About: Smart Pooling: AI-powered COVID-19 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	Smart Pooling: AI-powered COVID-19 testing
Creator	Restrepo, Silvia Arbelaez, Pablo Bravo-Sánchez, Laura Castillo, Angela Cepeda, Martha Escobar, María Forero-Shelton, Manu Guevara-Suarez, Marcela Gómez, Catalina Jeanneret, Guillaume Madrid-Wolff, Jorge Martínez, Julián Medaglia, Andrés Pedraza-Leal, Juan Roa, Maria Sarmiento, Olga Valderrama, Diego Velasco, Mauricio
Source	MedRxiv
abstract	Massive molecular testing for COVID-19 has been pointed as fundamental to moderate the spread of the disease. Pooling methods can enhance testing efficiency, but they are viable only at very low prevalences of the disease. We propose Smart Pooling, a machine learning method that uses sociodemographic data from patients to increase the efficiency of pooled molecular testing for COVID-19 by arranging samples into all-negative pools. We show efficiency gains of 42% with respect to individual testing at disease prevalence of up to 25%, a regime in which two-step pooling offers marginal efficiency gains. Additionally, we calculate the possible efficiency gains of one- and two-dimensional two-step pooling strategies and present the optimal strategies for disease prevalences up to 25%. We discuss practical limitations to conduct pooling in the laboratory.
has issue date	2020-07-15(xsd:dateTime)
bibo:doi	10.1101/2020.07.13.20152983
has license	medrxiv
sha1sum (hex)	2f904674d4fd87023b02950eeff6497be222926b
schema:url	https://doi.org/10.1101/2020.07.13.20152983
resource representing a document's title	Smart Pooling: AI-powered COVID-19 testing
resource representing a document's body	covid:2f904674d4fd87023b02950eeff6497be222926b#body_text
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