Bayesian inference of infected patients in group testing with prevalence estimation

• URL: http://arxiv.org/abs/2004.13667v2
• Date: Sat, 13 Jun 2020 02:28:03 GMT
• Title: Bayesian inference of infected patients in group testing with prevalence estimation
• Authors: Ayaka Sakata
• Abstract summary: Group testing is a method of identifying infected patients by performing tests on a pool of specimens collected from patients. We show that the true-positive rate is improved by taking into account the credible interval of a point estimate of each patient.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Group testing is a method of identifying infected patients by performing tests on a pool of specimens collected from patients. For the case in which the test returns a false result with finite probability, we propose Bayesian inference and a corresponding belief propagation (BP) algorithm to identify the infected patients from the results of tests performed on the pool. We show that the true-positive rate is improved by taking into account the credible interval of a point estimate of each patient. Further, the prevalence and the error probability in the test are estimated by combining an expectation-maximization method with the BP algorithm. As another approach, we introduce a hierarchical Bayes model to identify the infected patients and estimate the prevalence. By comparing these methods, we formulate a guide for practical usage.

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