EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control

Yang, Chuang1; Zhang, Zhiwen1; Fan, Zipei1,2; Jiang, Renhe1,2; Chen, Quanjun1,2; Song, Xuan1,2; Shibasaki, Ryosuke1,2

2023-08-01

10.1109/TVCG.2022.3165385

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS   影响因子和分区

1077-2626

1941-0506

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.

Human mobility simulation epidemic control visual analytics interactive system big trajectory data

SCI ; EI

英语

通讯

Computer Science

Computer Science, Software Engineering

WOS:001022080200010

IEEE COMPUTER SOC

20221511959382

Big data ; Diagnosis ; Disease control ; Visualization

Medicine and Pharmacology:461.6 ; Health Care:461.7 ; Data Processing and Image Processing:723.2

COMPUTER SCIENCE

Web of Science

1.Univ Tokyo, Ctr Spatial Informat Sci, Tokyo 1138654, Japan
2.Southern Univ Sci & Technol, SUSTech UTokyo Joint Res Ctr Super Smart City, Shenzhen 518055, Guangdong, Peoples R China

Yang, Chuang,Zhang, Zhiwen,Fan, Zipei,et al. EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control[J]. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,2023,29(8):3586-3601.

Yang, Chuang.,Zhang, Zhiwen.,Fan, Zipei.,Jiang, Renhe.,Chen, Quanjun.,...&Shibasaki, Ryosuke.(2023).EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control.IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,29(8),3586-3601.

Yang, Chuang,et al."EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control".IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 29.8(2023):3586-3601.