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.

翻译：基于智能体模型（ABMs）已成为研究复杂社会交互的强大工具，尤其在公共卫生和传染病研究领域。为增强传统ABM、实现模型自动校准并降低大规模扩展所需的计算资源，我们通过耦合图神经网络（GNN）与长短期记忆（LSTM）网络，开发了一种张量化且可微分的智能体模型。该模型被用于研究2019年新西兰爆发的麻疹疫情，展现了精准模拟疫情动态的能力，尤其是在反复病例高峰期。本文表明，通过融合最新人工智能（AI）技术与传统ABM的优势，我们能够更深入地洞察传染病爆发的动力学机制，从而在制定兼顾疫情管控与日常生活干扰最小化的有效策略时做出更明智的决策。