Modeling the Spread of Infectious Diseases in Multiagent Systems: Insights from Agent-Based Simulations

Modeling the Spread of Infectious Diseases in Multiagent Systems illuminates dynamic disease transmission at the micro level within complex social contexts. This paper explores the integration of multiagent systems (MAS) and agent-based simulations (ABS) for understanding infectious disease spread within complex social contexts. MAS involves autonomous agents with unique characteristics contributing to macro-level outcomes via micro-level interactions. ABS, when incorporated into MAS, simulates individual behaviors, revealing insights into complex systems. Agent-based modeling (ABM) surpasses limitations of compartmental models by considering demographics, mobility, and social networks in disease spread. It accommodates diverse scenarios and intervention strategies. Social network integration in ABMs illuminates disease propagation within specific contexts. The synergy of human and intelligent agents enhances simulations, incorporating human-like decision-making. Visualizing disease spread dynamics informs policy, while multiagent models predict intervention impacts. Effective communication is vital in managing public perception of these advancements. The fusion of MAS, ABS, and AI offers an innovative approach to comprehend complex systems, especially in disease control, with potential implications for policy and intervention strategies.