Development and internal validation of a predictive model for COVID-19 mass screening based on symptoms and a simple olfactory test

Abstract

The early detection of symptoms and rapid testing are the basis of an efficient screening strategy to control COVID-19 transmission. Most COVID-19 patients show olfactory dysfunction and in many cases this is the first symptom. This study aims to develop a machine learning COVID-19 predictive tool based on symptoms and a simple olfactory test, which consists of identifying the smell of an aromatized hydroalcoholic gel (CovidGel Test). A multi-centre population-based prospective study was carried out in the city of Reus (Catalonia, Spain). A total of 519 patients were included, 386 (74.4%) had at least one symptom and 133 (25.6%) were asymptomatic. A classification tree model including sex, age, relevant symptoms and the CovidGel Test results obtained a sensitivity of 0.97 (95% CI 0.91–0.99), a specificity of 0.39 (95% CI 0.34–0.44) and an AUC of 0.87 (95% CI 0.83–0.92). This shows that the CovidGel Test is a promising mass screening tool for predicting COVID-19.