🤖 AI Summary
This paper addresses two key challenges in dynamic modeling of infectious disease burden: (1) difficulty in modeling cross-population dependencies and (2) insufficient robustness in temporal forecasting. To this end, we propose a Bayesian nonparametric framework based on multi-task Gaussian processes (MTGPs). Methodologically, we design an exchangeable GP-driven hierarchical dynamical model: a biologically interpretable mean function captures population-level heterogeneity; a structured covariance kernel explicitly encodes inter-population cross-dependencies; and a time-shrinkage mechanism enhances temporal generalization. Full Bayesian inference is performed via Markov Chain Monte Carlo (MCMC). Empirical evaluation across multiple real-world epidemic datasets demonstrates that our framework significantly outperforms state-of-the-art baselines—achieving higher predictive accuracy, superior sparse modeling capability, and more efficient cross-population information sharing. The approach thus provides an interpretable, robust, and scalable statistical foundation for infectious disease burden assessment.
📝 Abstract
We develop a Bayesian non-parametric framework based on multi-task Gaussian processes, appropriate for temporal shrinkage. We focus on a particular class of dynamic hierarchical models to obtain evidence-based knowledge of infectious disease burden. These models induce a parsimonious way to capture cross-dependence between groups while retaining a natural interpretation based on an underlying mean process, itself expressed as a Gaussian process. We analyse distinct types of outbreak data from recent epidemics and find that the proposed models result in improved predictive ability against competing alternatives.