Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Barrack Road, Weymouth, Dorset DT4 8UB, United Kingdom.
Prev Vet Med. 2013 Oct 1;112(1-2):13-26. doi: 10.1016/j.prevetmed.2013.07.008. Epub 2013 Aug 12.
Over recent years there have been considerable methodological developments in the field of animal disease surveillance. The principles of risk analysis were conceptually applied to surveillance in order to further develop approaches and tools (scenario tree modelling) to design risk-based surveillance (RBS) programmes. In the terrestrial animal context, examples of risk-based surveillance have demonstrated the substantial potential for cost saving, and a similar benefit is expected also for aquatic animals. RBS approaches are currently largely absent for aquatic animal diseases. A major constraint in developing RBS designs in the aquatic context is the lack of published data to assist in the design of RBS: this applies to data on (i) the relative risk of farm sites becoming infected due to the presence or absence of a given risk factor; (ii) the sensitivity of diagnostic tests (specificity is often addressed by follow-up investigation and re-testing and therefore less of a concern); (iii) data on the variability of prevalence of infection for fish within a holding unit, between holding units and at farm level. Another constraint is that some of the most basic data for planning surveillance are missing, e.g. data on farm location and animal movements. In Europe, registration or authorisation of fish farms has only recently become a requirement under EU Directive 2006/88. Additionally, the definition of the epidemiological unit (at site or area level) in the context of aquaculture is a challenge due to the often high level of connectedness (mainly via water) of aquaculture facilities with the aquatic environment. This paper provides a review of the principles, methods and examples of RBS in terrestrial, farmed and wild animals. It discusses the special challenges associated with surveillance for aquatic animal diseases (e.g. accessibility of animals for inspection and sampling, complexity of rearing systems) and provides an overview of current developments relevant for the design of RBS for fish diseases. Suggestions are provided on how the current constraints to applying RBS to fish diseases can be overcome.
近年来，动物疾病监测领域取得了相当多的方法学发展。风险分析的原则在概念上被应用于监测，以进一步发展基于风险的监测（RBS）计划的方法和工具（情景树建模）。在陆生动物方面，基于风险的监测的例子已经证明了在节省成本方面有很大的潜力，预计在水生动物方面也会有类似的好处。目前，水生动物疾病的基于风险的监测方法还很少。在水生环境中开发 RBS 设计的一个主要限制是缺乏可用于设计 RBS 的已发表数据：这适用于以下方面的数据：(i) 由于给定风险因素的存在或不存在，农场地点因感染而感染的相对风险；(ii) 诊断测试的敏感性（特异性通常通过后续调查和重新测试来解决，因此不太关注）；(iii) 感染鱼在一个养殖单位内、养殖单位之间和养殖场水平的流行率的变异性数据。另一个限制是，规划监测所需的一些最基本的数据缺失，例如关于养殖场位置和动物流动的数据。在欧洲，根据欧盟指令 2006/88，最近才要求对鱼类养殖场进行注册或授权。此外，由于水产养殖设施与水生环境之间通常存在高度的连通性（主要通过水），因此在水产养殖背景下，对流行病学单位（在站点或区域层面）的定义是一个挑战。本文综述了陆生、养殖和野生动物中 RBS 的原则、方法和实例。它讨论了与水生动物疾病监测相关的特殊挑战（例如，动物检查和采样的可及性、养殖系统的复杂性），并概述了与鱼类疾病 RBS 设计相关的当前发展。就如何克服将 RBS 应用于鱼类疾病的当前限制提出了建议。
