CovIdentify Dataset

This dataset supports the study "A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19," which developed an Intelligent Testing Allocation (ITA) method. The study demonstrated the efficacy of using continuous digital biomarkers like resting heart rate and steps to enhance COVID-19 diagnostic testing positivity rates. The findings suggest significant potential for large-scale, symptom-independent surveillance testing to alleviate diagnostic test shortages. The provided data is from the CovIdentify study launched by Duke's BIG IDEAs Lab in the Biomedical Engineering Department. From April 2nd, 2020 to May 25th, 2021, 2,887 participants connected their smartwatches to the CovIdentify platform, including 1,689 Garmin, 1,091 Fitbit, and 107 Apple smartwatches