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Author (up) Eyheramendy, S.; Saa, P.A.; Undurraga, E.A.; Valencia, C.; Lopez, C.; Mendez, L.; Pizarro-Berdichevsky, J.; Finkelstein-Kulka, A.; Solari, S.; Salas, N.; Bahamondes, P.; Ugarte, M.; Barcelo, P.; Arenas, M.; Agosin, E.
Title Screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test Type
Year 2021 Publication iScience Abbreviated Journal iScience
Volume 24 Issue 12 Pages 103419
Keywords TRANSMISSION; DYSFUNCTION; SCALE
Abstract The sudden loss of smell is among the earliest and most prevalent symptoms of COVID-19 when measured with a clinical psychophysical test. Research has shown the potential impact of frequent screening for olfactory dysfunction, but existing tests are expensive and time consuming. We developed a low-cost ($0.50/test) rapid psychophysical olfactory test (KOR) for frequent testing and a model-based COVID-19 screening framework using a Bayes Network symptoms model. We trained and validated the model on two samples: suspected COVID-19 cases in five healthcare centers (n = 926; 33% prevalence, 309 RT-PCR confirmed) and healthy miners (n = 1,365; 1.1% prevalence, 15 RT-PCR confirmed). The model predicted COVID-19 status with 76% and 96% accuracy in the healthcare and miners samples, respectively (healthcare: AUC = 0.79 [0.75-0.82], sensitivity: 59%, specificity: 87%; miners: AUC = 0.71 [0.63-0.79], sensitivity: 40%, specificity: 97%, at 0.50 infection probability threshold). Our results highlight the potential for low-cost, frequent, accessible, routine COVID-19 testing to support society's reopening.
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ISSN 2589-0042 ISBN Medium
Area Expedition Conference
Notes WOS:000740250200009 Approved
Call Number UAI @ alexi.delcanto @ Serial 1524
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