Fig. 1: Overview of the Intelligent Testing Allocation (ITA) model, the CovIdentify cohort, and data.

a Overview of the ITA model in comparison to a Random Testing Allocation (RTA) model that demonstrates the benefit of using the ITA model over existing RTA methods to improve the positivity rate of diagnostic testing in resource-limited settings. Human symbols with orange and blue colors represent individuals with and without COVID-19 infection, respectively. b A total of 7348 participants were recruited following informed consent in the CovIdentify study, out of whom 1289 participants reported COVID-19 diagnostic tests (1157 diagnosed as negative for COVID-19 and 132 diagnosed as positive for COVID-19). c The top panel shows the time-averaged step count and the bottom panel shows the time-averaged resting heart rate (RHR) of all participants (n = 50) in the training set (Supplementary Fig. 3, blue) who were tested positive for COVID-19 with the pre-defined baseline (between –60 and –22 days from the diagnostic test) and detection (between –21 and –1 days from the diagnostic test) periods marked with vertical black dashed lines. The dark green dashed lines and the light green dash-dotted lines display the baseline period mean and ± 2 standard deviations from the baseline mean, respectively. The light purple dashed vertical line shows the diagnostic test date.