Two-Stage Adaptive Pooling with RT-qPCR for COVID-19 Screening

TL;DR

This paper introduces two adaptive pooling schemes, 2-STAP and 2-STAMP, that improve COVID-19 testing efficiency and accuracy by leveraging soft RT-qPCR information, outperforming traditional and existing non-adaptive pooling methods.

Contribution

The paper presents novel two-stage adaptive pooling schemes that utilize soft RT-qPCR data, achieving higher throughput, sensitivity, and specificity than prior group testing methods.

Findings

Achieve 13.5x higher testing throughput than individual testing.

Maintain sensitivity of 99.50% and specificity of 99.62%.

Require fewer pipetting operations than non-adaptive schemes.

Abstract

We propose two-stage adaptive pooling schemes, 2-STAP and 2-STAMP, for detecting COVID-19 using real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR) test kits. Similar to the Tapestry scheme of Ghosh et al., the proposed schemes leverage soft information from the RT-qPCR process about the total viral load in the pool. This is in contrast to conventional group testing schemes where the measurements are Boolean. The proposed schemes provide higher testing throughput than the popularly used Dorfman's scheme. They also provide higher testing throughput, sensitivity and specificity than the state-of-the-art non-adaptive Tapestry scheme. The number of pipetting operations is lower than state-of-the-art non-adaptive pooling schemes, and is higher than that for the Dorfman's scheme. The proposed schemes can work with substantially smaller group sizes than non-adaptive schemes and are simple to describe. Monte-Carlo simulations using the statistical model in the work of Ghosh et al. (Tapestry) show that 10 infected people in a population of size 961 can be identified with 70.86 tests on the average with a sensitivity of 99.50% and specificity of 99.62. This is 13.5x, 4.24x, and 1.3x the testing throughput of individual testing, Dorfman's testing, and the Tapestry scheme, respectively.