"How did communities respond to the COVID-19 pandemic?": Exploring socio demographic factors and movement patterns throughout the global pandemic.

Keywords: Medical and health geography, Covid-19, movement patterns, socio-demographic factors, Virginia
Abstract Type: Paper Abstract

Abstract

Understanding how different communities reduced their mobility to the COVID-19 pandemic is essential to predicting and controlling future emerging diseases. Behavioral response to the pandemic varied greatly across populations, based on social, cultural, and economic factors. Here, we compared how census tracts across Virginia changed their movement patterns during two key periods: late March 2020, when lockdown policies were first instituted, and July 2020, when lockdown fatigue reduced compliance with stay-at-home orders, which also began to lift across summer 2020. To do this, we used a human mobility flow dataset provided by SafeGraph that measured trips to other census tracts on a daily basis, and compared average numbers of trips out of each census tract in Virginia during our study periods with a baseline pre-pandemic period. We then used a generalized linear model to determine the social and economic factors that best predicted the census tracts that reduced mobility most in April and July 2020. We found that the most important predictors differed between these two periods. In April 2020, the best predictors of reduced mobility were median age of individuals in the census tract, proportion with a Bachelor’s degree or higher, and population density. In July 2020, the best predictors of reduced mobility were proportion with a Bachelor’s degree or higher, and proportion of Black residents. These results suggest that demographic characteristics strongly impact how communities respond to emerging diseases and lockdown policies, and can help identify key communities that may be at highest risk during future epidemics.