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. 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.

Inference on spatiotemporal dynamics for coupled biological populations

“…Likelihood-based inferences via particle filters have been inaccessible for metapopulation models owing to the 'curse of dimensionality' [26]. However, BPF methods can be effective on metapopulation models, as demonstrated in this paper and previously [5,35,51]. All high-dimensional nonlinear filters entail numerical approximation, and these can be assessed by comparing predictive skill (i.e.…”

“…Subsequent research should readily be able to challenge the assumptions of the model in light of subsequent data. In practice, this requires that the scientists provide a free, open-source software environment within which the published analysis can readily be reproduced, modified and extended [ 5 , 60 ]. The development of a principled data analysis environment assists the researchers to explore their own models and data, and this environment should be shared as part of the publication process.…”

“…In epidemiology, metapopulation dynamics can be a barrier to the regional elimination and eventual eradication of a pathogen and may determine the successful invasion of a new pathogen or a new strain of an existing pathogen [ 3 ]. In other situations, spatiotemporal dynamics may be an unavoidable component of the system under study without being the focus of the investigation [ 4 , 5 ].…”

“…Biological systems are characterized by nonlinear stochastic dynamics together with incomplete and noisy measurements [ 18 ]. We, therefore, focus on the class of partially observed Markov process (POMP) models [ 28 ], acknowledging that deterministic models can be conceptually useful but are problematic as statistical explanations of noisy systems [ 5 , 29 ]. The Markov property asserts that the dynamic process has no memory conditional on its current state, which is algorithmically convenient while being scientifically non-restrictive since we can choose what to include in the state.…”