Eyheramendy, SusanaSaa, Pedro A.Undurraga, Eduardo A.Valencia, CarlosLópez, CarolinaMéndez Alcamán, LuisPizarro-Berdichevsky, JavierFinkelstein-Kulka, AndrésSolari, SandraSalas, NicolásBahamondes, PedroUgarte, MartínBarceló, PabloArenas, MarceloAgosin, Eduardo2022-04-042022-04-042021Susana Eyheramendy, Pedro A. Saa, Eduardo A. Undurraga, Carlos Valencia, Carolina López, Luis Méndez, Javier Pizarro-Berdichevsky, Andrés Finkelstein-Kulka, Sandra Solari, Nicolás Salas, Pedro Bahamondes, Martín Ugarte, Pablo Barceló, Marcelo Arenas, Eduardo Agosin, Screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test, iScience, Volume 24, Issue 12, 2021, 103419, ISSN 2589-0042, https://doi.org/10.1016/j.isci.2021.103419.https://doi.org/10.1016/j.isci.2021.103419http://hdl.handle.net/11447/5913The 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.enDiagnostic technique in health technologyDiagnosticsHealth technologyMathematical biosciencesArticle