Exchangeable Gaussian Processes with application to epidemics

TL;DR

This paper introduces a Bayesian non-parametric framework using multi-task Gaussian processes for modeling infectious disease outbreaks, enhancing predictive accuracy by capturing cross-group dependencies in epidemic data.

Contribution

It presents a novel hierarchical Gaussian process model tailored for epidemic analysis, improving upon existing methods in predictive performance and interpretability.

Findings

Enhanced predictive accuracy over competing models

Effective modeling of cross-group dependencies

Applicable to various epidemic datasets

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.