Accurately estimating human mobility and gauging its relationship with virus transmission is critical for the control of COVID-19 spreading. Using mobile device location data of over 100 million monthly active samples, we compute origin–destination travel demand and aggregate mobility inflow at each US county from March 1 to June 9, 2020. Then, we quantify the change of mobility inflow across the nation and statistically model the time-varying relationship between inflow and the infections. We find that external travel to other counties decreased by 35% soon after the nation entered the emergency situation, but recovered rapidly during the partial reopening phase. Moreover, our simultaneous equations analysis highlights the dynamics in a positive relationship between mobility inflow and the number of infections during the COVID-19 onset. This relationship is found to be increasingly stronger in partially reopened regions. Our study provides a quick reference and timely data availability for researchers and decision makers to understand the national mobility trends before and during the pandemic. The modeling results can be used to predict mobility and transmissions risks and integrated with epidemics models to further assess the public health outcomes.

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The COVID-19 pandemic has led to a globally unprecedented change in human mobility. Leveraging two-year bike-sharing trips from the largest bike-sharing program in Chicago, this study examines the spatiotemporal evolution of bike-sharing usage across the pandemic and compares it with other modes of transport. A set of generalized additive (mixed) models are fitted to identify relationships and delineate nonlinear temporal interactions between station-level daily bike-sharing usage and various independent variables including socio-demographics, land use, transportation features, station characteristics, and COVID-19 infections. Results show: 1) the proportion of commuting trips is substantially lower during the pandemic; 2) the trend of bike-sharing usage follows an “increase-decrease-rebound” pattern; 3) bike-sharing presents as a more resilient option compared with transit, driving, and walking; 4) regions with more white, Asian, and fewer African-American residents are found to become less dependent on bike-sharing; 5) open space and residential areas exhibit less decrease and earlier start-to-recover time; 6) stations near the city center, with more docks, or located in high-income areas go from more increase before the pandemic to more decrease during the pandemic. Findings provide a timely understanding of bike-sharing usage changes and offer suggestions on how different stakeholders should respond to this unprecedented crisis.

The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy and scaled to the entire population of each county and state. The research team are making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public in order to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.