Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties (Preprint)

Abstract

BACKGROUND The spread of COVID-19 at the local level is significantly impacted by population mobility. The U.S. has had extremely high per capita COVID-19 case and death rates. Efficient non-pharmaceutical interventions to control the spread of COVID-19 depend on our understanding of the determinants of public mobility. OBJECTIVE This study used social media data and machine learning to investigate population mobility across a sample of U.S. counties. Statistical analysis was used to examine the socioeconomic, demographic, and political determinants of mobility and the corresponding patterns of per capita COVID-19 case and death rates. METHODS Daily Google population mobility data for 1,085 U.S. counties from March 1st, 2020 to December 31st, 2020 were clustered based on differences in mobility patterns using K-means clustering methods. Social mobility indicators (retail, grocery and pharmacy, workplace, and residence) were compared across clusters. Statistical differences in socioeconomic, demographic, and political variables between clusters were explored to identify determinants of mobility. Clusters were matched with daily per capita COVID-19 cases and deaths. RESULTS Our results grouped U.S. counties into four mobility clusters. Clusters with higher population mobility had a higher percentage of the population aged 65 and over, a higher percentage of Whites with less than high school and college education, a larger percentage of the population with less than a college education, a smaller share of the population that is Hispanic, a lower percentage of the population using public transit to work, and a smaller share of voters who voted for Clinton during the 2016 Presidential Election. Furthermore, those clusters with greater social mobility experienced a sharp increase in per capita COVID-19 case and death rates from October to December 2020. CONCLUSIONS These results emphasize the importance of using Google data and machine learning methods in public health data to support the identification of underlying determinants of social mobility patterns and associated COVID-19 cases.