用世界模型模拟疫情传播，提升政策决策的准确性与预见性。
Toward World Models for Epidemiology
- 将疫情视为受控、部分可观测的动态系统，显式建模潜藏病况与行为反馈。
- 揭示监测数据延迟与策略依赖性对判断的扭曲，需通过反事实推演修正。
- 适合公共卫生决策者与计算流行病学研究者，用于优化防疫策略。
世界模型作为学习潜在动态、模拟反事实未来及在不确定性下规划的统一范式，正逐渐兴起。本文认为，计算流行病学是世界模型的一个自然且尚未充分开发的应用场景。这是因为疫情决策需推理隐藏的疾病负担、不完整且受政策影响的监测信号，且干预效果通过人类行为的自适应反馈传播。我们提出一个流行病学世界模型的概念框架，将疫情建模为受控的、部分可观测的动力系统：（i）真实疫情状态为隐变量；（ii）观测数据噪声大且内生于政策；（iii）干预作为序列动作，其影响通过行为与社会反馈扩散。通过三个案例研究说明显式世界建模对政策相关推理的必要性：行为监测中的战略性误报、住院与死亡等滞后信号的系统性延迟，以及相同历史在不同行动序列下产生反事实结果的干预分析。
原文摘要 · Abstract (English)
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.
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