Multi-agent simulation model for the evaluation of COVID-19 transmission

Area of Science:

• Epidemiology
• Computational Social Science

Background:

• COVID-19 pandemic highlighted the need for advanced epidemic modeling.
• Existing models often lack detailed individual behavior and multi-region dynamics.

Purpose of the Study:

• To develop an agent-based model (ABM) for analyzing COVID-19 spread in multi-region environments.
• To incorporate individual agent features and societal factors into epidemic simulations.
• To support decision-making in pandemic management.

Main Methods:

• Developed a novel agent-based model representing individuals with diverse features (health, mobility, awareness, etc.).
• Simulated epidemic spread across interconnected regions with varying agent concentrations.
• Incorporated hypothetical social scenarios to assess viral transmissibility impacts.

Main Results:

• The agent-based model effectively simulates COVID-19 epidemic spread in a multi-region context.
• Simulation results demonstrate the influence of individual agent characteristics and societal interactions on epidemic dynamics.
• The model provides insights into the effectiveness of different pandemic management interventions.

Conclusions:

• Agent-based modeling offers a powerful tool for understanding complex epidemic processes.
• The proposed model enhances epidemic analysis by integrating individual behavior and regional dynamics.
• This approach can significantly aid policymakers in developing targeted pandemic control strategies.