Comparative Mortality Rates from Coronavirus by Nation

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)

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Christopher A. Lawrence
Christopher A. Lawrence

Christopher A. Lawrence is a professional historian and military analyst. He is the Executive Director and President of The Dupuy Institute, an organization dedicated to scholarly research and objective analysis of historical data related to armed conflict and the resolution of armed conflict. The Dupuy Institute provides independent, historically-based analyses of lessons learned from modern military experience.
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Mr. Lawrence was the program manager for the Ardennes Campaign Simulation Data Base, the Kursk Data Base, the Modern Insurgency Spread Sheets and for a number of other smaller combat data bases. He has participated in casualty estimation studies (including estimates for Bosnia and Iraq) and studies of air campaign modeling, enemy prisoner of war capture rates, medium weight armor, urban warfare, situational awareness, counterinsurgency and other subjects for the U.S. Army, the Defense Department, the Joint Staff and the U.S. Air Force. He has also directed a number of studies related to the military impact of banning antipersonnel mines for the Joint Staff, Los Alamos National Laboratories and the Vietnam Veterans of American Foundation.
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His published works include papers and monographs for the Congressional Office of Technology Assessment and the Vietnam Veterans of American Foundation, in addition to over 40 articles written for limited-distribution newsletters and over 60 analytical reports prepared for the Defense Department. He is the author of Kursk: The Battle of Prokhorovka (Aberdeen Books, Sheridan, CO., 2015), America’s Modern Wars: Understanding Iraq, Afghanistan and Vietnam (Casemate Publishers, Philadelphia & Oxford, 2015), War by Numbers: Understanding Conventional Combat (Potomac Books, Lincoln, NE., 2017) , The Battle of Prokhorovka (Stackpole Books, Guilford, CT., 2019), The Battle for Kyiv (Frontline Books, Yorkshire, UK, 2023), Aces at Kursk (Air World, Yorkshire, UK, 2024), Hunting Falcon: The Story of WWI German Ace Hans-Joachim Buddecke (Air World, Yorkshire, UK, 2024) and The Siege of Mariupol (Frontline Books, Yorkshire, UK, 2024).
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Mr. Lawrence lives in northern Virginia, near Washington, D.C., with his wife and son.

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6 Comments

  1. 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!

  2. 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

  3. 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.

  4. 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.

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