Understanding Bike-sharing Mobility Patterns in Response to the COVID-19 Pandemic (preprint)

The outbreak of COVID-19 brings huge challenges to the bike-sharing system and even society structure. Thus, it is urgent to fully understand the impacts of pandemic on bike-sharing behavior. This paper proposed a comprehensive approach to investigate the mobility patterns influenced by the outbreak of COVID-19 pandemic with the case in Washington D.C. Multiple-source data, including bike-sharing trip information, COVID-19 information, geographic and POI information, were collected. Although the total bike-sharing trips decreased up to 80% in spatial-temporal analysis, the trips made by casual user still increased. In addition, the docking stations and trips from 2019 to 2021 were utilized to construct the bike-sharing network. The results present that major network properties, such as connectivity, clustering coefficient, and accessibility, experienced significant decrease during the pandemic. Through the detection of community with modularity method, the evolution of community structure before and after pandemic was captured. The increased long-range and long-time bike-sharing trips results in the combination between central communities and outer communities. To better understand the community structure, the POI (Point of Interests) auxiliary analysis was conducted and central community was found to have similar proportion of POIs even during the pandemic. Implications for bike-sharing management and operation policy was also addressed.

Risk perception and behavior change after personal vaccination for COVID-19 in the USA (preprint)

Although vaccines are crucial for giving pandemic-stricken societies the confidence to return to socioeconomic normalcy, vaccination may also induce laxity in personal protective behaviors (e.g., handwashing, facemask use). We use the quasi-experimental context of the COVID-19 vaccine rollout across the United States to quantify the impact of different stages of personal vaccination on people’s risk perceptions, daily activities, and risk mitigation behaviors, which we measure in a three-wave national panel study (N wave-1 = 7,358, N wave-2 = 3,000, N wave-3 = 2,345) from March to June, 2021, and validate using vaccination, infection, and human mobility data. Socializing rebounded after only partial vaccination. After full vaccination, communal activities recovered; however, the propensity for protective behaviors declined. The effects were heterogenous depending on vaccination level, demographics, and infection history. We further use a utility theory framework to model risk-value trade-offs and risk-construction for different behaviors.

Population Outflow from Wuhan Determines the Spread and Distribution of the COVID-19 Epidemic in China (preprint)

Sudden, large-scale, and diffuse human migration can amplify localized outbreaks into pandemics. Rapid and accurate tracking of human population mobility using reliable mobile phone data could therefore be helpful for policy responses. Here, we examine mobile phone geolocation data for all 18,514,920 counts of individuals egressing or transiting through the prefecture of Wuhan, China, between January 1 and 24, 2020. First, we document the efficacy of quarantine measures in ceasing population movement.Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency of, and geographical distribution of, COVID-19 infections through February 12, 2020, across all of China, up to two weeks in advance. Third, we present a risk model that not only predicts confirmed cases, but also identifies high- transmission-risk locales at an early stage. Fourth, we develop a mobility-data-driven modeling framework to statistically derive the growth pattern of COVID-19 and its spread; this model can yield a benchmark trend and an index for assessing COVID-19 risk for different geographic locations (290 prefectures). Prefectures above the index’s 90% confidence interval are likely experiencing more local transmissions than imported cases; prefectures below the 90% confidence interval are controlling the spread of the virus more effectively (or, alternatively, are at greater risk of information inaccuracy). This approach can be used by policy makers in developing nations, which typically have mobile phone infrastructure but limited healthcare capabilities, to make rapid and accurate risk assessments ahead of second-wave outbreaks in order to overcome resource and logistics limitations.