Real-time tracking of self-reported symptoms to predict potential COVID-19

Abstract

A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31-7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19.

Fig. 1 |. Association between symptoms and SARS-CoV-2 infection, and ROCs for prediction of the risk of a positive test.

a, Association between symptoms and the odds ratio of SARS-CoV-2 infection in 15,638 UK and 2,763 US participants who were tested via RT-PCR. Error bars represent 95% CIs. b,c, ROCs for prediction in the UK test set (b) and US validation set (c) of the risk of a positive test for SARS-CoV-2, using the following self-reported symptoms and traits: persistent cough, fatigue, skipped meals, loss of smell and taste, sex and age. Values for AUC, sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) are shown, with 95% CIs in parentheses.