{
      "metadata_visibility": "show",
      "abstract": "The COVID-19 pandemic has had a profound impact on public health, economies, and societies worldwide. The pandemic has led to widespread illness and mortality, particularly among vulnerable populations such as the elderly, individuals with preexisting health conditions, and frontline healthcare workers. This has caused strain on the healthcare systems, which leads to shortages of hospital beds, medical supplies, and healthcare personnel. The global economy has also suffered with many countries experiencing recessions or sharp contractions in economic activity. This resulted in widespread job losses, increased unemployment rates, and financial hardship for individuals and families. In the United States, the economic toll of the COVID-19 pandemic is estimated to reach 14 trillion by the end of 2023. Due to the far-reaching social and economic ramifications, there is a need to enhance predictive capabilities. Several key technologies such as big data analytics, artificial intelligence, and machine learning have been studied in pandemic prediction research. Nonetheless, there is a limited understanding on the challenges and future implications of these technologies. This paper explores the opportunities, limitations, and future implications in pandemic prediction by providing a comprehensive review of the relevant literature and explores a broad spectrum of issues from technology to ethical considerations. The contribution of this study is twofold. Firstly, it contributes to the research and development efforts in data science, artificial intelligence, and digital health technologies in the context of pandemic prediction. Secondly, this study provides valuable insights and raises awareness among policymakers, healthcare professionals, researchers, and organizations on the importance of pandemic prediction, public health preparedness, and global health security. \u00c2\u00a9 2025 Elsevier Inc. All rights reserved.",
      "eprint_status": "archive",
      "datestamp": "2026-04-15 02:10:06",
      "keywords": "COVID-19; Employment; Health care; Machine learning; Public health; Technological forecasting; Economic activities; Economy and society; Emerging technologies; Frontline; Global economies; Health condition; Healthcare systems; Healthcare workers; Machine-learning; Pandemic prediction; Big data",
      "note": "Cited by: 2",
      "official_url": "https://www.scopus.com/pages/publications/105019732737?origin=resultslist",
      "userid": 1,
      "dir": "disk0/00/02/05/54",
      "lastmod": "2026-04-15 02:10:06",
      "isbn": "978-044333871-7; 978-044333872-4",
      "rev_number": 3,
      "creators": [
        {
          "name": {
            "given": "Khairul Shafee",
            "lineage": null,
            "honourific": null,
            "family": "Kalid"
          }
        },
        {
          "name": {
            "lineage": null,
            "honourific": null,
            "family": "Sugathan",
            "given": "Savita K."
          }
        },
        {
          "name": {
            "honourific": null,
            "lineage": null,
            "family": "Naji",
            "given": "Gehad Mohamed Ahmed"
          }
        },
        {
          "name": {
            "given": "Ganesh",
            "lineage": null,
            "honourific": null,
            "family": "Kumar"
          }
        }
      ],
      "refereed": "TRUE",
      "title": "Future implications, opportunities, and limitations in pandemic prediction",
      "publication": "The Prediction of Future Pandemics: Artificial Intelligence and Nanotechnology Approaches",
      "uri": "https://khub.utp.edu.my/scholars/id/eprint/20554",
      "type": "book",
      "eprintid": 20554,
      "full_text_status": "none",
      "id_number": "10.1016/B978-0-443-33871-7.00011-8",
      "pagerange": "203 - 229",
      "status_changed": "2026-04-15 02:10:06",
      "publisher": "Elsevier",
      "ispublished": "pub",
      "date": 2025
    }