Scientific Reports (Sep 2022)

A machine learning COVID-19 mass screening based on symptoms and a simple olfactory test

  • Youcef Azeli,
  • Alberto Fernández,
  • Federico Capriles,
  • Wojciech Rojewski,
  • Vanesa Lopez-Madrid,
  • David Sabaté-Lissner,
  • Rosa Maria Serrano,
  • Cristina Rey-Reñones,
  • Marta Civit,
  • Josefina Casellas,
  • Abdelghani El Ouahabi-El Ouahabi,
  • Maria Foglia-Fernández,
  • Salvador Sarrá,
  • Eduard Llobet

DOI
https://doi.org/10.1038/s41598-022-19817-x
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 9

Abstract

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Abstract The early detection of symptoms and rapid testing are the basis of an efficient screening strategy to control COVID-19 transmission. The olfactory dysfunction is one of the most prevalent symptom and in many cases 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. A multi-centre population-based prospective study was carried out in the city of Reus (Catalonia, Spain). The study included consecutive patients undergoing a reverse transcriptase polymerase chain reaction test for presenting symptoms suggestive of COVID-19 or for being close contacts of a confirmed COVID-19 case. 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 olfactory 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 this machine learning predictive model is a promising mass screening for COVID-19.