# Graph World Models for Constrained Epidemic Policy Planning

arXiv: https://arxiv.org/abs/2609.35545 · HTML: https://mlsanity.com/paper/2609.35545/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2007.03113/) (2020)
- [Reinforcement Learning for Optimization of COVID-19 Mitigation policies](https://mlsanity.com/paper/2010.10560/) (2020)
- [Graph World Model](https://mlsanity.com/paper/2507.10539/) (2025)
- [DMWM: Dual-Mind World Model with Long-Term Imagination](https://mlsanity.com/paper/2502.07591/) (2025)
- [Where to locate COVID-19 mass vaccination facilities?](https://mlsanity.com/paper/2102.07309/) (2021)
- [Toward World Models for Epidemiology](https://mlsanity.com/paper/2604.09519/) (2026)
- [How (Not) to Hybridize Neural and Mechanistic Models for Epidemiological Forecasting](https://mlsanity.com/paper/2602.06323/) (2026)

## Abstract (arXiv)

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.