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Review

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

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Office of the Deputy Vice Chancellor: Research, Innovation and Engagement, Central University of Technology, Bloemfontein 9301, South Africa
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Centre for Sustainable Smart Cities 4.0, Faculty of Engineering, Built Environment and Information Technology, Central University of Technology, Bloemfontein 9301, South Africa
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ICT and Society Research Group, Department of Information Technology, Durban University of Technology, Durban 4001, South Africa
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(15), 5330; https://doi.org/10.3390/ijerph17155330
Received: 4 May 2020 / Revised: 24 June 2020 / Accepted: 29 June 2020 / Published: 24 July 2020
The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19’s cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing. View Full-Text
Keywords: contact tracing; 2019 novel coronavirus disease (COVID-19); nature-inspired computing (NIC); artificial intelligence (AI); big data contact tracing; 2019 novel coronavirus disease (COVID-19); nature-inspired computing (NIC); artificial intelligence (AI); big data
MDPI and ACS Style

Agbehadji, I.E.; Awuzie, B.O.; Ngowi, A.B.; Millham, R.C. Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing. Int. J. Environ. Res. Public Health 2020, 17, 5330. https://doi.org/10.3390/ijerph17155330

AMA Style

Agbehadji IE, Awuzie BO, Ngowi AB, Millham RC. Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing. International Journal of Environmental Research and Public Health. 2020; 17(15):5330. https://doi.org/10.3390/ijerph17155330

Chicago/Turabian Style

Agbehadji, Israel E., Bankole O. Awuzie, Alfred B. Ngowi, and Richard C. Millham 2020. "Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing" International Journal of Environmental Research and Public Health 17, no. 15: 5330. https://doi.org/10.3390/ijerph17155330

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