Areas which voted for Trump are increasingly seeing the highest growth in COVID-19 deaths
A lot of recent articles have discussed the increasing prevalence of COVID-19 cases in areas which voted for Donald Trump in 2016. William H. Frey of the Brookings Institution has done this particularly well (https://www.brookings.edu/blog/the-avenue/2020/05/07/as-states-reopen-covid-19-is-spreading-into-even-more-trump-counties/), showing unequivocally that the vast majority of recently-designated “high-prevalence” counties went for Trump in 2016.
As part of a separate project I’m working on, I accessed the COVID-19 data from the Johns Hopkins University Center for Systems Science and Engineering (available at https://github.com/CSSEGISandData/COVID-19. Some cleaning necessary). Among other things, these data report the number of confirmed COVID-19 cases and deaths by county by day. While it’s interesting to look at the number of confirmed cases (which is its own measure of local impact and is somewhat predictive of deaths), there are many well-reported reasons to question how accurately confirmed case levels reflect actual case levels (e.g. due to differences in test reliability and availability). While some of these issues remain true for measuring COVID-19 deaths, disparities in data quality across jurisdictions are less-likely—particularly when comparing jurisdictions within the same country, such as US counties.
Below, I present two dynamic maps covering March 1 - May 15, 2020. The first shows the total number of COVID-19 deaths in each county, by day. To ensure a good sense of the numbers in each county using only six categories, the dots represent (from smallest to largest): 1 - 2; 2 - 10; 10 - 100; 100 - 1,000; 1,000 - 10,000; and 10,000 - 30,000 county-level deaths.
Total COVID-19 deaths, by county. Data sourced from https://github.com/CSSEGISandData/COVID-19.
This is useful for understanding which places have been most affected by overall COVID-19 deaths through May 15. However, it doesn’t necessarily make clear which places have seen the most recent growth in deaths. To show this, in the second map I plot, for each county and day, the percentage increase in deaths from a week previous. The intensity of the heatmap then reflects the size of this percentage increase relative to that in all other counties on that day. In the first week of April New York City presents much more brightly than anywhere else, emphasizing how much more rapidly the death rate rose there compared to the rest of the country. In the weeks since, however, it has largely and increasingly been counties in the South and Midwest that have seen the greatest growth in COVID-19 deaths.
Relative growth in COVID-19 deaths compared to previous week, by county. Data sourced from https://github.com/CSSEGISandData/COVID-19.
Taken together, these maps illustrate the same story as the data on COVID-19 cases, but with more fatal consequences: many of those areas which helped boost the President to victory in 2016 are among the most active COVID-19 hotspots in the country. It remains to be seen what impact this might have on the November elections, but with continued disproportionate growth in cases in Trump states and counties as states begin opening back up (again, see the Frey piece), it seems increasingly likely that there will be fewer people casting ballots in those areas.