Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19 - Guokang Zhu; Jia Li; Zi Meng; Yi Yu; Yanan Li; Xiao Tang; Yuling Dong; Guangxin Sun; Rui Zhou; Hui Wang; Kongqiao Wang; Wang Huang (Discrete Dynamics in Nature and Society)
The coronavirus disease 2019 (COVID-19) pandemic has triggered a new response involving public health surveillance. The popularity of personal wearable devices creates a new opportunity for tracking and precaution of spread of such infectious diseases. In this study, we propose a framework, which is based on the heart rate and sleep data collected from wearable devices, to predict the epidemic trend of COVID-19 in different countries and cities. In addition to a physiological anomaly detection algorithm defined based on data from wearable devices, an online neural network prediction modelling methodology combining both detected physiological anomaly rate and historical COVID-19 infection rate is explored. Four models are trained separately according to geographical segmentation, i.e., North China, Central China, South China, and South-Central Europe. The anonymised sensor data from approximately 1.3 million wearable device users are used for model verification. Our experiment's results indicate that the prediction models can be utilized to alert to an outbreak of COVID-19 in advance, which suggests there is potential for a health surveillance system utilising wearable device data.