Active pooling design in group testing based on Bayesian posterior prediction.

Abstract

For identifying infected patients in a population, group testing is an effective method to reduce the number of tests and correct test errors. In group testing, tests are performed on pools of specimens collected from patients, where the number of pools is lower than that of patients. The performance of group testing considerably depends on the design of pools and algorithms that are used for inferring the infected patients from the test outcomes. In this paper, an adaptive design method of pools based on the predictive distribution is proposed in the framework of Bayesian inference. The proposed method, executed using a belief propagation algorithm, results in more accurate identification of the infected patients compared with the group testing performed on random pools determined in advance.