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How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic

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  • Nicola Luigi Bragazzi

    (Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
    Postgraduate School of Public Health, Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy)

  • Haijiang Dai

    (Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada)

  • Giovanni Damiani

    (Department of Dermatology, Case Western Reserve University, Cleveland, OH 44195, USA
    Clinical Dermatology, I.R.C.C.S. Istituto Ortopedico Galeazzi, 20161 Milan, Italy
    Department of Biomedical, Surgical and Dental Sciences, University of Milan, 20122 Milan, Italy)

  • Masoud Behzadifar

    (Social Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad 6813833946, Iran)

  • Mariano Martini

    (Postgraduate School of Public Health, Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy)

  • Jianhong Wu

    (Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada)

Abstract

SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:9:p:3176-:d:353387
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    References listed on IDEAS

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    1. Nicola Luigi Bragazzi & Ottavia Guglielmi & Sergio Garbarino, 2019. "SleepOMICS: How Big Data Can Revolutionize Sleep Science," IJERPH, MDPI, vol. 16(2), pages 1-13, January.
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    Cited by:

    1. Savvas Papagiannidis & Dinara Davlembayeva, 2022. "Bringing Smart Home Technology to Peer-to-Peer Accommodation: Exploring the Drivers of Intention to Stay in Smart Accommodation," Information Systems Frontiers, Springer, vol. 24(4), pages 1189-1208, August.
    2. Volkmar, Gioia & Fischer, Peter M. & Reinecke, Sven, 2022. "Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management," Journal of Business Research, Elsevier, vol. 149(C), pages 599-614.
    3. Abdelrahman E. E. Eltoukhy & Ibrahim Abdelfadeel Shaban & Felix T. S. Chan & Mohammad A. M. Abdel-Aal, 2020. "Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations," IJERPH, MDPI, vol. 17(19), pages 1-25, September.
    4. Moon, Seungyeon & Lee, Heesang, 2024. "Identifying technological opportunities using enhanced tech mining: The case of the E-health industry," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    5. 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.
    6. Monday Osayande & Osagie Osifo, 2024. "Application Of Covid-19 Data: Investigating The Impact On Weekly Stock Market Returns In Nigeria," Journal of Academic Research in Economics, Spiru Haret University, Faculty of Accounting and Financial Management Constanta, vol. 16(2 (July)), pages 403-416.
    7. Antonio Sarría-Santamera & Alua Yeskendir & Tilektes Maulenkul & Binur Orazumbekova & Abduzhappar Gaipov & Iñaki Imaz-Iglesia & Lorena Pinilla-Navas & Teresa Moreno-Casbas & Teresa Corral, 2021. "Population Health and Health Services: Old Challenges and New Realities in the COVID-19 Era," IJERPH, MDPI, vol. 18(4), pages 1-5, February.
    8. Behl, Abhishek & Gaur, Jighyasu & Pereira, Vijay & Yadav, Rambalak & Laker, Benjamin, 2022. "Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – A multi-theoretical approach," Journal of Business Research, Elsevier, vol. 148(C), pages 378-389.
    9. Alexander E. Kentikelenis & Leonard Seabrooke, 2022. "Governing and Measuring Health Security: The Global Push for Pandemic Preparedness Indicators," Global Policy, London School of Economics and Political Science, vol. 13(4), pages 571-578, September.
    10. Mohapatra, Biswajit & Tripathy, Sushanta & Singhal, Deepak & Saha, Rajnandini, 2022. "Significance of digital technology in manufacturing sectors: Examination of key factors during Covid-19," Research in Transportation Economics, Elsevier, vol. 93(C).
    11. P. V. Thayyib & Rajesh Mamilla & Mohsin Khan & Humaira Fatima & Mohd Asim & Imran Anwar & M. K. Shamsudheen & Mohd Asif Khan, 2023. "State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary," Sustainability, MDPI, vol. 15(5), pages 1-38, February.

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