Informing policy via dynamic models: Cholera in Haiti

TL;DR

This paper evaluates and improves statistical methods for modeling cholera transmission in Haiti, balancing biological accuracy and simplicity, to inform effective public health interventions.

Contribution

It introduces enhanced data analysis strategies and likelihood-based inference techniques for complex dynamic models of cholera, addressing model misspecification and improving decision-making.

Findings

Improved model fitting and diagnosis methods for cholera transmission models.

Demonstrated utility of likelihood maximization in high-dimensional spatiotemporal models.

Developed a reproducible workflow for future disease modeling studies.

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. 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.