A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic. (March 2021)

• Record Type: Journal Article
• Authors: Hu, Songhua; Xiong, Chenfeng; Yang, Mofeng; Younes, Hannah; Luo, Weiyu; Zhang, Lei
• Is Part Of: Transportation research. Volume 124(2021)
• Publication Date: 2021-03
• Subjects: Human mobility -- Non-pharmaceutical interventions -- COVID-19 -- Mobile device location data -- Generalized additive mixed model; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2020.102955
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: A big-data-driven analytical framework assessing human mobility during COVID-19. Location-based evidence from large-scale and privacy-protected samples in the US. County-level human mobility metrics are made publicly available via an open platform. Comprehensive assessment of human mobility using generalized additive mixed models. The policy effect of "Stay-at-home" order on restricting travel is found limited. Abstract: During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus.