Enhancing healthcare infrastructure resilience through Agent-Based Simulation methods

2025-01-01

Computer Communications

AI Summary

Healthcare systems exhibit insufficient resilience during public health emergencies. Method: This study develops a multi-agent simulation framework integrating adaptive learning mechanisms to model dynamic interactions among healthcare personnel, patients, medical supplies, and facilities. It innovatively embeds reinforcement learning into agent-based modeling (ABM) to enable autonomous behavioral evolution of agents and proposes “resilience entropy” as a novel quantitative resilience metric. The framework combines hybrid NetLogo–Python modeling, graph neural networks (GNNs) for facility topology representation, and Monte Carlo stress testing. Contribution/Results: Validated on real-world healthcare network data from three provinces/municipalities, the framework increases the failure threshold of critical hub nodes by 42%, reduces emergency dispatch response time by 35%, and achieves an 89.6% accuracy rate in policy recommendation.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Problem

• Enhancing healthcare resilience via agent-based simulations.
• Optimizing resource allocation during concurrent crises.
• Evaluating mitigation strategies for healthcare system risks.

Innovation

• Agent-based simulation model
• Simulation-based optimization approach
• Parameterizable risk scenarios