Graph World Models for Constrained Epidemic Policy Planning

Abstract: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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.35545 [cs.LG]

(or arXiv:2609.35545v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.35545 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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