Can AI agents simulate realistic disease spread?
Agent-based modeling for realistic reproduction of human mobility and contact behavior to evaluate test and isolation strategies in epidemic infectious disease spread
This research paper uses a multi-agent model (ABM) to simulate the spread of respiratory diseases, specifically focusing on evaluating different testing and isolation strategies for pandemic control.
The model simulates individual agents with realistic mobility patterns and disease progression, enabling the study of individual-level responses to different interventions. Key findings highlight the importance of symptom-based testing strategies and quarantine measures, demonstrating that increased testing of symptomatic individuals could potentially replace stricter lockdown interventions. The research also underscores the value of ABMs in providing detailed insights into disease dynamics and evaluating public health strategies for pandemic preparedness.