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Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19

التفاصيل البيبلوغرافية
العنوان: Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19
المؤلفون: Guokang Zhu, Jia Li, Zi Meng, Yi Yu, Yanan Li, Xiao Tang, Yuling Dong, Guangxin Sun, Rui Zhou, Hui Wang, Kongqiao Wang, Wang Huang
المصدر: Discrete Dynamics in Nature and Society, Vol 2020 (2020)
بيانات النشر: Wiley, 2020.
سنة النشر: 2020
المجموعة: LCC:Mathematics
مصطلحات موضوعية: Mathematics, QA1-939
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1026-0226
1607-887X
Relation: https://doaj.org/toc/1026-0226; https://doaj.org/toc/1607-887X
DOI: 10.1155/2020/6152041
URL الوصول: https://doaj.org/article/02c001caab074afb9311a763cb97caf3
رقم الأكسشن: edsdoj.02c001caab074afb9311a763cb97caf3
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:10260226
1607887X
DOI:10.1155/2020/6152041