Social and spatial disparities in individuals’ mobility response time to COVID-19: A big data analysis incorporating changepoint detection and accelerated failure time models

Abstract

Although an increasing number of studies have investigated the lag time between the outbreak of COVID-19 and behavior change, few have accurately measured response times to the epidemic at the individual scale as well as their social and spatial heterogeneities. Using a large-scale, long time series dataset of individual-level mobile phone trajectories from Shenzhen, China, we compared six changepoint detection (CPD) algorithms in terms of their performance in detecting true changepoints (CPs) in time series data of individuals’ daily travel distances. We found that the kernel-based CPD method outperformed other algorithms. We thus adopted this method to calculate Shenzhen residents’ mobility response times to the outbreak of COVID-19 and further used an accelerated failure time (AFT) model to explore factors affecting response times. The results suggest that the average and median mobility response times to the outbreak in Shenzhen were 4.64 days and 4 days, respectively. Males and the elderly responded more slowly to the outbreak, while responses were faster among residents in neighborhoods with a higher percentage of highly educated, married, or employed individuals; with better regional accessibility to the city center, railway stations, or the airport; and with higher residential density. These findings can assist policymakers in determining the policy timeline and re-assessing the effectiveness and equity impact of mobility restriction policies and designing more responsive policies varying by social groups and built environment features, helping build socially-resilient neighborhoods in the post-COVID era.