An LBS and agent-based simulator for Covid-19 research

Area of Science:

• Epidemiology
• Data Science
• Public Health

Background:

• Citizen mobility data is crucial for understanding epidemic spread, including COVID-19.
• Privacy concerns often limit the use of real mobility data, creating a data utilization gap.
• Existing methods may not adequately balance data utility with individual privacy.

Purpose of the Study:

• To propose a novel method for generating pseudo mobility data that preserves privacy.
• To create agent-specific mobility trajectories using geographical and demographic information.
• To develop a user-friendly tool for generating customizable synthetic mobility datasets.

Main Methods:

• Utilized actual geographical environment data from Location-Based Services (LBS).
• Generated per-agent mobility trajectories and exported them as GPS-like data.
• Assigned demographic characteristics (behavior, age, gender, vaccination, mask-wearing) to agents.
• Implemented a web-based data generator for customizable settings.

Main Results:

• Successfully generated pseudo mobility data on a per-agent basis.
• Simulated data incorporated realistic geographical and demographic factors.
• The web-based tool allows for tailored data generation for diverse research needs.
• The generated data demonstrated usability for epidemic spread analysis.

Conclusions:

• The proposed method effectively generates privacy-preserving pseudo mobility data.
• This approach addresses the privacy-security dilemma in utilizing citizen mobility data.
• The tool facilitates research in epidemic modeling and public health by providing usable synthetic data.