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

Abstract

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.

References in corpus (12)

• Understanding individual human mobility patterns
• A SIDARTHE Model of COVID-19 Epidemic in Italy
• Multiscale mobility networks and the large scale spreading of infectious diseases
• COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions
• Towards Better Bus Networks: A Visual Analytics Approach
• Scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data
• In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals
• Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models
• An Interactive Data Visualization and Analytics Tool to Evaluate Mobility and Sociability Trends During COVID-19
• RAMPVIS: Towards a New Methodology for Developing Visualisation Capabilities for Large-scale Emergency Responses
• Efficient Suspected Infected Crowds Detection Based on Spatio-Temporal Trajectories
• Population-scale testing can suppress the spread of infectious disease

Cited by in corpus (4)

• Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility
• Cluster-Aware Grid Layout
• Volume-Based Space-Time Cube for Large-Scale Continuous Spatial Time Series
• CSLens: Towards Better Deploying Charging Stations via Visual Analytics -- A Coupled Networks Perspective