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Learning from Large-Scale Wearable Device Data for Predicting Epidemics Trend of COVID-19.
- Source :
-
Discrete Dynamics in Nature & Society . 5/10/2020, p1-8. 8p. - Publication Year :
- 2020
-
Abstract
- The pandemics of COVID-19 triggered out an alarm on the public health surveillance. The popularity of wearable devices enables a new perspective for the precaution of the infectious diseases. In this study, we propose a framework, which is based on the heart rate and sleep data collected from the wearable devices, to predict the epidemic trend of COVID-19 in different countries and cities. On top of a physiological anomaly detection algorithm defined based on wearable device data, an online neural network prediction modelling methodology combining both detected physiological anomaly rate and historical COVID-19 infection rate is explored. 4 models are trained separately according to geographical segmentation, i.e., North China, Central China, South China, and South-Central Europe. The de-identified sensor data from about 1.3 million wearable device users are used for verification. Experiment results indicate that the prediction models can be utilized to alert the outbreak of COVID-19 in advance, which sheds light on a health surveillance system with wearable device. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10260226
- Database :
- Academic Search Index
- Journal :
- Discrete Dynamics in Nature & Society
- Publication Type :
- Academic Journal
- Accession number :
- 143137512
- Full Text :
- https://doi.org/10.1155/2020/6152041