图世界模型用于受限流行病政策规划
Graph World Models for Constrained Epidemic Policy Planning
- Virginia Tech（弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出EpiMind图世界模型框架，通过图分解状态空间模型和ADMM优化实现跨区域受限流行病政策规划，显著降低预测误差并保证资源可行性。
AI中文摘要：
流行病政策规划通常需要地理区域之间的协调，考虑由流动性驱动的溢出效应以及如何利用有限资源。现有方法要么缺乏耦合动态的动作条件模型，要么无法保证每期可行性。我们提出了EpiMind，一个用于跨区域受限流行病政策规划的图世界模型框架。图分解的循环状态空间模型从区域潜在信念生成联合策略条件轨迹，而图时间ADMM优化区域干预措施，通过投影强制共享资源可行性，并在学习模型下评估时间规格。EpiMind相对于无图动态建模将入院均方根误差降低了29%，在保证共享预算可行性的情况下，规划结果与最佳可行恒定策略相差1-5%，并在真实情境评估中在三种资源预算下优于所有可部署基线。这些结果表明，具有显式受限协调的图结构策略想象支持从学习动态中进行有效的流行病干预。
英文摘要：
Epidemic policy planning often requires coordination between geographical regions, taking into account mobility-driven spillovers and how to make use of limited resources. Existing methods either lack action-conditioned models of coupled dynamics or cannot guarantee per-period feasibility. We present EpiMind, a graph world model framework for constrained epidemic policy planning across regions. A graph-factored recurrent state-space model generates joint policy-conditioned rollouts from regional latent beliefs, while graph-temporal ADMM optimizes regional interventions, enforces shared-resource feasibility through projection, and evaluates temporal specifications under the learned model. EpiMind reduces admission RMSE by 29% relative to graph-free dynamics modeling, plans within 1-5% of the best feasible constant policy with guaranteed shared-budget feasibility, and outperforms all deployable baselines across three resource budgets in real-context evaluation. These results demonstrate that graph-structured policy imagination with explicit constrained coordination supports effective epidemic interventions from learned dynamics.
