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.

翻译：世界模型已成为学习潜在动态、模拟反事实未来以及在不确定性下支持规划的统一样式。在本文中，我们认为计算流行病学是世界模型的一个自然但尚未充分开发的场景。这是因为流行病决策需要考虑潜在疾病负担、不完善且依赖于政策的监测信号，以及干预效果受到适应性人类行为的影响。我们提出了一种流行病学世界模型的概念性框架，将流行病建模为受控、部分观测的动态系统，其中：(i) 真实流行病状态是潜在变量，(ii) 观测具有噪声且内生于政策，(iii) 干预作为顺序动作，其效果通过行为和社会反馈传播。我们通过三个案例研究阐明为何显式世界建模对于与政策相关的推理是必要的：行为监测中的策略性虚报、滞后信号（如住院和死亡）的系统性延迟，以及在替代动作序列下相同历史出现分岔的反事实干预分析。