A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity.

ABSTRACT: 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. 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. We then use a number of advanced feature selection techniques from machine learning to determine which of these predictors significantly impact the disease severity. We obtain a surprisingly simple result, where only two variables are clearly and robustly selected-population density and proportion of African Americans. Possible causes behind

SUBMITTER: Tumbas M 

PROVIDER: S-EPMC10080051 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature