Toward World Models for Epidemiology

ArXi:2604.09519v1 Announce Type: new World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and ing planning under uncertainty. In this paper, we argue that computational epidemiology is a natural and underdeveloped setting for world models. This is because epidemic decision-making requires reasoning about latent disease burden, imperfect and policy-dependent surveillance signals, and intervention effects are mediated by adaptive human behavior. We.