  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 implemented various measures and policies, such as
self-quarantine, travel restrictions, work from home, and regional lockdown, to
control the spread of the epidemic. These countermeasures seek to restrict
human mobility because COVID-19 is a highly contagious disease that is spread
by human-to-human transmission. Medical experts and policymakers have expressed
the urgency to effectively evaluate the outcome of human restriction policies
with the aid of big data and information technology. Thus, based on big human
mobility data and city POI data, an interactive visual analytics system called
Epidemic Mobility (EpiMob) was designed in this study. The system interactively
simulates the changes in human mobility and infection status in response to the
implementation of a certain restriction policy or a combination of policies
(e.g., regional lockdown, telecommuting, screening). Users can conveniently
designate the spatial and temporal ranges for different mobility restriction
policies. Then, the results reflecting the infection situation under different
policies are dynamically displayed and can be flexibly compared and analyzed in
depth. Multiple case studies consisting of interviews with domain experts were
conducted in the largest metropolitan area of Japan (i.e., Greater Tokyo Area)
to demonstrate that the system can provide insight into the effects of
different human mobility restriction policies for epidemic control, through
measurements and comparisons.
