Jordan David
New South Wales Department of Primary Industries, Wollongbar Agricultural Institute, New South Wales 2477, Australia.
Prev Vet Med. 2005 Aug 12;70(1-2):59-73. doi: 10.1016/j.prevetmed.2005.02.013. Epub 2005 Apr 2.
Samples from livestock or food items are often submitted to microbiological analysis to determine whether or not the group (herd, flock or consignment) is shedding or is contaminated with a bacterial pathogen. This process is known as 'herd testing' and has traditionally involved subjecting each sample to a test on an individual basis. Alternatively one or more pools can be formed by combining and mixing samples from individuals (animals or items) and then each pool is subjected to a test for the pathogen. I constructed a model to simulate herd-level sensitivity of the individual-sample approach (HSe) and the herd-level sensitivity of the pooled-sample approach (HPSe) of tests for detecting pathogen. The two approaches are compared by calculating the relative sensitivity (RelHSe = HPSe/HSe). An assumption is that microbiological procedures had 100% specificity. The new model accounts for the potential for HPSe and RelHSe to be reduced by the dilution of pathogen that occurs when contaminated samples are blended with pathogen-free samples. Key inputs include a probability distribution describing the concentration of the pathogen of interest in samples, characteristics of the pooled-test protocol, and a 'test-dose-response curve' that quantifies the relationship between concentration of pathogen in the pool and the probability of detecting the target organism. The model also compares the per-herd cost of the pooled-sample and individual-sample approaches to herd testing. When applied to the example of Salmonella spp. in cattle feces it was shown that a reduction in the assumed prevalence of shedding can cause a substantial fall in HPSe and RelHSe. However, these outputs are much less sensitive to changes in prevalence when the number of samples per pool is high, or when the number of pools per herd-test is high, or both. By manipulating the number of pools per herd and the number of samples per pool HPSe can be optimized to suit the range of values of true prevalence of shedding of Salmonella that are likely to be encountered in the field.
家畜或食品样本通常会被送去进行微生物分析，以确定该群体（畜群、禽群或货物批次）是否正在排出或被细菌病原体污染。这个过程被称为“群体检测”，传统上是对每个样本进行单独检测。或者，可以通过将个体（动物或物品）的样本合并和混合来形成一个或多个样本池，然后对每个样本池进行病原体检测。我构建了一个模型来模拟检测病原体的个体样本方法的群体水平敏感性（HSe）和合并样本方法的群体水平敏感性（HPSe）。通过计算相对敏感性（RelHSe = HPSe/HSe）来比较这两种方法。假设微生物检测程序具有100%的特异性。新模型考虑了在受污染样本与无病原体样本混合时，病原体稀释可能导致HPSe和RelHSe降低的可能性。关键输入包括描述样本中目标病原体浓度的概率分布、合并检测方案的特征以及一条“检测剂量反应曲线”，该曲线量化了样本池中病原体浓度与检测到目标生物体的概率之间的关系。该模型还比较了合并样本和个体样本方法在群体检测中的每群体成本。当应用于牛粪便中沙门氏菌属的例子时，结果表明，假设的排出患病率降低会导致HPSe和RelHSe大幅下降。然而，当每个样本池中的样本数量较多，或每次群体检测中的样本池数量较多，或两者都多时，这些输出对患病率变化的敏感性要低得多。通过控制每个群体中的样本池数量和每个样本池中的样本数量，可以优化HPSe，以适应在实际中可能遇到的沙门氏菌排出真实患病率的取值范围。
