Screening for sexually transmitted diseases has benefitted greatly from the use of group testing (pooled testing) to lower costs. With the development of assays that detect multiple infections, screening practices now involve testing pools of individuals for multiple infections simultaneously. Building on the research for single infection group testing procedures, we examine the performance of group testing for multiple infections. Our work is motivated by chlamydia and gonorrhea testing for the Infertility Prevention Project (IPP), a national program in the United States. We consider a two-stage pooling algorithm currently used to perform testing for the IPP. We first derive the operating characteristics of this algorithm for classication purposes (e.g., expected number of tests, misclassication probabilities, etc.) and identify pool sizes that minimize the expected number of tests. We then develop an expectation-maximization algorithm to estimate probabilities of infection using both group and individual retest responses. Our research shows that group testing can oer large cost savings when classifying individuals for multiple infections and can provide prevalence estimates that are
actually more ecient than those from individual testing.
More information on Chris McMahan may be found at http://people.clemson.edu/~mcmaha2/index.html