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

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.