Group testing as a strategy for the epidemiologic monitoring of COVID-19

TL;DR

This paper analyzes the use of sample pooling in COVID-19 testing, developing models for viral load detection and prevalence estimation, and explores applications for epidemic prevention in closed communities.

Contribution

It introduces a statistical model of viral load detection, proposes a prevalence measurement method considering false negatives, and applies pooling strategies to epidemic control.

Findings

Sample pooling can efficiently estimate prevalence with adjusted false negative considerations.

The proposed models improve understanding of pooling effects on test accuracy.

Application examples demonstrate pooling's potential in epidemic prevention in closed settings.

Abstract

Sample pooling consists in combining samples from multiple individuals into a single pool that is then tested using a unique test-kit. A positive test means that at least one individual within the pool is infected. Here, we propose an analysis and applications of sample pooling to the epidemiologic monitoring of COVID-19. We first introduce a model of the RT-qPCR process used to test for the presence of virus in a sample and construct a statistical model for the viral load in a typical infected individual inspired by the clinical data from Jones et. al. (2020). We then propose a method for the measure of the prevalence in a population, based on group testing, taking into account the increased number of false negatives associated with this method. Finally, we present an application of sample pooling for the prevention of epidemic outbreak in closed connected communities (e.g. nursing homes).