Socio-spatial inequality and the effects of density on COVID-19 transmission in US cities

Abstract

Cities are often associated with the rapid spread of infectious diseases, driven by the perceived risks of urban density and overcrowding. However, transmission risk can vary considerably within urbanized areas as a function of socio-spatial disparities and the adoption of mitigating behaviors across communities. Here we examine the effect of density on coronavirus disease 2019 (COVID-19) infection rates at the neighborhood scale, within and across US cities. We integrate high-spatial resolution measures of land use and residential population density, mobility, infection rates and social determinants of health to evaluate the impact of neighborhood context on infection risk, while controlling for the potential mitigating effects of social distancing behavior. We are particularly focused on disparities among marginalized and vulnerable neighborhoods, and the generalizability of the results across political, socioeconomic, regional and built environment contexts. Our findings demonstrate a nonlinear relationship between urban density and infection rates, with higher-density neighborhoods more likely to adopt mitigating behaviors to reduce transmission. However, low-income and minority communities, facing cascading health challenges, are found to be least able to modify mobility behavior and therefore experienced a disproportionate burden of COVID-19 infection risk during the first wave of the pandemic. This study analyzes the influence of density on COVID-19 infection rates in cities across the United States and their relationship with socio-spatial inequalities. Its main finding is that density has a nonlinear relationship with infection rates and that socioeconomic factors influence mitigating behaviors in neighborhoods.