Using Bayesian inference to research wildlife reservoirs of bTB
Bovine tuberculosis (bTB) infections—caused by zoonotic bacteriaMycobacterium bovis— are globally relevant, difficult to control, and scrutinised by disease ecologists across many host species. Research on mammals maintaining bTB reservoirs over wildlife-livestock boundaries dominate the literature, is high-profile and economically important. Yet evidence indicating that no capable bTB host should be disregarded in the search for bTB controls suggests that the current body of research on bTB infection reflects a tradeoff between economic and ecological importance. It is possible that Bayesian approaches could help bridge the data gaps between less and well-studied bTB hosts by enabling information on host ecology from non-disease studies to inform future epidemiological models.
In badger bTB research, a better understanding of transmissions within and among badger reservoirs, as well as between badgers and cattle, or other non-reservoir host species, is required. Like all disease systems, the understanding of the badger-bTB system is constantly shifting with new pieces of information, which can act to better inform priors with expert knowledge and improve our beliefs. For example, the rapid serological Dual-Path Platform VetTB test has recently been validated for bTB testing in badgers and a new badger behaviour called super-ranging has been detected, which is potentially responsible for long-distance bTB transmissions . Consequently, these specific pieces of information could help provide updated estimates of disease transmission and disease progression within a badger bTB system .
Although the number of “how-to” papers describing the power of Bayesian inference in the context of wildlife epidemiology is increasing , research incorporating Bayesian modelling strategies specifically interested in the ante-mortem badger bTB system is limited. Consequently, we are only able to only touch on six specific examples of this following a comprehensive Web of Science search using combinations of the terms: “Bayesian”; “bovine”; “tuberculosis”; “model”; “badger”; “bTB”; and “testing”. Of these six publications , all used Bayesian methods to explore badger bTB transmissions on a “landscape-scale”, here defined as the geographical extent of Woodchester Park, Gloucestershire, UK, where the capture-mark-recapture data common to all six studies was collected. We consider that in the “data-poor” environment of wildlife disease modelling, our understanding of host-pathogen systems over a diversity of geographical scales is particularly limited.
In South Island, New Zealand—where brushtail possum (Trichosurus vulpecula ) were speculated to be the keystone reservoir species of bTB for ca . three decades (Trichosurus vulpecula ) —recent Bayesian research has provided confirmation that its possum population is responsible for South Island’s bTB maintenance; rather than its cattle population. In the UK, although it has been confirmed via Bayesian Integrated Population Models why Woodchester Park badgers are an efficient bTB reservoir , the directionality of bTB transmissions between badgers and cattle remains debated, and it is suspected that badgers are responsible for roughly half of bTB infections in cattle within high cattle-bTB incidence areas . Another analysis concluded that badger to cattle transmissions were ~10.4 times more frequent than vice versa . The geographic scale of study is just one aspect of ecological hierarchy which requires consideration within a whole-system model. Yet until we have whole-system modelling capabilities, we must be careful with general comparisons between studies over varying geographic, spatial, temporal and or statistical scales because they inevitably suffer from the change-of-support problem : they all require complex inference of variables at values that have not been observed. One solution to the change-of-support problem is Bayesian hierarchical modelling.
A whole-system model of the bTB systems, capable of linking information throughout an ecological hierarchy, is required, and an example of what this model may look like, is presented in Figure 1. A particular limitation in the development of such a model is the ability to incorporate individual badger heterogeneities; individual traits are often ignored in disease models since detailed longitudinal datasets of individuals—such as the Woodchester Park dataset—within diseased populations are rare.
Within the badger bTB system, complex interactions among individual badger heterogeneities —such as between sex, inbreeding, disease and ageing —and among-individual variation in traits, act as proxies for infectiousness or “risk” , and are thought to drive fine-scale bTB dynamics. Fundamentally, an understanding of fine-scale disease processes in combination with Bayesian methodologies arms ecologists with the ability to parameterise previously unobservable processes, such as actuarial senescence . In addition to Hudson’s study, Bayesian analyses of the Woodchester badger population have contributed knowledge on the increased susceptibility of male badgers to bTB and on the diagnostic accuracies of badger bTB tests : information which improves our capability to model badger heterogeneities in the future. The importance of latent variables and individual heterogeneities to a “whole-system” model of wildlife disease is further discussed in the final section of this review.
Overall, the idea of achieving a better understanding of the bTB system in wildlife hosts using a whole-system method is not a new one. Analogous to the “whole-system” model suggested within this review, proposed the need for a novel modelling framework, and recommended a comprehensive epidemiological model. In addition, suggested that combining contact networks with Bayesian inference is the future direction for understanding wildlife epidemics. The inclusion of a hierarchy of scale within whole-system ecological models in general have been recommended by several authors .