Smell and taste symptom-based predictive model for COVID-19 diagnosis.

ABSTRACT:

Background

The presentation of coronavirus 2019 (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-19 positivity. Selected classifiers were assessed for prediction performance using receiver operating characteristic (ROC) curve analysis.

Results

A total of 145 participants with positive COVID-19 testing and 157 with negative results we

SUBMITTER: Roland LT 

PROVIDER: S-EPMC7267242 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature