An infection prediction model developed from inpatient data can predict out-of-hospital COVID-19 infections from wearable data when controlled for dataset shift

The COVID-19 pandemic highlighted the importance of early detection of illness and the need for health monitoring solutions outside of the hospital setting. We have previously demonstrated a real-time system to identify COVID-19 infection before diagnostic testing, that was powered by commercial-off-the-shelf wearables and machine learning models trained with wearable physiological data from COVID-19 cases outside of hospitals. However, these types of solutions were not readily available at the onset nor during the early outbreak of a new infectious disease when preventing infection transmission was critical, due to a lack of pathogen-specific illness data to train the machine learning models. This study investigated whether a pretrained clinical decision support algorithm for predicting hospital-acquired infection (predating COVID-19) could be readily adapted to detect early signs of COVID-19 infection from wearable physiological signals collected in an unconstrained out-of-hospital setting. A baseline comparison where the pretrained model was applied directly to the wearable physiological data resulted a performance of AUROC = 0.52 in predicting COVID-19 infection. After controlling for contextual effects and applying an unsupervised dataset shift transformation derived from a small set of wearable data from healthy individuals, we found that the model performance improved, achieving an AUROC of 0.74, and it detected COVID-19 infection on average 2 days prior to diagnostic testing. Our results suggest that it is possible to deploy a wearable physiological monitoring system with an infection prediction model pretrained from inpatient data, to readily detect out-of-hospital illness at the emergence of a new infectious disease outbreak.