Toward World Models for Epidemiology

TL;DR

This paper advocates for the application of world models in epidemiology, emphasizing their potential to improve decision-making by modeling latent states, noisy observations, and intervention effects in complex epidemic systems.

Contribution

It introduces a conceptual framework for epidemiological world models, addressing latent states, noisy data, and behavioral feedback in epidemic control.

Findings

Illustrates the necessity of explicit world models through case studies

Highlights challenges like misreporting and delays in epidemic data

Demonstrates counterfactual analysis for policy evaluation

Abstract

World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting 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 introduce a conceptual framework for epidemiological world models, formulating epidemics as controlled, partially observed dynamical systems in which (i) the true epidemic state is latent, (ii) observations are noisy and endogenous to policy, and (iii) interventions act as sequential actions whose effects propagate through behavioral and social feedback. We present three case studies that illustrate why explicit world modeling is necessary for policy-relevant reasoning: strategic misreporting in behavioral surveillance, systematic delays in time-lagged signals such as hospitalizations and deaths, and counterfactual intervention analysis where identical histories diverge under alternative action sequences.