Conclusion
Modelling host-pathogen systems can be considered a “wicked problem” : its success is dependent on multidisciplinary thinking between statisticians, epidemiologists, and ecologists; and there is a balance between accepting over-simplified solutions and being overwhelmed by overly complex ones . Moreover, any solution involves balancing conflicting and fluid temporal and spatial ecological scales . In addition to space and time, the environmental processes that describe host movement—such as climate or seasons—are an often a disregarded, yet essential, third dimension required to model disease systems .
A deeper forensic approach is required to better understand and parameterise complex host-pathogen systems, and the Bayesian toolkit provides a good starting place for this. Overall, future studies of host-pathogen systems require a better representation of ecological hierarchy and scale. Scale needs to be examined in terms of applying suitable Bayesian methods (statistical hierarchy) and by paying attention to the complexity of the system that is being analysed (ecological hierarchy). The join-up between these scales has rarely been studied within ecological systems, and has never been completed for a single system, yet is essential to providing a whole-system model.
Our survey of the wildlife disease literature shows that the current application of Bayesian networks to disease problems is limited: in particular, there is a paucity of hierarchical analyses that infer truly latent parameters or individual heterogeneities across ecological scales. Bayesian methods are now being used in several wildlife disease systems but usually only to tackle standard hypotheses at a single level of the ecological hierarchy or, at most, spanning two levels of the ecological or statistical hierarchies.
By developing Bayesian hierarchical modelling methods and integrating them with real-world empirical data that is not exclusively serological, the potential exists for ecologists to create whole-system models that can provide unique insights into the epidemiology of wildlife disease networks. And the first step towards the whole-system model is to develop a Bayesian hierarchical model that spans the state-space nature of each level of the host-pathogen ecological hierarchy.