Early warning of vulnerable counties in a pandemic using socio-economic variables

In the U.S. in early 2020, heterogenous and incomplete county-scale data on COVID-19 hindered effective interventions in the pandemic. While numbers of deaths can be used to estimate actual number of infections after a time lag, counties with low death counts early on have considerable uncertainty about true numbers of cases in the future. Here we show that supplementing county-scale mortality statistics with socioeconomic data helps estimate true numbers of COVID-19 infections in low-data counties, and hence provide an early warning of future concern. We fit a LASSO negative binomial regression to select a parsimonious set of five predictive variables from thirty-one county-level covariates. Of these, population density, public transportation use, voting patterns and % African-American population are most predictive of higher COVID-19 death rates. To test the model, we show that counties identified as under-estimating COVID-19 on an early date (April 17) have relatively higher deaths later (July 1) in the pandemic. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).