Investigating the impact of farmer behaviour and farmer-led control of infectious disease outbreaks in livestock

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The high density of livestock kept on farms means that they are often at risk of outbreaks of infectious diseases, which can spread rapidly both within and between farms. Examples that have affected the UK in recent years include bovine tuberculosis (bTB) and foot-and-mouth disease (FMD), while bovine viral diarrhoea virus (BVD) poses an emerging risk to the industry for the immediate future. For each of these, the general goal is to mitigate the impact of the disease (often by eradicating the infection from all UK farms) whilst attempting to minimise the total economic impact on the livestock industry. Control can be achieved in two main ways: either with prevention policies dictated by national agencies (for example the imposition of a national ban on movement of all livestock) or through preventive measures taken by the farmer (for example voluntary vaccination or tighter biosecurity). This project will determine the scenarios when farmers will take unilateral action or when national measures are required.We will develop a range of mathematical models that are able to predict the spread of infectious diseases and capture farmers' responses to the changing risks of infection. Models will range from relatively simple simulations that are designed to provide an understanding of the underlying mechanisms, to specific examples fitted to known diseases including bTB, FMD and BVD. These three diseases cover a range of transmission mechanisms and infection types: from slow endemic diseases like bTB, to rapid epidemics like FMD.A vitally important aspect for this project is robustly predicting the behaviour of farmers. This will also be refined as the project progresses: starting from the simple assumption that each farmer acts to perfectly minimise their expected costs, to including more realistic heterogeneous dynamics as determined by structured interviews with farmers. Using modern quantitative social-science approaches will allow us to analyse farmers' altruistic behaviour, level of trust and uptake of control. This will be coupled with elicitation to provide us with a set of distributions of behaviour and response to outbreaks that we will incorporate into our models, such that individual farmers will react differently, based upon their perceived risks and benefits as well as their sets of beliefs. This inclusion of farmer behaviour may modify the effectiveness of any nationally imposed control policy, and our predictions will therefore inform policy makers regarding how they should respond to outbreaks. The ultimate outcome will be a robust prediction of how important infectious diseases of livestock can be better controlled to minimise impact on both individual farmers and the livestock industry as a whole. In particular, we will investigate when and how national agencies can ensure active compliance of farmers with disease control regulations.Given the nature of this grant, communication with livestock policy makers, agricultural agencies and farmers is crucial. We will liaise closely with all relevant agencies throughout the project and provide access to simple graphical user interfaces (GUIs) for our suite of mathematical models that will allow stakeholders to visually assess the risks associated with livestock disease outbreaks and the role of multiple interventions.