Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings

Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings

Symptoms-based models for predicting SARS-CoV-2 infection may improve clinical decision-making and be an alternative to resource allocation in under-resourced settings. In this study we aimed to test a model based on symptoms to predict a positive test result for SARS-CoV-2 infection during the COVI...

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Bibliographic Details
Main Authors: Andrea Ramírez Varela, Sergio Moreno López, Sandra Contreras-Arrieta, Guillermo Tamayo-Cabeza, Silvia Restrepo-Restrepo, Ignacio Sarmiento-Barbieri, Yuldor Caballero-Díaz, Luis Jorge Hernandez-Florez, John Mario González, Leonardo Salas-Zapata, Rachid Laajaj, Giancarlo Buitrago-Gutierrez, Fernando de la Hoz-Restrepo, Martha Vives Florez, Elkin Osorio, Diana Sofía Ríos-Oliveros, Eduardo Behrentz
Format: Article
Language:English
Published: Elsevier 2022-06-01
Series:Preventive Medicine Reports
Subjects:
SARS-CoV-2
COVID-19
Logistic model
Machine learning
Symptoms
Anosmia
Online Access:http://www.sciencedirect.com/science/article/pii/S221133552200105X

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http://www.sciencedirect.com/science/article/pii/S221133552200105X

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