The spread of the COVID-19 virus is highly variable among U.S. counties. Seventeen factors known or thought to be related to spread of the COVID-19 virus were studied by Poisson regression analysis of confirmed cases and deaths in 883 U.S. counties with a population of 50,000 or more as of May 31, 2020. With little exception, each factor was predictive of incidence and mortality.

 

Most of the correlations are as expected but the lower numbers of cases in counties with a larger percentage of Hispanics in the population is contrary to claims that Hispanics work disproportionately in industries where the virus spread rapidly. 

Similarly, the finding that the cases and deaths are more frequent in counties with higher median incomes may seem counter to the evidence that people with lower incomes are more likely to be exposed to the virus. Again the correlation prevails after controlling for other factors such as unemployment. Recall that one of the early hotspots was Westchester County New York, one of the wealthiest counties in the U.S. It is likely that air travel and business meetings increased risk among more affluent people.

 

The regression equation can be used to identify priority locations for preventive efforts and preparation for medical care caseloads when prevention is unsuccessful. Based on the correlation of cases and deaths to days since stay-at-home orders were issued, the orders reduced the cases about 48 percent  and deaths about 50 percent. The shutdowns probably prevented about 1.4 million cases and 92,000 deaths. The study that claimed the warnings and shutdowns prevented 60 million COVID-19 cases in the U.S. appears to be grossly inflated by a factor of 40. Focusing preventive efforts on the more vulnerable counties may be more effective and less economically damaging than statewide shutdowns.

 

Preprint available at medRxiv (June 26, 2020):

https://doi.org/10.1101/2020.06.25.20139956

Sourced through Scoop.it from: www.medrxiv.org