When we look at the cumulative confirmed cases and fatality data for close to two hundred countries, it seems quite remarkable that some countries seem to do 10 times better than other countries in terms of case fatality rate.  We might conclude that the novel coronavirus discriminates by country or that certain countries manage their pandemics much better than others.  The truth is more complicated. 

When examining mortality rates two issues are important: how we count deaths and how we test patients.  The case fatality rate is a straightforward statistic calculated by dividing number of confirmed deaths by the number of confirmed cases for any group or subgroup of patients.  For an ongoing pandemic, this statistic is almost always understated.  Firstly, because it generally takes several days for a confirmed case to resolve itself into death or a cure, so the denominator using the current case count is overstated.  Secondly, many deaths occur outside the healthcare system and although the patient was never tested positive for coronavirus they often show many signs that they probably died of COVID-19.  The cause of death assignment is often subjective, especially when there are comorbidities involved.  The most probable ones should be counted in both the numerator and denominator to determine the true COVID-19 fatality rate.

The other major problem is that the total number of infectees is rarely known while the pandemic is raging since we are most likely testing only a subpopulation of all infectees. Often the testing protocol stipulates that we test only the most seriously ill and most at-risk.  This protocol is actually not the most efficient way to test the population given limited resources because you wind up testing patients that are most likely to test positive anyway and miss a large number of asymptomatic and mildly symptomatic cases, vastly underestimating the denominator of the fatality rate.  This is one reason it is so important to test early and widely to get an accurate picture of how the infection is spreading within the general population.  The other major impetus for wide testing is, of course, to ascertain that every infectee is identified and isolated as soon as possible and all their potential contacts tracked and isolated as well to contain the spread of the disease.  Once we are able to do this we can more confidently calculate the COVID-19 fatality rate as the total confirmed and probable deaths due to COVID-19 divided by the total number of deaths (confirmed and probable) plus cures (including all those infectees that never showed any symptoms).  While the pandemic is raging we can only estimate this number. 

We need to do this so we can identify those segments of the population that are most at risk early.  The table below lists the top countries in the world in terms of confirmed cases and fatalities plus a few countries that have been identified as doing well in terms of managing their pandemics.  We had already examined the age and gender effect in Spain and Italy and found them to be very similar in form with mortality doubling every 7yrs in the range 35-75yrs old.  Both countries tested nearly 2.9% of their population (as of April 27) which seems like a reasonable amount but since both countries were highly infected 11% to 17% of the tests returned positive.  When a high percentage of tests return positive results it means that we are only testing those that are most suspect and not really testing the full population adequately.

South Korea is now held up as the best practice example in early and widespread testing and contact tracing.  While they have only tested 1.2% of their population, they tested to the point where only 2% of their tests returned positive.  Interestingly enough when we look at their age-dependent data, we see that they follow a steeper exponential form (mortality doubling every 5.9 yrs)  that is lower overall due to more complete sampling of the full population.  The steepening we believe is due to the fact that most countries test their at-risk (older male) population better than they do the younger population which tends to have milder and sometimes asymptomatic cases.  When we look at Germany which is often considered the best practice country in Europe, we see a very similar graph to that of South Korea.  Germany has tested more than South Korea (2.5% vs 1.2% of their population, respectively) but not as thoroughly as South Korea with 8% of their tests returning positive results. 

USA testing completeness and efficiency is very poor and needs to improve by a factor of 2.5 to match Germany’s efficiency.  Testing kits should not be distributed randomly to achieve the same percentage of the population tested, but rather more to those regions that are hotter than others and returning more than 10% positive results.  States like NJ that continue to return near 50% positive test results must increase their testing by more than 5 fold to achieve a more accurate measure of their infection.  If the President wants a lower fatality rate for the USA, he simply needs to improve testing.

Because age is such a strong effect, we see that the apparent difference in fatality rates between Germany (3.8%) and South Korea (2.3%) may be entirely explained by the older population of Germany (47.1 vs 41.8 yrs).  The difference between South Korea and New Zealand could also be explained by age distribution differences in their population.  As testing becomes more complete and efficient in the seven countries with double-digit percentage fatalities, we predict that their fatality rates will decline by more than a factor of 2.  The fatality rate differential now seen among all countries will narrow significantly and we may then search for other comorbidity risk and structural factors that could better explain any residual differences in how each country is impacted by the COVID-19 pandemic. 

Median AgeTests
% Test Positive
Spain       4,905   229,42223,52110.3%   502.942.7 28,77917%
Belgium       4,028     46,687  7,20715.4%   621.841.9 18,46822%
Switzerland       3,409     29,164  1,6655.7%   194.642.4 28,34312%
Italy       3,289  199,41426,97713.5%   445.045.5 29,60011%
USA       3,0591,010,35656,7975.6%   172.038.1 17,21118%
France       2,546  165,84222,29313.4%   342.341.4   7,10336%
Netherlands       2,232    38,245  4,51811.8%   264.041.2 11,31920%
UK       2,319  157,14921,09213.4%   311.240.5 10,60522%
Germany       1,890  158,213  6,0213.8%      71.947.1 24,7388%
Sweden       1,877    18,926  2,27412.0%   225.541.2   9,35720%
Norway       1,396      7,554     2052.7%      37.939.2 30,3105%
S. Korea          208    10,738     2432.3%        4.741.8 11,8692%
Taiwan            18          429          61.4%        0.240.7   2,5901%
N. Zealand          299      1,470       181.2%        3.737.9 26,1431%
Australia          264      6,720        831.2%        3.338.7 20,2771%