Prof Rowland Kao, University of Edinburgh: Combining genomics and epidemiology to analyse bi-directional transmission of Myocbacterium bovis for cattle and badgers in Great Britain.

Quantifying pathogen transmission in multi-host systems is difficult, but crucial for disease control. In a well known example, the agent of bovine Tuberculosis (bTB), Mycobacterium bovis, persists in cattle populations in Britain and around the world, often where potential wildlife reservoirs are present. However, the relative contribution of different host species to bTB persistence is generally unknown. In Britain the role of badgers in the persistence of infection in cattle is highly contentious, despite decades of research and control efforts. Here, we apply Bayesian phylogenetic and machine-learning approaches to bacterial genome data to quantify the role of badgers and cattle in M. bovis infection dynamics in an endemic area. Our results are consistent with a maintenance role of the sampled badger population and if representative, suggest control operations targeting cattle and badgers simultaneously are required. We provide the first directional estimates of inter-species transmission rates for M. bovis and demonstrate how combining genomics and epidemiology with evolutionary and machine-learning approaches informs the study of multi-host pathogen systems.