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

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.

Keywords: COVID-19; Generalized additive mixed model; Human mobility; Mobile device location data; Non-pharmaceutical interventions.

Fig. 1

A Big-Data Driven Analytical Framework for Understanding Human Mobility Trend and Policy Decision Support during COVID-19 Pandemic.

Fig. 2

(a) Daily varying pattern of three human mobility metrics and (b) changes of metrics from February 1st, 2020 to May 31st, 2020 compared with January 2020. Note: On February 17th, 2020 (Washington's Birthday) and May 25th, 2020 (Memorial Day), fluctuations are observed across the nation due to the holiday effects, we fix the outliers with linear interpolation.

Fig. 3

Percentage change in three human mobility metrics across U.S. counties in different periods (i.e. March 20th, 2020 to March 27th, 2020; April 20th, 2020 to April 27th, 2020) compared with January 2020. Note: All analyses are based on contiguous United States (i.e. Hawaii and Alaska are excluded).

Fig. 4

Residuals of the Uncorrelated GAM model and the GAMM model in Modeling Δ Trip per person from February 1st, 2020 to May 31st, 2020.

Fig. 5

The time-varying marginal effect of stay-at-home and reopening orders from February 1st, 2020 to May 31st, 2020.

Fig. 6

Estimated spline function from February 1st, 2020 to May 31st, 2020. (a) Δ Trip per person; (b) Δ Person-miles traveled; (c) Δ Proportion of staying home.