Group testing for infectious disease detection: multiplex assays and back-end screening

Project Summary/AbstractTesting individuals for infectious diseases is important for disease surveillance and for ensuring the safety of blooddonations. When faced with questions on how to test as many individuals as possible and still operate withinbudget limits, public health of?cials are increasingly turning toward the use of group testing (pooled testing). Inthese applications, individual specimens (such as blood or urine) are combined to form a single pooled specimenfor testing. Individuals within negative testing pools are declared negative. Individuals within positive testingpools are retested in some predetermined algorithmic manner to determine which individuals are positive andwhich individuals are negative. For low disease prevalence settings, this innovative testing process leads to feweroverall tests, which subsequently lowers costs, when compared to testing specimens individually. Previous research in group testing has focused largely on testing for infections, such as HIV and chlamydia,one at a time. However, motivated by the development of new technology, disease testing practices are movingtowards the use of multiplex assays that detect multiple infections at once. This research proposal presents the?rst comprehensive extensions of group testing to a multiplex assay setting. The ?rst goal is to develop newgroup testing strategies that allow for multiplex assays to be used in sexually transmitted disease testing andblood donation screening applications. This will allow laboratories to obtain the maximum possible cost savingsthrough proper applications of group testing. The second goal is to develop new group testing strategies toincrease the classi?cation accuracy?both with single and multiple infections?in these same applications. This willbe done by performing directed con?rmatory testing after individuals are initially classi?ed as positive or negative.An overarching theme of this research is to acknowledge individual risk factors by incorporating them into thegroup testing process. In terms of biostatistical innovation, this research involves developing new classi?cationand Bayesian modeling procedures for correlated latent-variable data.