This article started with a simple graphic that I posted on Facebook for people to comment on.
I got the idea from a post on Linkedin that compared Sweden’s deaths with those in the US and it was really surprising, based on the constant media denigration of Sweden and their modified lockdown strategy. As the data shows above, despite not locking down their under 65 population, Sweden has to date had very few deaths under age 65. Less so than regions where lockdowns of the under65 populations were intense (and in Arizona’s case, happened twice). This comparison also made sense due to population size similarity between the regions (Arizona is about 7.3M, Sweden about 10.2M, this part of NYC is about 8.4M). Another interesting datapoint on Sweden’s unique management of the COVID outbreak is pasted below. Around early July the case rate adjusted sharply and now new case growth is a very small number per day. This is interesting that the case growth slowed so quickly, especially in light of their strategy to not close schools, restaurants, etc.
One of the persistent questions about this comparison was whether it had merit since NYC is much more dense than Sweden and Arizona (I assume that’s true, but haven’t looked at the numbers). So since NYC is more dense, it makes some intuitive sense to us that the density factor may account for a greater number of deaths. Does it?
Correlations of different societal and geographical factors with COVID-19 Cases and Deaths has been one large area of interest of mine through this outbreak. I have reported on this in this blog multiple times as the outbreak has spread. In the past, I observed that population density is slightly correlated with case count across the globe but is basically uncorrelated with deaths. Does this still hold today now that the virus has spread to new places?
Correlation of Various Factors with Normalized COVID-19 Death Count
Note that the factor most positively correlated with Deaths in a region is the number of Cases in the region normalized by the population. This is followed closely by the Instantaneous Rate of Change of Cases (the slope of Case Growth). You would expect this to be the case, but it’s a bit surprising to see that the number of cases in a region is only just over twice as correlated with deaths as the Body Mass Index mean for males! This would also indicate that there are regions where the BMI of the population has had more of an impact on deaths as the case count in the region. As evidence that high case count does not always lead to high deaths (and conversely that lower case counts can lead to high deaths, see the chart of Arizona counties, where we have results all over the board. The counties with the highest death rates are generally the ones with lowest population density and highest pre-existing morbidities. Some counties (cities) have very high case counts and low to moderate deaths. Other counties have low case counts and high deaths. It’s all over the map.
Arizona COVID-19 Stats by County
I did the original assessment to compare what has happened in Sweden vs. other regions largely because of the negative media attention that Sweden has received from their COVID-19 lockdown strategy. As it turns out, for populations under 65 (the ones who were actually not on lockdown) there has been very few deaths (but lots of cases). This is surprising considering that in Arizona and NYC, government interventions such as lockdowns, closing businesses, and mandatory face masks have been credited with slowing the growth of the outbreak. There are many surprising things I’ve noticed through this time of COVID. I point out a few others in this post regarding the unintuitive role population density plays in COVID-19 deaths as well as the observation that the correlation of COVID deaths with high COVID case counts is much smaller than we would have guessed (I would have suspected 90% or higher correlation).
Overall what does this show us? Our intuition is not necessarily to be trusted and should be assessed more critically using data rather than prior beliefs. The same applies to media reports, which tend to only show data in support of a pre-existing narrative.