Use of contact structures for the control of infectious diseases in the British aquaculture industry

传染性鱼类疾病对英国水产养殖业构成持续威胁。到目前为止，英国很幸运地避免了病毒性出血性败血症、棘轮虫和传染性造血坏死等主要传染病的大规模爆发。然而，这些疾病在欧洲部分地区普遍存在，可能对英国水产养殖产生严重的经济影响。这个研究项目的目的是发展了解、预防和控制这些疾病和其他传染病爆发的能力。与苏格兰海洋和环境、渔业和水产养殖科学中心（Cefas）的项目伙伴合作，我们将吸收来自英格兰、苏格兰和威尔士的关于英国水产养殖业结构和运作的现有详细信息，将其纳入整个英国养鱼场和渔业之间疾病传播途径的第一个单一网络。该结构将使用网络理论的现代方法进行分析，以确定将有助于并告知设计控制和预防鱼类疾病流行的最佳策略的特性。特别是，通过在这一现实网络上运行大小流行病的计算机模拟，我们将确定各种行为并测试可能的干预措施的效力。以类似天气预报的方式，运行数百万次模拟将使我们能够测试可能爆发情景的全部范围，并确定其一般可能性，以及分析源自我们选择的任何特定站点或站点集群的高风险传播动态。此外，这个“数字实验室”使我们能够探索和调整广泛的现有和新的遏制、缓解和根除政策，这些措施包括从特定地点的控制，通过当地接触者追踪，到全国性措施（例如，针对利益攸关方的特定症状的宣传运动），同时施加现实的能力限制。对干预措施的分析也会产生相关的经济成本，因为参与一项与传染病本身一样昂贵的干预战略显然会适得其反。这里的总体目标不仅仅是探索和分析，而且，更重要的是，创建一个实用、灵活的专家系统，该系统可以提供（缓慢变化的）网络架构、病原体特性和任何新爆发的最新数据，并能够对传播动态进行准确的实时风险评估，以及能够在特定地点和环境下提出最有效的应对策略。最后，鱼类病原体的一个不寻常的特性是，它们的特性和影响在感染性、感染引起可识别症状的程度和鱼类死亡率方面往往依赖于温度。因此，这项工作的一个重要目标是将农场特定的温度数据纳入网络上流行病传播的表示中。这是跨多个尺度对系统进行建模的更广泛战略的一部分，这样其他特定于农场的数据（如规模、类型和产量）可以输入到模型中。这样的农场级子模型将很容易改变，以最大限度地提高模拟器对未来研究的适应性。在这方面，我们的具体目标是解决气候变化（数年至数十年）在水温升高影响鱼类疾病流行方面的影响。据我们所知，没有人调查过这些对英国水产养殖业未来影响的规模和严重程度，我们需要（更好地）做好准备。因此，本项目旨在为英国水产养殖业的实地专业知识、科学见解和数据建立一个持久的基础，以保障其未来在面临持续的流行病威胁时取得成功。

Infectious fish diseases present an ongoing threat to the British Aquaculture Industry. Britain has so far been fortunate in avoiding large-scale outbreaks of major infectious diseases such as Viral Haemorrhagic Septicaemia, Gyrodactylus salaris and Infectious Haematopoietic Necrosis. However, these diseases are prevalent in parts of Europe and could potentially have serious economic implications for British Aquaculture. This research project aims to develop the capacity to understand, prevent and control outbreaks of these and other infectious diseases. Working with project partners at Marine Scotland and the Centre for Environment, Fisheries & Aquaculture Science (Cefas), we will assimilate existing detailed information from England, Scotland and Wales on the structure and operation of the British Aquaculture industry into the first single network of disease transmission routes between fish farms and fisheries throughout Britain. This structure will be analysed using modern methods from network theory to identify properties that will assist and inform the design of optimal strategies for the control and prevention of fish disease epidemics. In particular, by running computer simulations of large and small epidemics upon this realistic network, we will identify the full range of behaviours and test the efficacy of possible interventions. Acting in a similar way to weather forecasting, running millions of simulations will enable us to test the full breadth of possible outbreak scenarios, and establish their general likelihoods, as well as profiling high-risk spreading dynamics originating at any specific site or site cluster we choose. In addition, this "numerical laboratory" lets us explore and fine-tune a broad range of existing and novel containment, mitigation, and eradication policies These measures range from site-specific controls, through local contact tracing, to nationwide measures (e.g., a stakeholder-targeted awareness campaign of specific symptoms to look out for), while simultaneously imposing realistic capacity constraints. Analysis of interventions will also have an associated economic costing, as it would clearly be counter-productive to engage in an intervention strategy that was as costly as the infectious disease itself. The overall aim here is not just exploratory and analytical, but, moreover, to create a practical, flexible expert system that can be fed the latest data on the (slowly changing) network architecture, pathogen properties, and any emerging outbreak, and be able to produce accurate real-time risk assessments of spreading dynamics, as well as being able to suggest the most effective counter-strategies given specific locations and circumstances.Finally, an unusual property of fish pathogens is that their properties and effects are often temperature-dependent in terms of their infectivity, the extent infections cause recognisable symptoms, and fish mortality. An important goal of this work is therefore to include farm-specific temperature data into the representation of epidemic spread on the network. This is part of a wider strategy to model the system across multiple scales whereby other farm-specific data such as size, type and production can feed into the model. Such farm-level sub-models will be readily changeable to maximise adaptability of the simulator for future research. In this respect, we specifically aim to address the effects of climate change (over years to decades) in terms of higher water temperatures impacting fish disease epidemics. As far as we are aware, no-one has ever investigated the scale and severity of these future impacts on the British Aquaculture industry, and we need to be (better) prepared. This project thus aspires to create a durable foundation of field expertise, scientific insights, and data on the British Aquaculture Industry, to safeguard its future success in the face of ongoing epidemic threats.