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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 07 24; 17(15)IJ

Abstract

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.

Authors+Show Affiliations

Office of the Deputy Vice Chancellor: Research, Innovation and Engagement, Central University of Technology, Bloemfontein 9301, South Africa.Centre for Sustainable Smart Cities 4.0, Faculty of Engineering, Built Environment and Information Technology, Central University of Technology, Bloemfontein 9301, South Africa.Office of the Deputy Vice Chancellor: Research, Innovation and Engagement, Central University of Technology, Bloemfontein 9301, South Africa.ICT and Society Research Group, Department of Information Technology, Durban University of Technology, Durban 4001, South Africa.

Pub Type(s)

Journal Article
Review

Language

eng

PubMed ID

32722154

Citation

Agbehadji, Israel Edem, et al. "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, vol. 17, no. 15, 2020.
Agbehadji IE, Awuzie BO, Ngowi AB, et al. 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(15).
Agbehadji, I. E., Awuzie, B. O., Ngowi, A. B., & Millham, R. C. (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(15). https://doi.org/10.3390/ijerph17155330
Agbehadji IE, et al. 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 07 24;17(15) PubMed PMID: 32722154.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing. AU - Agbehadji,Israel Edem, AU - Awuzie,Bankole Osita, AU - Ngowi,Alfred Beati, AU - Millham,Richard C, Y1 - 2020/07/24/ PY - 2020/05/04/received PY - 2020/06/24/revised PY - 2020/06/29/accepted PY - 2020/7/30/entrez PY - 2020/7/30/pubmed PY - 2020/8/22/medline KW - 2019 novel coronavirus disease (COVID-19) KW - artificial intelligence (AI) KW - big data KW - contact tracing KW - nature-inspired computing (NIC) JF - International journal of environmental research and public health JO - Int J Environ Res Public Health VL - 17 IS - 15 N2 - 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. SN - 1660-4601 UR - https://www.unboundmedicine.com/medline/citation/32722154/Review_of_Big_Data_Analytics_Artificial_Intelligence_and_Nature_Inspired_Computing_Models_towards_Accurate_Detection_of_COVID_19_Pandemic_Cases_and_Contact_Tracing_ L2 - https://www.mdpi.com/resolver?pii=ijerph17155330 DB - PRIME DP - Unbound Medicine ER -