Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19
Open accessAbstract
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.
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A framework uses heart rate and sleep data from wearable devices to predict the progression of COVID-19 outbreaks in different countries and cities.
“The framework proposed in [44] uses heart rate and sleep data from wearable devices to predict the progression of COVID-19 outbreaks in different countries and cities.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [44]
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Sleep and cardiorespiratory function assessed by a smart bed over 10 weeks post COVID-19 infection
Garcia‐Molina, G., Guzenko, D., DeFranco, S., Aloia, M. S., Mills, R., Mushtaq, F., Somers, V. K., & Cauter, E. V. (2025) · Scientific Reports · doi:10.1038/s41598-025-87069-6 · Cited by 1 · see citing works
A Design Proposition for An IoT Health Data Collection System using Smart Wearable Devices
Zietsman, P., van Rooyen, G., & Bam, L. (2023) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice/itmc58018.2023.10332271 · Cited by 1 · see citing works
Fitbeat: COVID-19 Estimation based on Wristband Heart RatePreprint
Liu, S., Han, J., Puyal, E. L., Kontaxis, S., Sun, S., Locatelli, P., Dineley, J., Pokorny, F. B., Costa, G. D., Leocani, L., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.09263 · Cited by 1 · see citing works
Ferreira, D. D., Santos, L. O., Alvarenga, T. A., Rodríguez, D. Z., Barbosa, B. H. G., Ferreira, A. C. B. H., Alves, D. F. D. S., Carmona, E. V., Duran, É. C. M., & Lopes, M. H. B. D. M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91794-0.00018-4
A review on wearable devices for detecting and assisting Covid-19 infected patient
Devi, R. D. H., Naresh, R., Kumar, C. N. S. V., Bharathi, V., & Narayanan, K. L. (2023) · AIP conference proceedings · doi:10.1063/5.0125263
Analysis on Smart Healthcare Monitoring Based on Compound Dimension
Vennilapriya, B., & Thangammal, C. B. (2024) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eetpht.10.5266
COVID-19 surveillance based on consumer wearable devices
Zhang, C., Sun, A., Liao, J., Zhang, C., Yu, K., Ma, X., & Wang, G. (2024) · Digital Health · doi:10.1177/20552076241247374
The Future of Virology Diagnostics Using Wearable Devices Driven by Artificial Intelligence
Sallam, M., Mijwil, M. M., Abotaleb, M. S. A., & Sailawi, A. S. A. A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7352-1.ch020
Machine Learning Used in Communicable Disease Control: A Scoping Review
Birdi, S., Patel, A., Rabet, R., Singh, N., Durant, S., Vosoughi, T., Kapra, F., Shergill, M., Mesfin, E., Ziegler, C. P., et al. (2026) · Public health reviews · doi:10.3389/phrs.2026.1608074
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- Authors
- Zhu, G., Li, J., Meng, Z., Yu, Y., Li, Y., Tang, X., Dong, Y., Sun, G., Zhou, R., Wang, H., Wang, K., & Huang, W.
- Affiliations
- Huami Corporation, Hefei, China
- Corresponding author
- Wang, Kongqiao
- Venue
- Discrete Dynamics in Nature and Society
- Publisher
- Wiley
- Published
- 2020-05-05
- Volume
- 2020
- Pages
- 1-8
- ISSN
- 1026-0226 (ISSN-L)
- 1607-887X
- OpenAlex
- W3023696618
- Cited by
- 77
- References
- 23
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 7 · 2023: 13 · 2022: 10 · 2021: 23 · 2020: 17
- FWCI
- 11.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Discrete Dynamics in Nature and Society (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- Funders
- Huami Corporation
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
- Keywords
- COVID-19 pandemic, epidemic forecasting, wearable devices, heart rate, sleep data, disease surveillance, outbreak early warning, geographical segmentation
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