一种支持AI的基于智能体的模型及其在新西兰麻疹暴发模拟中的应用
An AI-enabled Agent-Based Model and Its Application in Measles Outbreak Simulation for New Zealand
AI总结：
本研究耦合GNN与LSTM开发张量化可微分ABM，实现自动校准与低计算成本扩展，用于模拟2019年新西兰麻疹暴发，精准再现疫情动态，为平衡防疫与民生的决策提供支持。
摘要：
基于智能体的模型（Agent Based Models, ABMs）已成为研究复杂社会互动的有力工具，尤其在公共卫生与传染病研究领域。为改进传统ABM，实现自动模型校准并降低模型扩展所需的计算资源，我们通过耦合图神经网络（Graph Neural Network, GNN）和长短期记忆（Long Short-Term Memory, LSTM）网络，开发了一种张量化且可微分的基于智能体的模型。该模型被用于研究2019年新西兰发生的麻疹暴发，展现出精准模拟暴发动态的良好能力，尤其是在重复病例的高峰期。本文表明，借助最新的人工智能（Artificial Intelligence, AI）技术与传统ABMs的能力，我们能更深入地洞察传染病暴发的动态机制。这反过来有助于我们在制定有效策略时做出更明智的决策，以在疫情管控与最小化日常生活干扰之间取得平衡。
英文摘要：
Agent Based Models (ABMs) have emerged as a powerful tool for investigating complex social interactions, particularly in the context of public health and infectious disease investigation. In an effort to enhance the conventional ABM, enabling automated model calibration and reducing the computational resources needed for scaling up the model, we have developed a tensorized and differentiable agent-based model by coupling Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) network. The model was employed to investigate the 2019 measles outbreak occurred in New Zealand, demonstrating a promising ability to accurately simulate the outbreak dynamics, particularly during the peak period of repeated cases. This paper shows that by leveraging the latest Artificial Intelligence (AI) technology and the capabilities of traditional ABMs, we gain deeper insights into the dynamics of infectious disease outbreaks. This, in turn, helps us make more informed decision when developing effective strategies that strike a balance between managing outbreaks and minimizing disruptions to everyday life.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
