Group Testing in the Presence of Error with Application to HIV/AIDS

描述（由申请人提供）：汇集标本以提高筛查个体罕见疾病的效率有着悠久的历史，可以追溯到20世纪40年代在军事应征者中筛查梅毒。多年来，样本池或群体测试已应用于昆虫学，遗传学，国际贸易，制药和血库行业，计算机科学，分析化学和许多其他领域。在传染病的背景下，群体测试通常用于（i）病例识别，即，检测患有感兴趣疾病的所有个体，和（ii）流行率估计，即，估计群体中患有特定疾病的个体的比例。作为前者的一个例子，目前国家的血库和北卡罗来纳州的公共HIV检测系统采用样本池来检测最近感染HIV的个体。团体测试可以比个人测试更有效，更准确，更精确;然而，由于大量和多样化的团体测试文献，许多支持使用团体测试的结果在传染病环境中未被认可。我们建议研究群体检验的统计方面，并将其应用于艾滋病毒/艾滋病和其他传染病的病例识别和流行估计。我们的具体目标是推导和比较的操作特性的多级和基于阵列的组测试算法的识别和估计问题中存在的测试误差。检验误差模型将考虑常数和样本池大小依赖性灵敏度和特异性。我们假设，阵列测试算法上级传统的多级测试算法的效率和准确性。虽然该方法的动机是检测急性艾滋病毒，我们的结果将直接适用于其他传染病。

DESCRIPTION (provided by applicant): Pooling of specimens to increase efficiency of screening individuals for rare diseases has a long history, dating back to screening of syphilis in military inductees in the 1940s. Over the years, specimen pooling or group testing has been applied in entomology, genetics, international trade, the pharmaceutical and blood bank industries, computer science, analytical chemistry, and many other areas. In the context of infectious diseases, group testing is typically used for (i) case identification, i.e., detecting all individuals having the disease of interest, and (ii) prevalence estimation, i.e., estimating the proportion of individuals in the population having a particular disease. As an example of the former, currently the nation's blood banks and the state of North Carolina's public HIV testing system employ specimen pooling to detect individuals recently infected with HIV. Group testing can be more efficient, accurate, and precise than individual testing; yet, many of the results supporting the use of group testing have gone unrecognized in the infectious disease setting owing to the vast and diverse group testing literature. We propose to research statistical aspects of group testing with application to case identification and prevalence estimation in HIV/AIDS and other infectious diseases. Our specific aims are to derive and compare the operating characteristics of multistage and array based group testing algorithms for both the identification and estimation problems in the presence of test error. Models for test error will consider constant and pool size dependent sensitivity and specificity. We hypothesize that array testing algorithms are superior to conventional multistage testing algorithms in terms of efficiency and accuracy. While the methodology is motivated by detection of acute HIV, our results will be directly applicable to other infectious diseases.