Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science

JAHMUNAH, V., SUDARSHAN, V.K., OH, S.L., GURURAJAN, R., GURURAJAN, R., ZHOU, X., TAO, X., FAUST, Oliver, CIACCIO, E.J., NG, K.H. and ACHARYA, U.R. (2021). Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science. International Journal of Imaging Systems and Technology.

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Official URL: https://onlinelibrary.wiley.com/doi/full/10.1002/i...
Link to published version:: https://doi.org/10.1002/ima.22552


© 2021 Wiley Periodicals LLC. In 2020 the world is facing unprecedented challenges due to COVID-19. To address these challenges, many digital tools are being explored and developed to contain the spread of the disease. With the lack of availability of vaccines, there is an urgent need to avert resurgence of infections by putting some measures, such as contact tracing, in place. While digital tools, such as phone applications are advantageous, they also pose challenges and have limitations (eg, wireless coverage could be an issue in some cases). On the other hand, wearable devices, when coupled with the Internet of Things (IoT), are expected to influence lifestyle and healthcare directly, and they may be useful for health monitoring during the global pandemic and beyond. In this work, we conduct a literature review of contact tracing methods and applications. Based on the literature review, we found limitations in gathering health data, such as insufficient network coverage. To address these shortcomings, we propose a novel intelligent tool that will be useful for contact tracing and prediction of COVID-19 clusters. The solution comprises a phone application combined with a wearable device, infused with unique intelligent IoT features (complex data analysis and intelligent data visualization) embedded within the system to aid in COVID-19 analysis. Contact tracing applications must establish data collection and data interpretation. Intelligent data interpretation can assist epidemiological scientists in anticipating clusters, and can enable them to take necessary action in improving public health management. Our proposed tool could also be used to curb disease incidence in future global health crises.

Item Type: Article
Uncontrolled Keywords: Artificial Intelligence & Image Processing; 0801 Artificial Intelligence and Image Processing
Identification Number: https://doi.org/10.1002/ima.22552
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 05 Mar 2021 17:17
Last Modified: 17 Mar 2021 14:00
URI: https://shura.shu.ac.uk/id/eprint/28326

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