AI 中文总结
该研究针对难以在线校准的智能体流行病模型，提出GenDA框架，可从聚合监测数据中估计流行病状态与参数，提升了同化后的预测效果。
AI 中文摘要
可靠的流行病监测通常需要从含噪声、空间聚合且可能稀疏的监测数据中推断区域感染负担与传播异质性。智能体模型（ABMs）因能表征个体行为、接触异质性及局部干预措施而适用于该任务，但这些特性使其难以在线校准。我们开发了一种用于部分观测流行病ABM的生成式AI数据同化（GenDA）框架，该框架在考虑可观测宏观态与潜在智能体级微观态间差距的同时，估计流行病状态与异质性参数场。GenDA结合了用于宏观态校正的无训练得分基生成更新、基于宏观态差异的直接参数更新，以及恢复与ABM一致性的宏微观重新分配步骤。在受控且地理明确的合成实验中，该框架从聚合观测中恢复了区域感染负担、主要热点结构及有效传播异质性，且相较于仅状态同化，同化后预测结果有所提升。
英文摘要
Reliable epidemic monitoring often requires inferring regional infection burden and transmission heterogeneity from noisy, spatially aggregated, and potentially sparse surveillance data. Agent-based models (ABMs) are attractive for this task because they represent individual behavior, contact heterogeneity, and localized interventions, but these same features make them difficult to calibrate online. We develop a generative AI-based data-assimilation (GenDA) framework for partially observed epidemic ABMs that estimates both the epidemic state and a heterogeneous parameter field while respecting the gap between observable macrostates and latent agent-level microstates. GenDA combines a training-free, score-based generative update for macrostate correction with a direct parameter update based on macrostate discrepancies, followed by a macro-micro reassignment step that restores consistency with the ABM. In controlled and geographically explicit synthetic experiments, the framework recovers regional epidemic burden, dominant hotspot structures, and effective transmission heterogeneity from aggregated observations, while improving post-assimilation forecasts relative to state-only assimilation.
