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

TL;DR

This paper introduces DOPE, a novel Bayesian D-optimal pooling strategy for SARS-CoV-2 testing that improves accuracy and efficiency by maximizing information gain and incorporating prior knowledge.

Contribution

The paper presents a new pooling design method using D-optimal experimental design principles, outperforming existing strategies in COVID-19 testing.

Findings

DOPE reduces testing errors compared to traditional pooling methods.

It requires fewer tests to achieve similar or better accuracy.

Provides probabilistic infection estimates rather than binary results.

Abstract

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. We present a novel pooling strategy that implements D-Optimal Pooling Experimental design (DOPE). DOPE defines optimal pooled tests as those maximizing the mutual information between data and infection states. We estimate said mutual information via Monte-Carlo sampling and employ a discrete optimization heuristic for maximizing it. DOPE outperforms common pooling strategies both in terms of lower error rates and fewer tests utilized. DOPE holds several additional advantages: it provides posterior distributions of the probability of infection, rather than only binary classification outcomes; it naturally incorporates prior information of infection probabilities and test error rates; and finally, it can be easily extended to include other, newly discovered information regarding COVID-19. Hence, we believe that implementation of Bayesian D-optimal experimental design holds a great promise for the efforts of combating COVID-19 and other future pandemics.