AC-DC: Amplification Curve Diagnostics for Covid-19 Group Testing

TL;DR

This paper reviews Covid-19 PCR testing, analyzes existing group testing methods, and introduces new analytical results for adaptive semiquantitative group testing, improving performance bounds and algorithms especially under noisy conditions.

Contribution

It provides new analytical bounds, algorithms, and noise protocols for adaptive semiquantitative group testing, enhancing applicability and robustness.

Findings

Performance bounds for adaptive group testing methods.

Algorithmic solutions for noisy testing protocols.

Enhanced handling of heavy hitters in testing.

Abstract

The first part of the paper presents a review of the gold-standard testing protocol for Covid-19, real-time, reverse transcriptase PCR, and its properties and associated measurement data such as amplification curves that can guide the development of appropriate and accurate adaptive group testing protocols. The second part of the paper is concerned with examining various off-the-shelf group testing methods for Covid-19 and identifying their strengths and weaknesses for the application at hand. The third part of the paper contains a collection of new analytical results for adaptive semiquantitative group testing with probabilistic and combinatorial priors, including performance bounds, algorithmic solutions, and noisy testing protocols. The probabilistic setting is of special importance as it is designed to be simple to implement by nonexperts and handle heavy hitters. The worst-case paradigm extends and improves upon prior work on semiquantitative group testing with and without specialized PCR noise models.