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

TL;DR

This paper introduces a novel Bayesian copula-based spatio-temporal model for multi-type infectious disease data, enabling better understanding of complex pathogen interactions across geography and time.

Contribution

It develops a joint state-space model with copula-based dependence structures and efficient MCMC algorithms, advancing multi-type epidemic modelling.

Findings

Successfully identified simulated epidemic patterns

Accurately inferred interaction parameters

Fitted to real European meningococcal disease data

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.