Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.

翻译：公共卫生决策必须确定何时以及如何实施干预措施来控制传染病的流行。这些决策应基于疫情数据以及当前对传播动态的理解。此类决策可以转化为关于科学驱动动态模型的统计问题。因此，我们面临基于随机、部分观测的非线性动态模型构建可信且数据驱动的决策的方法论任务。这需要权衡生物保真度与模型简洁性，并正视各复杂度层级模型中存在的模型误设问题。我们以2010-2019年海地霍乱疫情为案例，评估当前应对这些问题的研究方法。我们考察了三个由专家团队开发的动态模型，用于指导疫苗接种政策。我们评估了先前用于拟合这些模型的方法，并展示了改进后的数据分析策略，从而提升统计拟合效果。具体而言，我们提出了诊断模型误设的方法以及由此改进模型构建的路径。此外，我们展示了近期在高维非线性动态模型似然最大化方面的进展，使这类模型能够应用于时空发病数据的似然推断。我们的工作流程具有可复现性和可扩展性，有助于未来对该疾病系统的深入研究。