Pooling of samples: strain or solution?
This article was originally published here
Clin Microbiol Infect. April 17, 2021: S1198-743X (21) 00190-7. doi: 10.1016 / j.cmi.2021.04.007. Online before printing.
BACKGROUND: Pool testing strategies combine samples from several people and test them in groups. A pool testing approach can shorten screening time and increase testing rate during times of limited test availability and inadequate reporting speed. Pool tests have been used effectively for a wide variety of infectious disease screening parameters. Historically, it came from serological testing in syphilis. During the current COVID-19 pandemic, pool testing is being considered around the world to inform openness strategies and monitor infection rates after interventions are implemented.
OBJECTIVES: This narrative review aims to provide a comprehensive overview of global efforts to implement pool testing, particularly for COVID-19 testing.
SOURCES: Data was extracted from a detailed search of peer-reviewed articles and preprinted reports using Medline / PubMed, medRxiv, Web of Science and Google through March 21, 2021, using the terms for research “pool test”, “viral”, “serum”, “SARS-CoV-2”, “COVID-19”.
CONTENT: This review summarizes the history and theory of swimming pool testing. We have identified numerous peer-reviewed articles that describe specific details and practical implementation of pool testing. Success stories and limitations of pooled testing, in general and specifically related to the detection of SARS-CoV-2 RNA and antibodies, are reviewed. While promising, there are significant operational, pre-analytical, logistical and economic challenges that must be overcome to advance pool testing.
IMPLICATIONS: The theory of pool testing is well understood and many successful examples are available in the past. Operationalizing pool testing requires sophisticated processes that are tailored to local medical circumstances. Special attention should be given to sample collection, sample pooling and strategies to avoid resampling.