https://socictopen.socict.org/files/original/1d0a173c17a954d6e98277513faedd16.pdf ec36da26bc98329d54b984be6f46428e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Learning from Large-Scale Wearable Device Data for Predicting Epidemics Trend of COVID-19 Creator An entity primarily responsible for making the resource Hui Wang, Jia Li, Rui Zhou, Yanan LI, Yi Yu, Xiao Tang, Yuling Dong, Zi Meng, Wang Huang, Guangxin Sun, Guokang Zhu, Kongqiao Wang Description An account of the resource 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. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Identifier An unambiguous reference to the resource within a given context DOI: 10.1155/2020/6152041 Source A related resource from which the described resource is derived Discrete Dynamics in Nature and Society Publisher An entity responsible for making the resource available Hindawi Limited Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mathematics