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A review of big data analytics models for assessing non-pharmaceutical interventions for COVID-19 pandemic management

Author

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  • Fatemeh Navazi
  • Yufei Yuan
  • Norm Archer

Abstract

Before vaccine development during the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were the only solutions to mitigate COVID-19 infections. Governments continued to use them even after starting vaccine administration. In this research, we review different big data analytics models that assess and optimize the effectiveness of NPIs. These models are categorized into three big data analytics groups: descriptive, which measures the infection rate changes caused by NPIs; predictive, which predicts the future of the pandemic by implementing several NPIs; and data-driven prescriptive, which suggests optimal control policies. We further analyze each method's basic assumptions, limitations, and applicability during different pandemic phases and under different scenarios. This review of COVID-19 NPI evaluation methods will be beneficial for decision-makers to know which model to select for policy-making in possible future pandemics, which are more likely recently due to globalization. Finally, we suggest some future research directions.

Suggested Citation

  • Fatemeh Navazi & Yufei Yuan & Norm Archer, 2024. "A review of big data analytics models for assessing non-pharmaceutical interventions for COVID-19 pandemic management," Journal of Management Analytics, Taylor & Francis Journals, vol. 11(3), pages 358-388, July.
    • Handle: RePEc:taf:tjmaxx:v:11:y:2024:i:3:p:358-388
    DOI: 10.1080/23270012.2024.2372632
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