Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models

TL;DR

This paper introduces a Bayesian multivariate autoregressive model to estimate pathogen interactions from infection time-series data, accounting for confounders, with applications to respiratory viruses in urban populations.

Contribution

The study develops a novel statistical framework that robustly infers pathogen interactions from multivariate time series, extending existing models to include covariance estimation and confounder control.

Findings

Identified positive and negative covariances among respiratory viruses.

Validated the model with simulated data.

Applied to real data revealing specific pathogen interactions.

Abstract

It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts. Although community ecology approaches have been applied to determine pathogen interactions at the within-host scale, methodologies enabling robust inference of the epidemiological impact of pathogen interactions are lacking. Here we developed a novel statistical framework to identify statistical covariances from the infection time-series of multiple pathogens simultaneously. Our framework extends Bayesian multivariate disease mapping models to analyse multivariate time series data by accounting for within- and between-year dependencies in infection risk and incorporating a between-pathogen covariance matrix which we estimate. Importantly, our approach accounts for possible confounding drivers of temporal patterns in pathogen infection frequencies, enabling robust inference of pathogen-pathogen interactions. We illustrate the validity of our statistical framework using simulated data and applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness.