Figure 2
Algorithms aligned by PX can be used to classify COVID-19 infection. Each panel shows a set of receiver operator curves (ROC) with shading indicating ± 95% CI. PX = date of maximal change from average over the 21-day DX region; SX = date of onset of one of four core symptoms of COVID-19; DX = date of diagnostic testing for COVID-19; HR = heart rate, HRV = heart rate variability, and RR = respiratory rate. Numbers in relationship to PX, SX, and DX refer to number of days before (negative numbers) or after (positive numbers) each of these dates. Models trained by alignment to PX were more accurate as the evaluation window approached PX (A; from red pre-PX to blue post-PX; n = 73; in all cases, the number of negative training samples was 179,010; the number of positive training samples were: 8678, 9059, 9527, 9719, and 9705, respectively), with a peak accuracy at the window of PX + 0: PX + 2 days. ROC curves generated from models trained by alignment to DX performed best when evaluated relative to PX (B; n = 41, restricted to the subset of individuals with reliable symptom onset reports). Models trained by alignment to PX, SX, and DX performed comparably when evaluated at PX + 0: PX + 2 days (C; n = 41). Exclusion of any physiological measure lowers performance, with the ROC AUC dropping the most when HRV was omitted (D; n = 73).
