Semi-Quantitative Group Testing for Efficient and Accurate qPCR Screening of Pathogens with a Wide Range of Loads

TL;DR

This paper introduces a semi-quantitative group testing scheme for qPCR that improves efficiency and accuracy in pathogen screening across a wide load range, reducing tests by 24% with minimal false negatives.

Contribution

It presents a novel adaptive semi-quantitative group testing method that utilizes qPCR cycle threshold data for more efficient population screening.

Findings

Reduces number of tests by 24% compared to traditional methods.

Maintains negligible false negative rate.

Enhances detection sensitivity through quantized $Ct$ value bins.

Abstract

Pathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing plays a critical role in disease control and transmission prevention. Group testing is a well-established method for reducing the number of tests needed to screen large populations when the disease prevalence is low. However, it does not fully utilize the quantitative information provided by qPCR methods, nor is it able to accommodate a wide range of pathogen loads. To address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to efficiently screen populations via two-stage qPCR testing. The SQGT method quantizes cycle threshold ($Ct$) values into multiple bins, leveraging the information from the first stage of screening to improve the detection sensitivity. Dynamic $Ct$ threshold adjustments mitigate dilution effects and enhance test accuracy. Comparisons with traditional binary outcome GT methods show that SQGT reduces the number of tests by $24$% while maintaining a negligible false negative rate.