A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19

Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovidInMyLife study (6765 participants) and the MyPHd study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and partner search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7–11% when compared with RHR features alone, while including RHR features improved the AUC metrics of precision-recall curve (AUC-PR) by 36–50% when combined with steps features alone. The method showed an AUC of 0.77 in the cross-validated training set and independently tested positively and AUC-PR of 0.55±0.21 and 0.24 when applied to the independent test set, including symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a larger scale and without self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages.

Md Mobashir Hasan Shandhi, Peter J Cho, Ali R Roghanzadeh, Karnika Singh, Will Wang, Oana M Enache, Amanda Stern, Rami Sbahi, Bilge Tatar, Sean Fiscus, Qi Xuan Khoo, Yvonne Kuo, Joseph Hsieh, Alena Kalodizati, Amir Bahmani, Arash Alavi, Ustab Ray, Michael P Snyder, Geoffrey S Ginsburg, Daniel K Pasquale, Christopher W Woods, Ryan J Shaw, Jessilyn P Dunn