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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

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  • Israel Edem Agbehadji

    (Office of the Deputy Vice Chancellor: Research, Innovation and Engagement, Central University of Technology, Bloemfontein 9301, South Africa)

  • Bankole Osita Awuzie

    (Centre for Sustainable Smart Cities 4.0, Faculty of Engineering, Built Environment and Information Technology, Central University of Technology, Bloemfontein 9301, South Africa)

  • Alfred Beati Ngowi

    (Office of the Deputy Vice Chancellor: Research, Innovation and Engagement, Central University of Technology, Bloemfontein 9301, South Africa)

  • Richard C. Millham

    (ICT and Society Research Group, Department of Information Technology, Durban University of Technology, Durban 4001, South Africa)

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.

Suggested Citation

  • Israel Edem Agbehadji & Bankole Osita Awuzie & Alfred Beati Ngowi & 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," IJERPH, MDPI, vol. 17(15), pages 1-16, July.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:15:p:5330-:d:389160
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    References listed on IDEAS

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    1. Lei Qin & Qiang Sun & Yidan Wang & Ke-Fei Wu & Mingchih Chen & Ben-Chang Shia & Szu-Yuan Wu, 2020. "Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index," IJERPH, MDPI, vol. 17(7), pages 1-14, March.
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    6. Nicola Luigi Bragazzi & Haijiang Dai & Giovanni Damiani & Masoud Behzadifar & Mariano Martini & Jianhong Wu, 2020. "How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic," IJERPH, MDPI, vol. 17(9), pages 1-8, May.
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    Cited by:

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    3. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    4. Yueli Mei & Xiuyun Guo & Zhihao Chen & Yingzhi Chen, 2022. "An Effective Mechanism for the Early Detection and Containment of Healthcare Worker Infections in the Setting of the COVID-19 Pandemic: A Systematic Review and Meta-Synthesis," IJERPH, MDPI, vol. 19(10), pages 1-20, May.
    5. Zhang, Qingyu & Gao, Bohong & Luqman, Adeel, 2022. "Linking green supply chain management practices with competitiveness during covid 19: The role of big data analytics," Technology in Society, Elsevier, vol. 70(C).
    6. Ignacio Rodríguez-Rodríguez & José-Víctor Rodríguez & Niloofar Shirvanizadeh & Andrés Ortiz & Domingo-Javier Pardo-Quiles, 2021. "Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining," IJERPH, MDPI, vol. 18(16), pages 1-29, August.
    7. Saheb, Tahereh & Sabour, Elham & Qanbary, Fatimah & Saheb, Tayebeh, 2022. "Delineating privacy aspects of COVID tracing applications embedded with proximity measurement technologies & digital technologies," Technology in Society, Elsevier, vol. 69(C).
    8. Waleed Al Shehri & Jameel Almalki & Rashid Mehmood & Khalid Alsaif & Saeed M. Alshahrani & Najlaa Jannah & Someah Alangari, 2022. "A Novel COVID-19 Detection Technique Using Deep Learning Based Approaches," Sustainability, MDPI, vol. 14(19), pages 1-12, September.

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