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. As a case study, we consider the 2010-2019 cholera epidemic in Haiti. We study three dynamic models developed by expert teams to advise on vaccination policies. We assess previous methods used for fitting and evaluating these models, and we develop data analysis strategies leading to improved statistical fit. Specifically, we present approaches to diagnosis of model misspecification, development of alternative models, and computational improvements in optimization, in the context of likelihood-based inference on nonlinear dynamic systems. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.

翻译：公共卫生决策必须在何时以及如何实施干预措施以控制传染病流行的问题上做出。这些决策应基于流行病数据以及当前对传播动态的理解。此类决策可以被视为关于科学驱动的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的、数据驱动的决策的方法论任务。这需要应对生物保真度与模型简洁性之间的权衡，以及所有复杂度层面模型存在的误设现实。作为案例研究，我们考察了2010-2019年海地霍乱疫情。我们研究了由专家团队开发用于指导疫苗接种政策的三个动态模型。我们评估了之前用于拟合和评价这些模型的方法，并开发了能够改进统计拟合的数据分析策略。具体而言，在非线性动态系统的似然推断背景下，我们提出了模型误设诊断、替代模型开发以及优化计算改进的方法。我们的工作流程具有可重复性和可扩展性，便于对该疾病系统进行未来研究。