Effects of Population Co-location Reduction on Cross-county Transmission Risk of COVID-19 in the United States

TL;DR

This study analyzes how reducing population co-location through social distancing impacts COVID-19 spread across US counties, revealing a one-week delay in mitigation effects and highlighting the importance of cross-county travel restrictions.

Contribution

It provides empirical evidence on the effects of population co-location reduction on COVID-19 transmission risk using Facebook mobility data across US counties.

Findings

Co-location reduction correlates with decreased weekly case growth after one week.

Cross-group co-location probabilities decrease significantly due to travel restrictions.

Within-group co-location remains stable, indicating targeted social distancing impacts.

Abstract

The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social distancing and travel reduction are recognized as essential non-pharmacologic approaches to control the spread of COVID-19. Prior studies demonstrated that human movement and mobility drove the spatiotemporal distribution of COVID-19 in China. Little is known, however, about the patterns and effects of co-location reduction on cross-county transmission risk of COVID-19. This study utilizes Facebook co-location data for all counties in the United States from March to early May 2020. The analysis examines the synchronicity and time lag between travel reduction and pandemic growth trajectory to evaluate the efficacy of social distancing in ceasing the population co-location probabilities, and subsequently the growth in weekly new cases. The results show that the mitigation effects of co-location reduction appear in the growth of weekly new cases with one week of delay. Furthermore, significant segregation is found among different county groups which are categorized based on numbers of cases. The results suggest that within-group co-location probabilities remain stable, and social distancing policies primarily resulted in reduced cross-group co-location probabilities (due to travel reduction from counties with large number of cases to counties with low numbers of cases). These findings could have important practical implications for local governments to inform their intervention measures for monitoring and reducing the spread of COVID-19, as well as for adoption in future pandemics. Public policy, economic forecasting, and epidemic modeling need to account for population co-location patterns in evaluating transmission risk of COVID-19 across counties.