Informing policy via dynamic models: Cholera in Haiti

Abstract

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.