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

农场饲养的牲畜密度高，这意味着它们经常面临传染病爆发的风险，这种疾病可以在农场内部和农场之间迅速传播。近年来影响英国的例子包括牛结核病（bTB）和口蹄疫（FMD），而牛病毒性腹泻病毒（BVD）在不久的将来对该行业构成了新的风险。对于其中的每一个，总体目标是减轻疾病的影响（通常是通过根除所有英国农场的感染），同时尽量减少对畜牧业的总体经济影响。控制可以通过两种主要方式实现：要么通过国家机构规定的预防政策（例如在全国范围内禁止所有牲畜的流动），要么通过农民采取的预防措施（例如自愿接种疫苗或加强生物安全）。该项目将确定农民采取单边行动或需要采取国家措施的情景。我们将开发一系列能够预测传染病传播的数学模型，并捕捉农民对不断变化的感染风险的反应。模型的范围将从旨在提供对潜在机制的理解的相对简单的模拟，到适合于包括bTB、口蹄疫和BVD在内的已知疾病的具体示例。这三种疾病涵盖了一系列传播机制和感染类型：从结核等慢流行疾病到口蹄疫等快速流行疾病。该项目的一个至关重要的方面是可靠地预测农民的行为。随着项目的进展，这也将得到完善：从每个农民的行为都完美地最小化他们的预期成本的简单假设开始，到包括更现实的异质动态，这是通过与农民的结构化访谈确定的。使用现代定量社会科学方法将使我们能够分析农民的利他行为、信任水平和对控制的接受。这将与启发相结合，为我们提供一套行为分布和对疫情的反应，我们将把这些分布和反应纳入我们的模型，这样，每个农民将根据他们感知到的风险和利益以及他们的信念做出不同的反应。将农民行为纳入其中可能会改变任何国家实施的控制政策的有效性，因此，我们的预测将为决策者提供有关如何应对疫情的信息。最终的结果将是一个强有力的预测，可以更好地控制牲畜传染病的重要性，从而最大限度地减少对个体农民和整个畜牧业的影响。特别是，我们将调查国家机构何时以及如何确保农民积极遵守疾病控制条例。鉴于这笔赠款的性质，与畜牧业决策者、农业机构和农民的沟通至关重要。在整个项目过程中，我们将与所有相关机构密切联系，并为我们的数学模型套件提供简单的图形用户界面（gui），使利益相关者能够直观地评估与牲畜疾病爆发相关的风险以及多种干预措施的作用。

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.