Inference in incidence, infection, and impact: co-infection of multiple hosts by multiple pathogens

Abstract

A large literature concerns the epidemiology of single pathogens on single hosts. Yet in some environmental applications, such as fungal pathogens of forest tree seedlings, the "one host-one pathogen" paradigm may not be applicable. Multiple potential pathogens are often found in a single individual and/or multiple hosts share the same pathogens. Understanding diversity requires techniques to infer how multiple pathogens might regulate multiple hosts and to predict how impacts might vary with the environment. Here we present a hierarchical framework for the case where there is detection information based on multiple sources (cultures, gene sequencing, and survival observations), and the inference problem includes not only parameters that describe environmental influences on pathogen incidence, infection, and host survival, but also on latent states themselves--pathogen incidence at a site and infection statuses of hosts. Due to the large size of the model space, we develop a reversible jump Markov chain Monte Carlo approach to select models, estimate posterior distributions, and predict environmental influences on host survival. We demonstrate with application to a data set involving fungal pathogens on tree hosts, where data include host survival and fungal detection using cultures and DNA sequencing.