Structural modeling of COVID-19 spread in relation to human mobility

Abstract

Human mobility is considered as one of the prominent non-pharmaceutical interventions to control the spread of the pandemic (positive effect from mobility to infection). Conversely, the spread of the pandemic triggered massive changes to people’s daily schedules by limiting their movement (negative effect from infection to mobility). The purpose of this study is to investigate this bi-directional relationship between human mobility and COVID-19 spread across U.S. counties during the early phase of the pandemic when infection rates were stabilizing and activity-travel behavior reflected a fairly steady return to normal following the drastic changes observed during the pandemic’s initial shock. In particular, we applied Structural Regression (SR) model to investigate a bi-directional relationship between COVID-19 infection rate and the degree of human mobility in a county in association with socio-demographic and location characteristics of that county, and state-wide COVID-19 policies. Combining U.S. county-level cross-sectional data from multiple sources, our model results suggested that during the study period, human mobility and infection rate in a county both influenced each other, but in an opposite direction. Metropolitan counties experienced higher infection and lower mobility than non-metropolitan counties in the early stage of the pandemic. Counties with highly infected neighboring counties and more external trips had a higher infection rate. During the study period, community mitigation strategies, such as stay at home order, emergency declaration, and non-essential business closure significantly reduced mobility whereas public mask mandate significantly reduced infection rates. The findings of this study will provide important insights to policy makers in understanding the two-way relationship between human mobility and COVID-19 spread and to derive mobility-driven policy actions accordingly.

Characterizing pandemic waves: A latent class analysis of COVID-19 spread across US counties

“…It indicates that the presence of higher fraction of older adults does not make a county more vulnerable for pandemic spread. Findings from other studies support this observation with the claim that people in the older age group have a lower tendency to spread the disease than the younger population [41] , [42] . In contrast, counties with a higher share of African-American people are less likely to belong to Class 1 than other classes.…”

“…That means counties with a higher cumulative infection rate have a higher chance to have African-American people in their counties than the counties with low rates. The positive association between the infection rate and this racial group is also observed in [15] , [42] , [43] , [44] , [45] .…”

Impact of working from home on activity-travel behavior during the COVID-19 Pandemic: An aggregate structural analysis

“…Barrero et al, 2020 , Beck and Hensher, 2020 , Beck et al, 2020 , Carozzi et al, 2020 , Centers for Disease Control and Prevention (CDC), 2020 , Centers for Disease Control and Prevention (CDC), 2021a , Centers for Disease Control and Prevention (CDC), 2021b , Dong et al, 2020 , Google, 2020 , Rafiq et al, 2022 , US EPA, 2014 , Van Acker and Witlox, 2010 .…”

Beyond fear and abandonment: Public transport resilience during the COVID-19 pandemic

“…Furthermore, public health measures (e.g. staying at home, closing non-essential businesses or declaring a state of emergency) that restricted the mobility of residents also reduced infection rates overall ( Rafiq, et al, 2022 ).…”