
S. Korea 51.7 2,033 39
Japan 126.2 14,847 118
Taiwan 23.5 715 30
Vietnam 97.6 97 1
Singapore 5.7 36 6
This does seem to be the most useful measure of response to the Coronavirus. As can be seen, some countries have done a much better job than others. This does not seem to be tied to wealth. It does seem to be related to leadership, or lack thereof.
Excess deaths blog posts:
Excess Deaths and Coronavirus | Mystics & Statistics (dupuyinstitute.org)
Excess Mortality in Russia | Mystics & Statistics (dupuyinstitute.org)
Chris, how do the countries stack if controlling for level of international entries during the early days of the pandemic? How about if controlling for early restrictions on international entries (a factor related to your emphasis on leadership)?
Did a country such as New Zealand fare fairly well because of fewer entries (being a rather isolated pair of islands) as the norm or because of restricting entries once the pandemic manifested outside of New Zealand?
Did a country such as Canada, which normally does get a lot of visitors (including businessmen from China, especially to British Columbia) fare fairly well because of early responses to the pandemic? Because of greater swaths of rural regions (i.e. bands of low population-density regions) making it easier to restrict travel between scattered urban centers once the pandemic manifested? One would think that Russia would have that advantage as well, not that one can trust Russian figures; however, surplus death statistics might indicate that Russia didn’t fare well (as you mentioned). How did African countries with swaths of low population-density regions fare relative to more densely and centrally populated countries in Africa?
Did countries in Africa have later experience with the pandemic because of greater isolation? Although there are a lot of Chinese officials attempting to have influence in Africa, even that doesn’t result in a lot of human interaction between Chinese and Africans; so, isolation could be an advantage in Africa.
After accounting for international isolation and intranational isolation for each country, did government decisions make much of a difference? One would think so! Inquiring minds want to know.
By the way, one of my nephews had migrated to Finland as a chef (a couple of years prior to the pandemic) and was able to facilitate his teenage children migrating from Mississippi to Finland during the middle of the pandemic with the help of my sister and some U.S. Government contacts in spite of some severe travel restrictions; so, there are loop holes through government restrictions. Even when countries attempt to restrict travel, isolation is permeable!
Try applying the mortality rates from COVID-19 to the following stack of countries:
International tourism, number of arrivals – Country Ranking
Definition: International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead. Sources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival.
Source: World Tourism Organization, Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files.
Rank Country Value Year
1 France 86,861,000.00 2017
2 Spain 81,786,000.00 2017
3 United States 76,941,000.00 2017
4 China 60,740,000.00 2017
5 Italy 58,253,000.00 2017
6 Mexico 39,291,000.00 2017
7 United Kingdom 37,651,000.00 2017
8 Turkey 37,601,000.00 2017
9 Germany 37,452,000.00 2017
10 Thailand 35,592,000.00 2017
11 Austria 29,460,000.00 2017
12 Japan 28,691,000.00 2017
