Intelligent risk prediction in public health using wearable device data

The importance of infection risk prediction as a key public health measure has only been underscored by the COVID-19 pandemic. In a recent study, researchers use machine learning to develop an algorithm that predicts the risk of COVID-19 infection, by combining biometric data from wearable devices like Fitbit, with electronic symptom surveys. In doing so, they aim to increase the efficiency of test allocation when tracking disease spread in resource-limited settings. But the implications of technology that applies data from wearables stretch far beyond infection monitoring into healthcare delivery and research. The adoption and implementation of this type of technology will depend on regulation, impact on patient outcomes, and cost savings.

Automated summary

The article discusses a recent study that utilizes machine learning to develop an algorithm that predicts the risk of COVID-19 infection by combining biometric data from wearable devices with electronic symptom surveys. The implications of this technology extend beyond infection monitoring into healthcare delivery and research.