Informed Pooled Testing with Quantitative Assays

TL;DR

This paper compares three pooling strategies using quantitative assays for infection screening, demonstrating that optimized pooling can be more cost-effective than individual testing, with mMPA showing superior efficiency.

Contribution

It provides a comprehensive statistical analysis of three pooling methods with quantitative assays, highlighting their efficiency and diagnostic trade-offs.

Findings

Pooling strategies can be more cost-efficient than individual testing with proper pool sizes.

mMPA outperforms MPA and MP in cost-efficiency.

mMPA and MPA have higher specificity but lower sensitivity than MP and individual testing.

Abstract

Pooled testing is widely used for screening for viral or bacterial infections with low prevalence when individual testing is not cost-efficient. Pooled testing with qualitative assays that give binary results has been well-studied. However, characteristics of pooling with quantitative assays were mostly demonstrated using simulations or empirical studies. We investigate properties of three pooling strategies with quantitative assays: traditional two-stage mini-pooling (MP) (Dorfman, 1943), mini-pooling with deconvolution algorithm (MPA) (May et al., 2010), and marker-assisted MPA (mMPA) (Liu et al., 2017). MPA and mMPA test individuals in a sequence after a positive pool and implement a deconvolution algorithm to determine when testing can cease to ascertain all individual statuses. mMPA uses information from other available markers to determine an optimal order for individual testings. We derive and compare the general statistical properties of the three pooling methods. We show that with a proper pool size, MP, MPA, and mMPA can be more cost-efficient than individual testing, and mMPA is superior to MPA and MP. For diagnostic accuracy, mMPA and MPA have higher specificity and positive predictive value but lower sensitivity and negative predictive value than MP and individual testing. Included in this paper are applications to various simulations and an application for HIV treatment monitoring.