Socio-economic disparities and COVID-19 in the USA

TL;DR

This study analyzes COVID-19 spread in US counties up to August 2020, revealing correlations with socio-economic factors, especially in rural and non-white communities, using interpretable machine learning and game theory.

Contribution

It introduces a methodology combining interpretable machine learning and game theory to analyze socio-economic impacts on COVID-19 spread, applicable to future pandemics.

Findings

COVID-19 prevalence correlates with socio-economic conditions

Rural and non-white communities are disproportionately affected

Patterns differ significantly between urban and rural areas

Abstract

COVID-19 is not a universal killer. We study the spread of COVID-19 at the county level for the United States up until the 15$^{th}$ of August, 2020. We show that the prevalence of the disease and the death rate are correlated with the local socio-economic conditions often going beyond local population density distributions, especially in rural areas. We correlate the COVID-19 prevalence and death rate with data from the US Census Bureau and point out how the spreading patterns of the disease show asymmetries in urban and rural areas separately and are preferentially affecting the counties where a large fraction of the population is non-white. Our findings can be used for more targeted policy building and deployment of resources for future occurrence of a pandemic due to SARS-CoV-2. Our methodology, based on interpretable machine learning and game theory, can be extended to study the spread of other diseases.