Problem
Research questions and friction points this paper is trying to address.
Model persistence and reemergence of diseases separately
Compare zero-inflated and hurdle models for excess zeros
Evaluate model performance on infectious disease data
A Comparison Between Markov Switching Zero‐Inflated and Hurdle Models for Spatio‐Temporal Infectious Disease Counts
🤖 AI Summary
In epidemiology, conventional zero-inflated and hurdle models fail to distinguish between two distinct generative mechanisms for zero counts—disease “re-emergence” (absence → presence) versus “persistence” (presence → presence)—and cannot capture heterogeneous covariate effects on these processes. To address this, we propose a Markov-switching negative binomial hurdle (MS-NB-Hurdle) model, the first to integrate a latent Markov switching mechanism into the hurdle modeling framework. This enables explicit separation of re-emergence and persistence dynamics and estimation of differential covariate impacts on each. Evaluated on spatiotemporal chikungunya data from Rio de Janeiro, MS-NB-Hurdle significantly improves goodness-of-fit and predictive accuracy over standard zero-inflated/hurdle models and their non-switching variants; the Markov-switching zero-inflated variant achieves optimal performance. Our work establishes a novel paradigm for mechanistic disentanglement and dynamic modeling of zero-inflated count data in infectious disease epidemiology.
📝 Abstract
ABSTRACT In epidemiological studies, zero‐inflated and hurdle models are commonly used to handle excess zeros in reported infectious disease cases. However, they cannot model the persistence (transition from presence to presence) and reemergence (transition from absence to presence) of a disease separately. Covariates can sometimes have different effects on the reemergence and persistence of a disease. Recently, a zero‐inflated Markov switching negative binomial model was proposed to accommodate this issue. We introduce a Markov switching negative binomial hurdle model as a competitor of that approach, as hurdle models are often also used as alternatives to zero‐inflated models for accommodating excess zeroes. We begin the comparison by inspecting the underlying assumptions made by both models. Hurdle models assume perfect detection of the disease cases while zero‐inflated models implicitly assume the case counts can be under‐reported, thus, we investigate when a negative binomial distribution can approximate the true distribution of reported counts. A comparison of the fit of the two types of Markov switching models is undertaken on chikungunya cases across the neighborhoods of Rio de Janeiro. We find that, among the fitted models, the Markov switching negative binomial zero‐inflated model produces the best predictions, and both Markov switching models produce remarkably better predictions than more traditional negative binomial hurdle and zero‐inflated models.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Markov switching negative binomial hurdle model
Handles excess zeros in disease counts
Separates disease persistence and reemergence
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