Efficient Bayesian modelling of infectious diseases in wildlife

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Infectious diseases of wildlife result in significant welfare and conservation costs to wild animal populations. For example, chytridiomycosis is a fungal pathogen driving the mass extinction of numerous amphibian species worldwide, putting at risk >10% of all vertebrate species; while white-nose syndrome is estimated to have caused >6 million bat deaths in North America by 2012 alone. Diseases in wildlife can also have significant impacts on agriculture. For example, bovine tuberculosis (bTB) is a notifiable disease in livestock, which costs the UK government over £100 million per year in terms of testing and compensation for slaughtered animals, and also has huge impacts on the livelihoods of farmers. The pathogen has a wide host range, which includes protected species such as badgers, and currently, bTB is the subject of highly controversial badger culling trials aimed at attenuating disease transmission to livestock. Wildlife infectious diseases also represent a considerable threat to humans, as emerging zoonoses such as Ebola, Zika, West Nile virus, HIV and plague all attest to. In short, the list of WID outbreaks is long and growing, and we desperately need better tools to study their epidemiology.Mathematical modelling provides tools that enable us to better understand infectious disease dynamics, and can thus be used to help inform management strategies. However, the use of mathematical models without robustly fitting to observed data can lead to poor model predictions and inference, in turn hindering scientific enquiry and increasing the probability of making poor policy decisions. Fitting dynamic transmission models to observed data is highly challenging, since available data is incomplete, and thus standard statistical approaches that rely on estimation of the likelihood function cannot be employed.We will extend recent advances in simulation-based Bayesian inference methods, which have shown great utility in overcoming these difficulties. These approaches are flexible and tractable, but can be computationally demanding. This project will extend recent advances in the field to deal with key challenges, both in the scaling up of these methods to larger systems, and also in dealing with the complexities that typify wildlife disease systems, such as: incomplete longitudinal sampling of individuals (i.e. capture-mark-recapture), the application of multiple diagnostic tests, uncertainties in diagnostic test performance, complex spatial and meta-population structures, and demographic changes over time. We will explore the development of constrained simulation techniques, which have been shown to greatly improve the efficiency of these inference algorithms in small populations, and hence are good candidates for improving efficiency in larger, more complex populations. We will also explore the use of these algorithms to allow for the fitting and comparison of different transmission models, again extending recent work in the field. We will ground our research using the high-profile case study of bovine tuberculosis in badgers, which suffers from all of the system-uncertainty and data-quality issues described above. Additionally, the disease has a direct impact on the livelihoods of UK farmers, major policy decisions that influence voter behaviour, and the conservation and management of UK wildlife. We will use an unprecedented 40+ year longitudinal study of bTB in a natural, wild population of badgers, to provide a unique and powerful insight into the aetiology of the disease. Although we focus on wildlife disease systems in this project, the methodological advances developed will be applicable to a wider range of state-space systems.