de Vos C J, Saatkamp H W, Ehlers J
Business Economics Group, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, The Netherlands.
Prev Vet Med. 2007 Nov 15;82(1-2):123-37. doi: 10.1016/j.prevetmed.2007.05.009. Epub 2007 Jul 5.
Results of serological monitoring for Salmonella in finishing pigs are used to classify herds and target control measures at herds with high prevalence. The outcome of monitoring is determined by three factors: (a) the cut-off value for the optical density percentage (OD%) to declare a sample positive, (b) the classification scheme to allocate farms to different Salmonella prevalence classes, and (c) the annual number of samples per herd to calculate its Salmonella prevalence. Our goal was to analyse the impact of these three factors on (i) the accuracy of Salmonella monitoring in finishing pigs and (ii) the total number of tests required. We constructed a stochastic simulation model in Excel and @Risk to evaluate 12 monitoring scenarios based on: (a) four cut-off values for the OD% (10, 20, 30, and 40) and (b) three herd classification schemes. Furthermore, eight different sampling schemes were evaluated. The main outputs of the model are (a) the accuracy of monitoring which is reflected by the percentage of herds that retain classification when re-sampled at the same moment in time and (b) the total number of tests. To illustrate the model, we used input data from Salmonella monitoring in Lower Saxony, Germany. Model calculations demonstrated that - with the tests in use - monitoring scenarios based on cut-off OD% 10 are most accurate with 80-90% of herds retaining classification. Monitoring scenarios based on cut-off OD% 20 or 30 are, however, comparable to those based on cut-off OD% 40 with 50-70% of herds retaining classification. Besides, we predicted that herd classifications based on three classes (low-, moderate-, and high-prevalence) give more accurate results than when a zero-prevalence class is included. The total number of tests depends heavily on the sampling scheme and - if sampling is based on Salmonella prevalence class - the distribution of herds over the different classes. We predicted that the current German sampling scheme that is based on herd size requires more tests than those sampling schemes based on herd classification. Of these, the sampling scheme in which most samples are taken from high-prevalence herds is most accurate and might be a good incentive to reduce Salmonella prevalence at herd level if farmers had to pay for the tests themselves.
育肥猪沙门氏菌血清学监测结果用于对猪群进行分类，并针对高流行率猪群采取针对性的控制措施。监测结果由三个因素决定：(a) 判定样本为阳性的光密度百分比（OD%）临界值；(b) 将猪场分配到不同沙门氏菌流行率等级的分类方案；(c) 每个猪群每年用于计算其沙门氏菌流行率的样本数量。我们的目标是分析这三个因素对 (i) 育肥猪沙门氏菌监测准确性以及 (ii) 所需检测总数的影响。我们在Excel和@Risk中构建了一个随机模拟模型，基于 (a) OD%的四个临界值（10、20、30和40）以及 (b) 三种猪群分类方案评估12种监测方案。此外，还评估了八种不同的抽样方案。该模型的主要输出结果为：(a) 监测准确性，通过在同一时间重新采样时保持分类的猪群百分比来反映；(b) 检测总数。为了说明该模型，我们使用了德国下萨克森州沙门氏菌监测的输入数据。模型计算表明，使用现有检测方法时，基于OD%临界值10的监测方案最为准确，80 - 90%的猪群保持分类。然而，基于OD%临界值20或30的监测方案与基于OD%临界值40的方案相当，50 - 70%的猪群保持分类。此外，我们预测基于三个等级（低、中、高流行率）的猪群分类比包含零流行率等级时能给出更准确的结果。检测总数在很大程度上取决于抽样方案，并且 - 如果抽样基于沙门氏菌流行率等级 - 还取决于不同等级猪群的分布情况。我们预测，当前基于猪群规模的德国抽样方案比基于猪群分类的抽样方案需要更多检测。其中，从高流行率猪群采集最多样本的抽样方案最为准确，如果农民需要自行支付检测费用，这可能是降低猪群水平沙门氏菌流行率的一个良好激励措施。
