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

Jahmunah, Vicnesh and Sudarshan, Vidya K. and Oh, Shu Lih and Gururajan, Raj ORCID: https://orcid.org/0000-0002-5919-0174 and Gururajan, Rashmi and Zhou, Xujuan and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Faust, Oliver and Ciaccio, Edward J. and Ng, Kwan Hoong and Acharya, U. Rajendra (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. pp. 1-17. ISSN 1098-1098


Abstract

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 wear-able device, infused with unique intelligent IoT features (complex data analysis and intelligent data visualization) embedded within the system to aid inCOVID-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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 Jul 2013 - 17 Jan 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 Jul 2013 - 17 Jan 2021)
Date Deposited: 23 Feb 2021 23:03
Last Modified: 23 Feb 2021 23:29
Uncontrolled Keywords: contact tracing, coronavirus disease, COVID-19, deep learning, digital tools, intelligent internet of things, wearable devices
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460206 Knowledge representation and reasoning
Socio-Economic Objectives (2008): C Society > 92 Health > 9204 Public Health (excl. Specific Population Health) > 920404 Disease Distribution and Transmission (incl. Surveillance and Response)
Socio-Economic Objectives (2020): 20 HEALTH > 2004 Public health (excl. specific population health) > 200404 Disease distribution and transmission (incl. surveillance and response)
Identification Number or DOI: https://doi.org/10.1002/ima.22552
URI: http://eprints.usq.edu.au/id/eprint/41407

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