Mofeng Yang

TL;DR: It is found 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, and the dynamics in a positive relationship between mobility inflow and the number of infections during the COVID-19 onset are highlighted.

TL;DR: In this article, the authors present 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.

TL;DR: A summary of the platform is presented and the methodology used to process data and produce the platform metrics are described, to continuously inform decision-makers about the impacts of COVID-19 on their communities using an interactive analytical tool.

TL;DR: The data analytics and longitudinal models reveal a spontaneous mobility reduction that occurred regardless of government actions and a ‘floor’ phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order.

TL;DR: In this article, a pedestrian-based epidemic spreading model is introduced to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic control interventions.