Smell and taste symptom‐based predictive model for COVID‐19 diagnosis

Abstract

Background The presentation of COVID‐19 overlaps with common influenza symptoms. There is limited data on whether a specific symptom or collection of symptoms may be useful to predict test positivity.

Methods An anonymous electronic survey was publicized through social media to query participants with COVID‐19 testing. Respondents were questioned regarding 10 presenting symptoms, demographic information, comorbidities and COVID‐19 test results. Stepwise logistic regression was used to identify predictors for COVID positivity. Selected classifiers were assessed for prediction performance using receiver operating characteristic analysis (ROC).

Results One‐hundred and forty‐five participants with positive COVID‐19 testing and 157 with negative results were included. Participants had a mean age of 39 years, and 214 (72%) were female. Smell or taste change, fever, and body ache were associated with COVID‐19 positivity, and shortness of breath and sore throat were associated with a negative test result (p<0.05). A model using all 5 diagnostic symptoms had the highest accuracy with a predictive ability of 82% in discriminating between COVID‐19 results. To maximize sensitivity and maintain fair diagnostic accuracy, a combination of 2 symptoms, change in sense of smell or taste and fever was found to have a sensitivity of 70% and overall discrimination accuracy of 75%.

Conclusion Smell or taste change is a strong predictor for a COVID‐19 positive test result. Using the presence of smell or taste change with fever, this parsimonious classifier correctly predicts 75% of COVID‐19 test results. A larger cohort of respondents will be necessary to refine classifier performance.