A Bayesian Spatial Analysis of the Association of Socioeconomic Inequality, Epidemiological Conditions and Human Mobility Changes During the US COVID-19 Epidemic

Abstract

Background: The spread of COVID-19 in the US prompted non-pharmaceutical interventions which caused a sudden reduction in mobility everywhere, although with large local disparities between different counties. Methods: Using a Bayesian spatial modelling framework, we investigated the association of county-level demographic and socioeconomic factors with changes in workplaces mobility at two points in time: during the early stages of the epidemic (lockdown phase) and in the following phase (recovery phase). Findings: While controlling for the epidemiological situation, we found that the county-level socioeconomic and demographic covariates explain about 40% of the variance in changes in workplaces mobility in the lockdown phase, which reduces to about 10% in the recovery phase. During the lockdown phase, larger drops in workplaces mobility were observed in counties with a higher income, an older population, a lower density of Hispanic population, that are less-densely populated but with a larger density of workforce. Additionally, when also accounting for the residual spatial variability, the variance explained by the model in both phases increases up to 80%, suggesting strong proximity effects. Interpretation: This study suggests a strong association in the early stages of the epidemic between county-level changes in workplaces mobility and demographic and socioeconomic inequalities. Similar behaviours in nearby counties are present across the whole period of study, indicating a potential link to state- and county-wise regulations. These results provide community-level insights on the evolution of the US mobility during the COVID-19 epidemic that could directly benefit policy evaluation and interventions. Funding: None.Declaration of Interests: The authors declare no conflict of interests.