Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19
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...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/6152041 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832550393449545728 |
|---|---|
| author | Guokang Zhu Jia Li Zi Meng Yi Yu Yanan Li Xiao Tang Yuling Dong Guangxin Sun Rui Zhou Hui Wang Kongqiao Wang Wang Huang |
| author_facet | Guokang Zhu Jia Li Zi Meng Yi Yu Yanan Li Xiao Tang Yuling Dong Guangxin Sun Rui Zhou Hui Wang Kongqiao Wang Wang Huang |
| author_sort | Guokang Zhu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-02c001caab074afb9311a763cb97caf3 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-02c001caab074afb9311a763cb97caf32025-02-03T06:06:44ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/61520416152041Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19Guokang Zhu0Jia Li1Zi Meng2Yi Yu3Yanan Li4Xiao Tang5Yuling Dong6Guangxin Sun7Rui Zhou8Hui Wang9Kongqiao Wang10Wang Huang11Huami Corporation, Hefei, ChinaHuami Corporation, Hefei, ChinaHuami Corporation, Hefei, ChinaHuami Corporation, Hefei, ChinaHuami Corporation, Hefei, ChinaHuami Corporation, Hefei, ChinaHuami Corporation, Hefei, ChinaHuami Corporation, Hefei, ChinaHuami Corporation, Hefei, ChinaHuami Corporation, Hefei, ChinaHuami Corporation, Hefei, ChinaHuami Corporation, Hefei, ChinaThe 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.http://dx.doi.org/10.1155/2020/6152041 |
| spellingShingle | Guokang Zhu Jia Li Zi Meng Yi Yu Yanan Li Xiao Tang Yuling Dong Guangxin Sun Rui Zhou Hui Wang Kongqiao Wang Wang Huang Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19 Discrete Dynamics in Nature and Society |
| title | Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19 |
| title_full | Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19 |
| title_fullStr | Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19 |
| title_full_unstemmed | Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19 |
| title_short | Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19 |
| title_sort | learning from large scale wearable device data for predicting the epidemic trend of covid 19 |
| url | http://dx.doi.org/10.1155/2020/6152041 |
| work_keys_str_mv | AT guokangzhu learningfromlargescalewearabledevicedataforpredictingtheepidemictrendofcovid19 AT jiali learningfromlargescalewearabledevicedataforpredictingtheepidemictrendofcovid19 AT zimeng learningfromlargescalewearabledevicedataforpredictingtheepidemictrendofcovid19 AT yiyu learningfromlargescalewearabledevicedataforpredictingtheepidemictrendofcovid19 AT yananli learningfromlargescalewearabledevicedataforpredictingtheepidemictrendofcovid19 AT xiaotang learningfromlargescalewearabledevicedataforpredictingtheepidemictrendofcovid19 AT yulingdong learningfromlargescalewearabledevicedataforpredictingtheepidemictrendofcovid19 AT guangxinsun learningfromlargescalewearabledevicedataforpredictingtheepidemictrendofcovid19 AT ruizhou learningfromlargescalewearabledevicedataforpredictingtheepidemictrendofcovid19 AT huiwang learningfromlargescalewearabledevicedataforpredictingtheepidemictrendofcovid19 AT kongqiaowang learningfromlargescalewearabledevicedataforpredictingtheepidemictrendofcovid19 AT wanghuang learningfromlargescalewearabledevicedataforpredictingtheepidemictrendofcovid19 |