13 Hong Kong SAR, China 27,884,000.00 2017
14 Greece 27,194,000.00 2017
15 Malaysia 25,948,000.00 2017
16 Russia 24,390,000.00 2017
17 Canada 20,798,000.00 2017
18 Poland 18,258,000.00 2017
19 Netherlands 17,924,000.00 2017
20 Macao SAR, China 17,255,000.00 2017
21 Saudi Arabia 16,109,000.00 2017
22 Croatia 15,593,000.00 2017
23 India 15,543,000.00 2017
24 Portugal 15,432,000.00 2017
25 Ukraine 14,230,000.00 2017
26 Indonesia 14,040,000.00 2017
27 Singapore 13,903,000.00 2017
28 Korea 13,336,000.00 2017
29 Vietnam 12,922,000.00 2017
30 Denmark 11,743,000.00 2017
31 Bahrain 11,370,000.00 2017
32 Morocco 11,349,000.00 2017
33 Belarus 11,060,200.00 2017
34 Romania 10,926,000.00 2017
35 Ireland 10,338,000.00 2017
36 South Africa 10,285,000.00 2017
37 Czech Republic 10,160,000.00 2017
38 Switzerland 9,889,000.00 2017
39 Bulgaria 8,883,000.00 2017
40 Australia 8,815,000.00 2017
41 Belgium 8,385,000.00 2017
42 Egypt 8,157,000.00 2017
43 Kazakhstan 7,701,000.00 2017
44 United Arab Emirates 7,126,000.00 2005
45 Sweden 7,054,000.00 2017
46 Tunisia 7,052,000.00 2017
47 Argentina 6,720,000.00 2017
48 Philippines 6,621,000.00 2017
49 Brazil 6,589,000.00 2017
50 Georgia 6,483,000.00 2017
51 Chile 6,450,000.00 2017
52 Norway 6,252,000.00 2017
53 Dominican Republic 6,188,000.00 2017
54 Hungary 5,650,000.00 2017
55 Cambodia 5,602,000.00 2017
56 Syrian Arab Republic 5,070,000.00 2011
57 Iran 4,867,000.00 2017
58 Albania 4,643,000.00 2017
59 Cuba 4,594,000.00 2017
60 Kyrgyz Republic 4,568,000.00 2017
61 Colombia 4,113,000.00 2017
62 Peru 4,032,000.00 2017
63 Jordan 3,843,500.00 2017
64 Puerto Rico 3,797,000.00 2017
65 Uruguay 3,674,000.00 2017
66 Cyprus 3,652,000.00 2017
67 Israel 3,613,000.00 2017
68 Slovenia 3,586,000.00 2017
69 New Zealand 3,555,000.00 2017
70 Myanmar 3,443,000.00 2017
71 Lao PDR 3,257,000.00 2017
72 Estonia 3,245,000.00 2017
73 Finland 3,180,000.00 2017
74 Costa Rica 2,960,000.00 2017
75 Andorra 2,831,000.00 2016
76 Uzbekistan 2,690,000.00 2017
77 Lithuania 2,523,000.00 2017
78 Azerbaijan 2,454,000.00 2017
79 Algeria 2,451,000.00 2017
80 Zimbabwe 2,423,000.00 2017
81 Oman 2,372,000.00 2017
82 Jamaica 2,353,000.00 2017
83 Malta 2,274,000.00 2017
84 Qatar 2,256,500.00 2017
85 Iceland 2,225,000.00 2017
86 Slovak Republic 2,162,000.00 2017
87 Sri Lanka 2,116,400.00 2017
88 Guatemala 2,113,000.00 2017
89 Latvia 1,949,000.00 2017
90 Nigeria 1,889,000.00 2016
91 Montenegro 1,877,000.00 2017
92 Lebanon 1,857,000.00 2017
93 Panama 1,843,000.00 2017
94 Côte d’Ivoire 1,800,000.00 2017
95 Nicaragua 1,787,000.00 2017
96 Ecuador 1,608,000.00 2017
97 Paraguay 1,584,000.00 2017
98 Botswana 1,574,000.00 2016
99 El Salvador 1,556,000.00 2017
100 Namibia 1,499,000.00 2017
101 Serbia 1,497,000.00 2017
102 Armenia 1,495,000.00 2017
103 Mozambique 1,447,000.00 2017
104 The Bahamas 1,439,000.00 2017
105 Uganda 1,402,000.00 2017
106 Senegal 1,365,000.00 2017
107 Kenya 1,364,000.00 2017
108 Mauritius 1,342,000.00 2017
109 Tanzania 1,275,000.00 2017
110 Lesotho 1,137,000.00 2017
111 Bolivia 1,134,000.00 2017
112 Zambia 1,083,000.00 2017
113 Luxembourg 1,046,000.00 2017
114 Cameroon 994,000.00 2016
115 Pakistan 966,000.00 2012
116 Nepal 940,000.00 2017
117 Ethiopia 933,000.00 2017
118 Rwanda 932,000.00 2016
119 Bosnia and Herzegovina 923,000.00 2017
120 Eswatini 921,000.00 2017
121 Ghana 897,000.00 2015
122 Iraq 892,000.00 2013
123 Honduras 851,000.00 2017
124 Fiji 843,000.00 2017
125 Malawi 837,000.00 2017
