Estimating seroprevalence of SARS-CoV-2 antibodies using three self-reported symptoms: development of a prediction model based on data from Ischgl, Austria.

ABSTRACT

We report the development of a regression model to predict the prevalence of severe acute respiratory syndrome coronavirus (SARS-CoV-2) antibodies on a population level based on self-reported symptoms. We assessed participant-reported symptoms in the past 12 weeks, as well as the presence of SARS-CoV-2 antibodies during a study conducted in April 2020 in Ischgl, Austria. We conducted multivariate binary logistic regression to predict seroprevalence in the sample. Participants (n = 451) were on average 47.4 years old (s.d. 16.8) and 52.5% female. SARS-CoV-2 antibodies were found in n = 197 (43.7%) participants. In the multivariate analysis, three significant predictors were included and the odds ratios (OR) for the most predictive categories were cough (OR 3.34, CI 1.70-6.58), gustatory/olfactory alterations (OR 13.78, CI 5.90-32.17) and limb pain (OR 2.55, CI 1.20-6.50). The area under the receiver operating characteristic curve was 0.773 (95% CI 0.727-0.820). Our regression model may be used to estimate the seroprevalence on a population level and a web application is being developed to facilitate the use of the model.