# Bayesian inference of infected patients in group testing with prevalence estimation

arXiv: https://arxiv.org/abs/2004.13667 · HTML: https://mlsanity.com/paper/2004.13667/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Viral Load Inference in Non-Adaptive Pooled Testing](https://mlsanity.com/paper/2403.09130/) (2024)

## Abstract (arXiv)

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.