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Epidemic intervention policies are textual artefacts that human decision-makers interpret, justify, and revise through natural language, making large language models a natural candidate for epidemic policy reasoning. A naive LLM, however, lacks the epidemic dynamics needed to project intervention consequences, the quan...
EpiMind, a graph world model framework for constrained epidemic policy planning across regions, demonstrates that graph-structured policy imagination with explicit constrained coordination supports effective epidemic interventions from learned dynamics.
This work decomposes infections into trend, seasonal, and residual components and uses these signals to drive continuous-time latent dynamics while jointly forecasting and inferring time-varying transmission, recovery, and immunity-loss rates.
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