Many people are wondering about variation in the Covid-19 pandemic across countries. Why (the North of) Italy and not Portugal. How was South Korea able to contain the virus, but other countries did not even though they had less time. The New York Times published a long article that examined this question, but nobody really knows.
Some of the speculations focus on biological factors that may protect individuals or may make them more vulnerable. However, so far these factors explain a small portion of the variation in death rates. The biggest predictor is the number of people who are infected by the virus. Australia and New Zealand have few deaths because Covid-19 did not spread widely among their populations.
One possible explanation could be the response of countries to the pandemic. Countries like the UK and Sweden may have more deaths because they did not lock down their countries. The problem with these speculations is that many factors are likely to contribute to the variation and it is difficult to spot these factors without statistical analyses.
The NYT article mentions that hundreds of studies are underway to look for predictors of variation across nations, but no results are being mentioned. Maybe researchers are cautious.
“Doctors who study infectious diseases around the world say they do not have enough data yet to get a full epidemiological picture, and that gaps in information in many countries make it dangerous to draw conclusions”
Drawing conclusions is different from exploring data. There is nothing dangerous about exploring patterns in data. Clearly many people are curious and statistical analysis can provide more valuable information than armchair speculations about climate or culture.
As a cross-cultural psychologists, I am familiar with many variables that distinguish nations from each other. The most prominent dimension is individualism. Western cultures tend to be more individualistic than Asian cultures. This might suggest that culture plays a role because Asian cultures have had fewer Covid-19 deaths. However, individualism as measured by Hofstede’s dimension is a weak predictor and did not survive statistical controls. Other dimensions that were less plausible also did not predict variation in Covid-19 deaths.
However, one variable that was a predictor was the number of tourists that travel to a country (tourism data).
Tourism reflects how connected a country is with the rest of the world. Australia and New Zealand are not only islands, they are also geographically isolated which explains why relatively few people visit these otherwise attractive locations. Covid-19 also has speared much of Eastern Europe and many Eastern European countries rank low on the tourism index.
Additional analysis show that tourism is becoming a weaker predictor over time. The reason is the recent rise of cases and deaths in Latin America. Latin America was relatively unaffected in April, but lately Ecuador and Brazil have seen alarming increases in cases.
The graph also shows that tourism does not explain all of the differences between countries. For example, the UK has way more cases than predicted by the regression line. This may reflect the slow response to the Covid-19 crises in the UK. Sweden is also above the regression line, possibly due to the policy to keep schools and businesses. Switzerland is a direct neighbor of the North of Italy, where the epidemic in Europe started. Canada is above the regression line, but was on the regression line on April 15. The reason is that Canada acted quickly in the beginning, but is now seeing a late increase in death in care homes.
In conclusion, these results suggest that timing is a big factor in the current differences across countries. Countries with high death tolls were simply unlucky to be at the center of the pandemic or well connected to it. As the pandemic progresses, this factor will become less important. Some countries, like Austria and (the South of) Germany that were hit early have been able to contain the spread of Covid-19. In other countries, numbers are increasing, but no country is seeing increases as dramatic as in Italy (or New York) where Covid-19 spread before social distancing measures were in place. New factors may predict what will happen in the times of the “new normal” when countries are trying to come out of lock-downs.
I don’t think that publishing these results is dangerous. The results are what they are. It is just important to realize that they do not prove that tourism is the real causal factor. It is also possible that tourism is correlated with some other variables that reflect the real cause. To demonstrate this, we need to find measures of these causal factors and demonstrate that they predict variation in death tolls of nations better than tourism and statistically remove the relationship of tourism with Covid-19 deaths. So, this blog post should be seen as a piece of a puzzle rather than the ultimate answer to a riddle.