Strong Effects of Population Density and Social Characteristics on Distribution of Covid-19 Infections in the United States

Abstract

Coronavirus disease 2019 (Covid-19) has devastated global populations and has had a large impact in the United States with the number of infections and deaths growing exponentially. Using a smooth generalized additive model with quasipoisson counts for total infections and deaths, we developed a county-level predictive model that included population demographics, social characteristics, social distancing, and testing data. This model strongly predicted the actual US distribution of Covid-19, accounting for 94.8% of spatial-temporal variation in total infections and 99.3% in Covid-19 related fatalities from March 15, 2020. US counties with higher population density, poverty index, civilian population, and minorities, especially African Americans had a higher number of confirmed infections adjusted for county population. Social distancing measured by the change in the rate of human encounter per km2 relative to pre-covid- 19 national average was associated with slower rate of Covid-19 infections, whereas higher testing was associated with higher number of infections. The number of people infected was increasing, however, the rate of increase in new infections was starting to show signs of plateauing starting from the second week of April. Our model projects 2.11 million people to test positive for Covid-19 and 122,951 fatalities by June 1, 2020. Importantly, our model suggests strong social differences in the infections and deaths across US communities, and inequities in areas with larger African American minorities and higher poverty index expected to show higher rates of Covid-19 infections and deaths. Preventive steps including social distancing and community closures have been a cornerstone in stopping the transmission and potentially reducing the spread of the disease. Crucial knowledge of the role of social characteristics in the disease transmission is essential to understand current disease distribution, predict future distribution, and plan additional preventive steps.