The demands to simultaneously mitigate climate change, enhance biodiversity and maintain food security, mean that environmental land management policies are likely to result in substantial changes to our landscape. In turn, these changes will influence disease transmission risks, with increased contact amongst wildlife, humans and livestock likely to exacerbate the success of existing pathogens that can infect multiple hosts, and result in emergent disease threats. Such rapid systemic changes also mean that better methods to assess enhanced and emergent disease outbreaks are in urgent need, as our ability to mitigate threats require us to identify changes in disease risk and ‘disease ecology’ at an early stage, before the changes become established.

One important example is animal Tuberculosis in British and Irish cattle and wildlife (aTB, caused by Mycobacterium bovis), where there are many excellent, dense datasets ideal for developing new methods and insights. Importantly, changing land management policies are likely to change both the way we manage livestock and the range and contact patterns of the Eurasian badger, the most prominent wildlife host. It may also increase the role of other potential aTB hosts, including wild deer, for which there is increasing evidence of infection in GB as is already true in other countries.

In this project, the student will join a well established research team in order to develop new approaches to using combinations of evolutionary, ecological and epidemiological data, with the aim that these methods could be used to understand emergent infection problems. The student will exploit a combination of existing datasets and novel ones on the circulation of M. bovis in British cattle and wildlife. They will develop analytical tools with the aim of rapidly identifying changes in the underlying ecology of disease circulation. In this case, when the relative roles of cattle, badgers and other wildlife are showing evidence of changing in importance. The project has the potential to inform appropriate targeting of future control of M. bovis transmission in cattle and wildlife.

The ideal applicant will have good evidence of mathematics or statistics skills and preferably experience of computer programming. The student will develop a mix of data analysis skills, including machine learning, network analysis and Bayesian statistical analysis, applied to the increasingly important field of ‘phylodynamics’ where the aim is to use a combination of pathogen genetic and epidemiological data to understand pathogen circulation and therefore inform control.