# Epidemic Policy Optimisation

Epidemic interventions are textual policies, yet a plain LLM lacks the epidemic dynamics to project their consequences, the surveillance signals to judge severity, and the institutional constraints on admissible actions. EpiWorld grounds an LLM policy agent in an action-conditioned epidemiological world model, screens candidate interventions through counterfactual rollouts, and distils simulated outcomes into reusable lessons. On retrospective COVID-19 and influenza data, the full configuration cuts 8-week cumulative hospitalisations by 59.3% versus the reference historical policy on real US Omicron, and by about 16% on average across six LLM backbones.

Epidemic interventions are textual policies, yet a plain LLM lacks the epidemic dynamics to project their consequences, the surveillance signals to judge severity, and the institutional constraints on admissible actions. EpiWorld grounds an LLM policy agent in an action-conditioned epidemiological world model, screens candidate interventions through counterfactual rollouts, and distils simulated outcomes into reusable lessons. On retrospective COVID-19 and influenza data, the full configuration cuts 8-week cumulative hospitalisations by 59.3% versus the reference historical policy on real US Omicron, and by about 16% on average across six LLM backbones.