Bilateral BBSRC-SFI: Tackling a multi-host pathogen problem - phylodynamic analyses of the epidemiology of M. bovis in Britain and Ireland

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Diseases that infect more than one host species can be particularly difficult to control, and well known examples include avian influenza (in wild birds, poultry and humans), rabies (in dogs and humans), Brucellosis (in livestock and humans) and Ebola virus (in primates and humans). If one or more of those is a wildlife species, control can prove particularly difficult. Wildlife are harder to observe, harder to access for purposes of disease control, and often we need to counterbalance the requirements of disease control with the needs of wildlife conservation. For all these reasons, identifying the root causes of disease transmission and quantifying the impact of disease control is a challenging problem in these 'multi-host' systems, a problem that is exacerbated when human management results in ecological disturbances that in themselves increase disease risk. An increasingly important tool in disentangling potential sources and routes of transmission is the deployment of mass "whole genome sequencing" of the causative agent of disease, taken from infected individuals. By tracking changes in the genetic code of the disease agent as it passes from individual to individual, and combining them with computer models that take into account other information we have on the transmission of disease (e.g. who was in contact with whom, and when, and how long individuals are infectious for) these data provide the best opportunity to identify 'who infected whom' - if not on the individual level, then at least at a level that is impossible without this kind of information. Importantly, it helps us to identify how important the different species are in these multi-species systems and also helps us to best identify how to control the disease. However, because these technologies and these uses of them are still relatively new, it is important to have good, well studied systems on which we can test and understand how best to use them.One disease problem which exhibits all these characteristics, has exceptional information and also represents an important problem in itself is bovine Tuberculosis (bTB) in cattle and badgers. BTB is estimated to cost the UK about £100 million per year, results in thousands of cattle slaughtered every year, and is a zoonotic risk to farmers, veterinarians and in particular individuals with compromising existing infections (e.g. HIV/AIDS). Badgers are known to be involved in the transmission of the disease to cattle and without some form of badger-targeted disease control, it will be impossible to eradicate. With any effective vaccine still many years away, badger culling is an important potential means of control but because badgers are a protected and much-loved species, it is highly controversial. This controversy is made worse by conflicting evidence regarding the value of culling, with trial culls in England suggesting that it induces a social 'perturbation effect' that makes culling impractical, while trials in the Republic of Ireland indicating it can be effective. In this project, we shall aim to build upon existing work and generate sequences for bTB in Irish cattle and badgers, taking advantage of the exceptional record they have of their badger population. Using mathematical models based on principles of 'social networks' to help us understand these data, we aim to contrast the control of bTB in Ireland, where badger culling has long been extensively used, with Northern Ireland and England, where it is not. This will allow us to estimate the potential benefit, if any, that badger culling could play in England, and the potential impact should culling efforts cease in Ireland. Thus this project will be of both immediate benefit to the control of bTB in cattle, but also have long term benefit in developing new approaches and insights that will improve our conceptual understanding of multi-host diseases, and the role that ecological disturbance plays in zoonotic disease emergence and spread.