Audigé L, Doherr M G, Hauser R, Salman M D
Institute of Virology and Immunoprophylaxis, CH-3147, Mittelhäusern, Switzerland.
Prev Vet Med. 2001 Apr 13;49(1-2):1-17. doi: 10.1016/s0167-5877(01)00182-9.
Traditionally, the planning of surveys (in particular, sample-size calculations) has relied on assumptions including the assumption of perfect screening tests. This paper presents a novel approach that can be used for planning animal-health surveys and interpreting screening-test results in the context of these surveys. A stochastic simulation model developed to assess the properties of herd-level sampling schemes and surveys has been adapted for large surveys aimed at substantiating freedom from infection at a national or regional level. We use a Bayesian approach to derive the post-survey probability of freedom from infection from the pre-survey probability of freedom and the likelihood ratio that is associated with screening-test results. We applied the model to two consecutive surveys conducted in 1998 and 1999 in Switzerland to substantiate freedom from infectious bovine rhinotracheitis (IBR) in the cattle population of about 56000 herds (median herd size of 15 cattle > 2 yr of age in 1999). In 1998, serum samples were taken from five cattle > 2 yr in 4672 herds, and in 1999 from all cattle > 2 yr old in 648 herds; samples were analysed by ELISA. The survey of 1999 provided less evidence than that of 1998 to support a status of freedom from infection; also, the characteristics of both herd-level sampling schemes were similar. We argue that the rationale for survey planning depends on the pre-survey probability of freedom from infection (i.e. our level of confidence that the infection does not occur in the targeted animal population). In consequence, surveys should be tailored to individual populations in the respective countries or regions. The model has been developed in an Excel spreadsheet to allow flexibility of use, and adaptation to many other animal-health issues.
传统上，调查规划（尤其是样本量计算）依赖于包括完美筛查试验假设在内的各种假设。本文提出了一种新颖的方法，可用于规划动物健康调查，并在这些调查的背景下解释筛查试验结果。一个为评估畜群水平抽样方案和调查的特性而开发的随机模拟模型，已被改编用于旨在证实国家或地区层面无感染的大型调查。我们使用贝叶斯方法，根据调查前的无感染概率和与筛查试验结果相关的似然比，推导出调查后的无感染概率。我们将该模型应用于1998年和1999年在瑞士进行的两项连续调查，以证实约56000个畜群（1999年2岁以上牛的畜群规模中位数为15头）的牛群中无传染性牛鼻气管炎（IBR）感染。1998年，从4672个畜群中的5头2岁以上的牛采集血清样本，1999年从648个畜群中的所有2岁以上的牛采集样本；样本通过酶联免疫吸附测定法（ELISA）进行分析。1999年的调查比1998年的调查提供的支持无感染状态的证据更少；此外，两种畜群水平抽样方案的特征相似。我们认为，调查规划的基本原理取决于调查前的无感染概率（即我们对目标动物群体中不发生感染的信心水平）。因此，调查应针对各个国家或地区的特定群体进行量身定制。该模型已在Excel电子表格中开发，以实现使用的灵活性，并适用于许多其他动物健康问题。
