  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.
