The outbreak of coronavirus disease (COVID-19) has swept across more than 180 countries and territories since late January 2020. As a worldwide emergency response, governments have taken various measures and implemented policies, such as self-quarantine, travel restrictions, work from home, and regional lockdown, to control the rapid spread of this epidemic. The common intention of these countermeasures is to restrict human mobility because COVID-19 is a highly contagious disease that is spread by human-to-human transmission. Medical experts and policy makers have expressed the urgency of being able to effectively evaluate the effects of human restriction policies with the aid of big data and information technology. Thus, in this study, based on big human mobility data and city POI data, we designed an interactive visual analytics system named EpiMob (Epidemic Mobility). The system interactively simulates the changes in human mobility and the number of infected people in response to the implementation of a certain restriction policy or combination of policies (e.g., regional lockdown, telecommuting, screening). Users can conveniently designate the spatial and temporal ranges for different mobility restriction policies, and the result reflecting the infection situation under different policies is dynamically displayed and can be flexibly compared. We completed multiple case studies of the largest metropolitan area in Japan (i.e., Greater Tokyo Area) and conducted interviews with domain experts to demonstrate that our system can provide illustrative insight by measuring and comparing the effects of different human mobility restriction policies for epidemic control.
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