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

Area of Science:

• Epidemiology
• Public Health
• Data Science

Background:

• The COVID-19 pandemic necessitated rapid implementation of human mobility restrictions to curb transmission.
• Evaluating the effectiveness of these diverse public health interventions requires robust analytical tools.
• Big data and information technology offer potential for real-time assessment of epidemic control strategies.

Purpose of the Study:

• To design and develop an interactive visual analytics system, named Epidemic Mobility (EpiMob).
• To simulate the impact of various human mobility restriction policies on epidemic spread.
• To provide a tool for policymakers to analyze and compare the outcomes of different control measures.

Main Methods:

• Utilized big human mobility data and city Points of Interest (POI) data.
• Developed an interactive visual analytics system (EpiMob) for simulation.
• Conducted case studies in the Greater Tokyo Area with domain expert interviews.

Main Results:

• EpiMob interactively simulates changes in human mobility and infection status based on policy implementation.
• The system allows users to define spatial and temporal parameters for restriction policies.
• Dynamic display and flexible comparison of infection situations under different policy scenarios were achieved.

Conclusions:

• The EpiMob system demonstrates utility in providing insights into the effects of human mobility restriction policies.
• It supports evidence-based decision-making for epidemic control by enabling measurement and comparison of policy outcomes.
• Visual analytics leveraging big data can enhance the evaluation of public health interventions during pandemics.