Bayesian copula-based modelling for multi-type spatio-temporal epidemic data

2026-05-05

AI Summary

This study addresses the challenge of modeling complex spatiotemporal interactions among multiple pathogen types, which hinders accurate characterization of inter-strain epidemiological dependencies. To overcome this limitation, the authors propose a biologically informed joint spatiotemporal state-space model for multi-type infectious diseases, incorporating Copula functions—used here for the first time in this context—to explicitly capture dependence structures among strains, thereby enhancing both model interpretability and flexibility. Bayesian inference is employed with efficient Markov chain Monte Carlo (MCMC), bridge sampling, and importance sampling to enable parameter estimation and model comparison in high-dimensional settings. Simulation studies demonstrate the method’s accuracy in dynamic pattern recognition and parameter recovery, while its application to monthly meningococcal disease data from 26 European countries further validates its practical utility. An open-source R package accompanies the work to facilitate reproducibility and broader adoption.

Abstract

The study of infectious disease epidemiology for multi-type disease pathogens requires modelling techniques that account for the complex interactions existing between strains across geography and time. In this paper, we propose a novel multi-type spatio-temporal infectious disease model to better support the understanding of these pathogens. We formulate a joint state-space for all epidemics arising for a given multi-type pathogen as well as biologically informed representations of how these epidemic states may interact. We introduce the use of several copula models to uncover the dependence structure of epidemics between strains. We develop a computationally efficient Markov chain Monte Carlo (MCMC) sampling scheme for all proposed models. We also provide robust model comparison techniques using bridge sampling and importance sampling to evaluate model evidence in high-dimensional space. We demonstrate the performance of our proposed models using simulated datasets, where simulated epidemics were successfully identified and associated parameters correctly inferred. The proposed models were also fitted to monthly multi-type incidence data on invasive meningococcal disease from 26 European countries. The accompanying software is freely available as a R package at https://github.com/Matthewadeoye/MultiOutbreaks.

Problem

• multi-type spatio-temporal epidemic
• infectious disease epidemiology
• strain interactions
• Bayesian modelling
• copula

Innovation

• Bayesian copula
• multi-type spatio-temporal model
• state-space modeling
• MCMC sampling
• model comparison