126 Sudan 813,000.00 2017
127 Cabo Verde 668,000.00 2017
128 Barbados 664,000.00 2017
129 North Macedonia 631,000.00 2017
130 Togo 496,000.00 2017
131 Mongolia 469,000.00 2017
132 Haiti 467,000.00 2017
133 Tajikistan 431,000.00 2017
134 Venezuela 427,000.00 2017
134 Belize 427,000.00 2017
136 Cayman Islands 418,000.00 2017
137 Trinidad and Tobago 395,000.00 2017
138 St. Lucia 386,000.00 2017
139 Yemen 366,700.00 2015
140 Monaco 355,000.00 2017
141 Dem. Rep. Congo 351,000.00 2016
142 Seychelles 350,000.00 2017
143 Kuwait 307,000.00 2013
144 Burundi 299,000.00 2017
145 Benin 281,000.00 2017
146 Suriname 278,000.00 2017
147 Gabon 269,000.00 2005
148 Angola 261,000.00 2017
149 Brunei 259,000.00 2017
150 Bhutan 255,000.00 2017
150 Madagascar 255,000.00 2017
152 Guyana 247,000.00 2017
152 Antigua and Barbuda 247,000.00 2017
154 Congo 206,000.00 2017
155 Mali 193,300.00 2017
156 Papua New Guinea 179,000.00 2016
157 Grenada 168,000.00 2017
158 Niger 164,000.00 2017
159 The Gambia 162,000.00 2017
160 Samoa 146,000.00 2017
161 Moldova 145,000.00 2017
162 Burkina Faso 143,000.00 2017
163 Eritrea 142,000.00 2016
164 Bangladesh 125,000.00 2014
165 Palau 123,000.00 2017
166 St. Kitts and Nevis 122,000.00 2015
167 New Caledonia 121,000.00 2017
168 Central African Republic 120,500.00 2015
169 Vanuatu 109,000.00 2017
170 Chad 87,000.00 2017
171 San Marino 78,000.00 2017
172 St. Vincent and the Grenadines 76,000.00 2017
173 Timor-Leste 74,000.00 2017
174 Dominica 72,000.00 2017
175 Liechtenstein 69,000.00 2017
176 Djibouti 63,000.00 2013
177 Tonga 62,500.00 2017
178 Guinea 60,000.00 2016
179 Sierra Leone 55,000.00 2016
180 Guinea-Bissau 43,800.00 2015
181 Libya 34,000.00 2008
182 Mauritania 30,000.00 2000
183 São Tomé and Principe 29,000.00 2016
184 Comoros 28,000.00 2017
185 Solomon Islands 25,700.00 2017
186 Turkmenistan 8,200.00 2007
187 Kiribati 5,800.00 2017
188 Tuvalu 2,500.00 2017
CDC’s listing of hot spots doesn’t quite fit with my hypothesis:
https://www.cdc.gov/coronavirus/2019-ncov/travelers/map-and-travel-notices.html
Here’s Bloomberg’s take on the case of countries being resilient (one of those indices that mask what is being measured):
https://www.bloomberg.com/graphics/covid-resilience-ranking/
The impact of the Great Influenza was associated with genetics. I suspect that the mortality rate from COVID has some correlation with genetics too.
I believe it is too early to judge the leadership of the countries according to the mortality rate.
Look at the comparison of Sweden to Norway, Denmark and Finland.
Ranked by tourist visits:
Denmark, 30th
Norway, 45th
Sweden, 52nd
Finland (with Mongol/Uralic DNA), 73rd
Ranked by mortality rate of COVID-19:
Sweden, 0.1408% of population died because of COVID-19
Denmark, 0.0438% of population died because of COVID-19
Finland (with Mongol/Uralic DNA), 0.0177% died because of COVID-19
Norway, 0.0147% of population died because of COVID-19
So, the case of Scandinavia fits better with the travel hypothesis than with the DNA hypothesis (if differences between Scandinavian DNA has relevant differences from Mongol/Uralic-Scandinavian DNA when it comes to susceptibility to dying from COVID-19). It does appear that Sweden jumped to the top because of not locking down. Did Sweden also refrain from locking out travel? If so then the jump could be partially attributed to Denmark and Norway relinquishing their pre-pandemic leads in tourist visits over Sweden.