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arXiv · 2609.35545

Graph World Models for Constrained Epidemic Policy Planning

Yiqi Su·Rashed Shelim·Lingyi Wang·Walid Saad·Naren Ramakrishnan
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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.

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Yiqi Su, Rashed Shelim, Lingyi Wang, Walid Saad, Naren Ramakrishnan. 2026-09-28. Graph World Models for Constrained Epidemic Policy Planning. https://arxiv.org/abs/2609.35545

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