Group testing in heterogeneous populations by using halving algorithms.
Group testing in heterogeneous populations by using halving algorithms.
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DOI：
10.1111/j.1467-9876.2011.01008.x
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发表时间：
2012-03-01
期刊：
影响因子：
--
通讯作者：
Tebbs JM
中科院分区：
文献类型：
--
作者：
Black MS;Bilder CR;Tebbs JM
群体（汇集）测试通常用于减少需要筛选大量个体的传染病或其他二元特征的测试总数。传统上，小组测试的研究假设每个人都是独立的，具有相同的积极性风险。最近，有越来越多的文献概括了以前在群体测试中的工作，包括异质性人群，使每个人都有不同的阳性风险。我们调查的效果，承认人口异质性的一个常用的组测试程序，这是众所周知的“减半”。对于该程序，将阳性组连续分成两个相等大小的半部分，直到所有组均检测为阴性或直到进行单独检测。我们发现，异质性不影响测试的平均数时，个人被随机分配到亚组。然而，当个人被分配到亚组的基础上，他们的风险概率，我们表明，我们提出的程序减少了测试的数量，利用异质性。这是说明了使用衣原体和淋病筛查数据从内布拉斯加州。
Group (pooled) testing is often used to reduce the total number of tests that are needed to screen a large number of individuals for an infectious disease or some other binary characteristic. Traditionally, research in group testing has assumed that each individual is independent with the same risk of positivity. More recently, there has been a growing set of literature generalizing previous work in group testing to include heterogeneous populations so that each individual has a different risk of positivity. We investigate the effect of acknowledging population heterogeneity on a commonly used group testing procedure which is known as ‘halving’. For this procedure, positive groups are successively split into two equal-sized halves until all groups test negatively or until individual testing occurs. We show that heterogeneity does not affect the mean number of tests when individuals are randomly assigned to subgroups. However, when individuals are assigned to subgroups on the basis of their risk probabilities, we show that our proposed procedures reduce the number of tests by taking advantage of the heterogeneity. This is illustrated by using chlamydia and gonorrhoea screening data from the state of Nebraska.
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