[Preprint] A Recovery Algorithm and Pooling Designs for One-Stage Noisy Group Testing Under the Probabilistic Framework

This paper is a preprint.

Here I study noisy group testing under the probabilistic framework by modelling the infection vector as a random vector with Bernoulli entries. The main contributions include a practical one-stage group testing protocol guided by maximizing pool entropy and a maximum-likelihood recovery algorithm under the probabilistic framework. The findings highlight the implications of introducing randomness to the infection vectors – that the combinatorial structure of the pooling designs plays a less important role than the parameters such as pool size and redundancy.

Available: medRxiv [External Link]
Collection: COVID-19 SARS-CoV-2 preprints from medRxiv and bioRxiv
Subject Area: Infectious Diseases
Paper: [medRxiv Link]
Citation: This is a preprint paper. Please refer to the final published paper.