Screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test

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dc.contributor.authorEyheramendy, Susana
dc.contributor.authorSaa, Pedro A.
dc.contributor.authorUndurraga, Eduardo A.
dc.contributor.authorValencia, Carlos
dc.contributor.authorLopez, Carolina
dc.contributor.authorMendez, Luis
dc.contributor.authorPizarro Berdichevsky, Javier
dc.contributor.authorFinkelstein Kulka, Andres
dc.contributor.authorSolari, Sandra
dc.contributor.authorSalas, Nicolas
dc.contributor.authorBahamondes, Pedro
dc.contributor.authorUgarte, Martin
dc.contributor.authorBarcelo, Pablo
dc.contributor.authorArenas, Marcelo
dc.contributor.authorAgosin, Eduardo
dc.date.accessioned2024-01-10T12:37:00Z
dc.date.available2024-01-10T12:37:00Z
dc.date.issued2021
dc.description.abstractThe 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.
dc.description.funderTechnological Adoption Fund SiEmpre from SOFOFA Hub (CORFO)
dc.description.funderANID through the Millennium Science Initiative Program
dc.description.funderANID Millennium Science Initiative Program
dc.description.funderANID FONDECYT
dc.description.funderANID FONDECYT de Iniciacion
dc.fechaingreso.objetodigital26-03-2024
dc.format.extent17 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.isci.2021.103419
dc.identifier.eissn2589-0042
dc.identifier.pubmedidMEDLINE:34786538
dc.identifier.urihttps://doi.org/10.1016/j.isci.2021.103419
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/76718
dc.identifier.wosidWOS:000740250200009
dc.information.autorucFacultad de Ingeniería; Arenas Saavedra, Marcelo Alejandro; S/I; 81488
dc.information.autorucInterdisciplinarias; Undurraga Fourcade, Eduardo Andres; S/I; 12868
dc.issue.numero12
dc.language.isoen
dc.nota.accesocontenido completo
dc.publisherCELL PRESS
dc.revistaISCIENCE
dc.rightsacceso abierto
dc.subjectTRANSMISSION
dc.subjectDYSFUNCTION
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleScreening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test
dc.typeartículo
dc.volumen24
sipa.codpersvinculados81488
sipa.codpersvinculados12868
sipa.indexWOS
sipa.trazabilidadCarga SIPA;09-01-2024
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