DOPE: D-Optimal Pooling Experimental design with application for SARS-CoV-2 screening.

ABSTRACT:

Objective

Testing individuals for the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen causing the coronavirus disease 2019 (COVID-19), is crucial for curtailing transmission chains. Moreover, rapidly testing many potentially infected individuals is often a limiting factor in controlling COVID-19 outbreaks. Hence, pooling strategies, wherein individuals are grouped and tested simultaneously, are employed. Here, we present a novel pooling strategy that builds on the Bayesian D-optimal experimental design criterion.

Materials and methods

Our strategy, called DOPE (D-Optimal Pooling Experimental design), is built on a novel Bayesian formulation of pooling. DOPE defines optimal pooled tests as those maximizing the mutual information between

SUBMITTER: Daon Y 

PROVIDER: S-EPMC8436773 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature