Active pooling design in group testing based on Bayesian posterior prediction

TL;DR

This paper proposes an adaptive Bayesian group testing method using predictive distribution and belief propagation to improve the accuracy of identifying infected patients while reducing tests.

Contribution

It introduces a novel pool design approach based on Bayesian posterior prediction, enhancing group testing efficiency and accuracy over random pool methods.

Findings

Improved accuracy in identifying infected patients.

Reduced number of tests needed.

Effective adaptive pool design demonstrated.

Abstract

In identifying infected patients in a population, group testing is an effective method to reduce the number of tests and correct the test errors. In the group testing procedure, 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 heavily depends on the design of pools and algorithms that are used in 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 the belief propagation algorithm results in more accurate identification of the infected patients, as compared to the group testing performed on random pools determined in advance.