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Updated: Oct 14, 2025

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

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Published on: June 23, 2022

An accurate model for SARS-CoV-2 pooled RT-PCR test errors.

Yair Daon^1,2, Amit Huppert^1,3, Uri Obolski^1,3

¹School of Public Health, The Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

Royal Society Open Science

|November 5, 2021

Summary

This summary is machine-generated.

Dorfman pooling for SARS-CoV-2 testing can be less accurate at low infection prevalence. This study introduces a new model showing increased false-negative rates, urging cautious use of pooling strategies.

Keywords:

COVID-19 pooling probabilistic model qRT-PCR‌

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Area of Science:

Pathogen detection
Epidemiological modeling
Diagnostic test evaluation

Background:

Sample pooling increases testing throughput for pathogens like SARS-CoV-2.
Dorfman pooling is a common strategy, but its error rates may be underestimated.
Existing analyses often assume pooled tests are unaffected by multiple infected samples.

Purpose of the Study:

To challenge the assumption that pooled tests are unaffected by multiple infected samples.
To propose a novel probabilistic model for pooled test outcomes.
To analyze the false-negative rate of Dorfman pooling, especially concerning infection prevalence.

Main Methods:

Experimental data collection on pooled SARS-CoV-2 tests.
Development of a new probabilistic model for pooled test outcomes.
Analysis of false-negative rates in Dorfman pooling under varying infection prevalence.

Main Results:

Experimental data contradicts the assumption that multiple infected samples do not affect pooled test accuracy.
The proposed model provides a more parsimonious explanation for pooled test outcomes.
False-negative rates in Dorfman pooling increase as infection prevalence decreases.

Conclusions:

Dorfman pooling's efficiency is highest when infection prevalence is low, paradoxically when its accuracy decreases.
The study highlights the need for cautious application of pooling strategies.
Further development of pooling schemes that accurately account for error rates is recommended.