![]() Different countries or regions within a country experience diverse numbers of new cases and fatalities, with patterns that are difficult to anticipate. Not only can we not explain, on an individual basis, who will experience severe illness or no symptoms at all, but we often lack this predictive power even on the larger scale of entire regions, where personal traits and individual genetical predispositions are averaged out. More than two years into the COVID-19 pandemic, there are still many open questions regarding the spread and severity of SARS-CoV-2. We argue that the approach may be useful whenever significant determinants of disease progression over diverse geographic regions should be selected from a large number of potentially important factors. ![]() Possible causes behind this result are discussed. We obtain a surprisingly simple result, where only two variables are clearly and robustly selected-population density and proportion of African Americans. We then use a number of advanced feature selection techniques from machine learning to determine which of these predictors significantly impact the disease severity. To account for these difficulties, we assemble 115 predictors for more than 3,000 US counties and employ a well-defined COVID-19 severity measure derived from epidemiological dynamics modeling. Understanding sociodemographic factors behind COVID-19 severity relates to significant methodological difficulties, such as differences in testing policies and epidemics phase, as well as a large number of predictors that can potentially contribute to severity.
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