Exchangeable Gaussian Processes with application to epidemics

Lampros Bouranis; Petros Barmpounakis; Nikolaos Demiris; Konstantinos Kalogeropoulos

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arXiv · 2512.05227

Exchangeable Gaussian Processes with application to epidemics

Lampros Bouranis·Petros Barmpounakis·Nikolaos Demiris·Konstantinos Kalogeropoulos

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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.

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Lampros Bouranis, Petros Barmpounakis, Nikolaos Demiris, Konstantinos Kalogeropoulos. 2025-12-04. Exchangeable Gaussian Processes with application to epidemics. https://arxiv.org/abs/2512.05227

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