Integrated modelling of demography, transmission and epidemiology of bovine tuberculosis in badgers

Wild badgers are an important reservoir of bovine tuberculosis in the United Kingdom. This wildlife-disease interaction is of massive economic importance to the UK livestock industry, yet we still lack the ecological and epidemiological tools to better understand how to manage it. We propose a modelling project that uses 30 years of information on badger and tuberculosis population dynamics, collected by the National Wildlife Management Centre. We aim to create a working model of badger demography and TB prevalence that can actually predict future dynamics, and predict the impacts of relevant management strategies, for example targetted badger culls or vaccination campaigns. Even when badgers and TB are mointored intensively, there remains a great deal of uncertainty in our understanding: the diagnostic tests used to identify TB infections vary in their sensitivity (their ability to detect infection) and specificity (their ability to distinguish TB infection from other diseases); badgers may avoid capture; badgers move among social groups, can roam widely, and may mate with distant individuals. All of this uncertainty is best handled using new modelling techniques called Bayesian Integrated Population Models. We have already created a framework for these models, using the Woodchester Park badger dataset. We now aim to:
(1) Model uncertainty in the sensitivity and specificity of the diagnostic tests used during the 30 year time series;
(2) Integrate this uncertainty into a full model of badger demography (age- and sex-specific survival and reproduction; disease transmission and disease-induced mortality; disease prevalence), and test this model's ability to predict system dynamics over the last decade.
(3) When we have a useful predictive model, we aim to test the efficacy of various management scenarios, including culling and vaccination regimes.

Overall, this project represents the first attempt to properly consider uncertainty in models of badgers and TB, and will yield the best evidence base for optimal control of bovine tuberculosis in the wildlife reservoir.

The PhD student will work in collaboration between the Centre for Ecology and Conservation and the College of Engineering, Mathematics and Physics at the University of Exeter, and the National Wildlife Management Centre, a section of DEFRA's Animal Health and Veterinary Laboratory Association.