About: Rapid, large-scale, and effective detection of COVID-19 via non-adaptive testing

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Rapid, large-scale, and effective detection of COVID-19 via non-adaptive testing
Creator	Täufer, Matthias
Source	Elsevier; Medline; PMC
abstract	Pooling of samples can increase lab capacity when using Polymerase chain reaction (PCR) to detect diseases such as COVID-19. However, pool testing is typically performed via an adaptive testing strategy which requires a feedback loop in the lab and at least two PCR runs to confirm positive results. This can cost precious time. We discuss a non-adaptive testing method where each sample is distributed in a prescribed manner over several pools, and which yields reliable results after one round of testing. More precisely, assuming knowledge about the overall incidence rate, we calculate explicit error bounds on the number of false positives which scale favourably with pool size and sample multiplicity. This allows for hugely streamlined PCR testing and cuts in detection times for a large-scale testing scenario. A viable consequence of this method could be real-time screening of entire communities, frontline healthcare workers and international flight passengers, for example, using the PCR machines currently in operation.
has issue date	2020-08-16(xsd:dateTime)
bibo:doi	10.1016/j.jtbi.2020.110450
bibo:pmid	32814072
has license	no-cc
sha1sum (hex)	87b94ce3db33af59063152dab33d888848641ed7
schema:url	https://doi.org/10.1016/j.jtbi.2020.110450
resource representing a document's title	Rapid, large-scale, and effective detection of COVID-19 via non-adaptive testing
has PubMed Central identifier	PMC7428746
has PubMed identifier	32814072
schema:publication	J Theor Biol
resource representing a document's body	covid:87b94ce3db33af59063152dab33d888848641ed7#body_text
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