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Forecasting COVID-19 pandemic: Unknown unknowns and predictive monitoring

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  • Luo, Jianxi

Abstract

During the current COVID-19 pandemic, there have been many efforts to forecast infection cases, deaths, and courses of development, using a variety of mechanistic, statistical, or time-series models. Some forecasts have influenced policies in some countries. However, forecasting future developments in the pandemic is fundamentally challenged by the innate uncertainty rooted in many “unknown unknowns,” not just about the contagious virus itself but also about the intertwined human, social, and political factors, which co-evolve and keep the future of the pandemic open-ended. These unknown unknowns make the accuracy-oriented forecasting misleading. To address the extreme uncertainty of the pandemic, a heuristic approach and exploratory mindset is needed. Herein, grounded on our own COVID-19 forecasting experiences, I propose and advocate the “predictive monitoring” paradigm, which synthesizes prediction and monitoring, to make government policies, organization planning, and individual mentality heuristically future-informed despite the extreme uncertainty.

Suggested Citation

  • Luo, Jianxi, 2021. "Forecasting COVID-19 pandemic: Unknown unknowns and predictive monitoring," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:tefoso:v:166:y:2021:i:c:s0040162521000342
    DOI: 10.1016/j.techfore.2021.120602
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    References listed on IDEAS

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    1. Mitroff, Ian I., 2020. "Corona virus: A prime example of a wicked mess," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    2. Chen, Zhuo, 2020. "COVID-19: A revelation – A reply to Ian Mitroff," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    3. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
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    Cited by:

    1. Messner, Wolfgang, 2023. "The contingency impact of culture on health security capacities for pandemic preparedness: A moderated Bayesian inference analysis," Journal of International Management, Elsevier, vol. 29(5).
    2. Lopreite, Milena & Misuraca, Michelangelo & Puliga, Michelangelo, 2024. "Outbreak and integration of social media in public health surveillance systems: A policy review through BERT embedding technique," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    3. Salma Benchekroun & V. G. Venkatesh & Ilham Dkhissi & D. Jinil Persis & Arunmozhi Manimuthu & M. Suresh & V. Raja Sreedharan, 2023. "Managing the retail operations in the COVID‐19 pandemic: Evidence from Morocco," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(1), pages 424-447, January.
    4. Naeini, Ali Bonyadi & Zamani, Mehdi & Daim, Tugrul U. & Sharma, Mahak & Yalcin, Haydar, 2022. "Conceptual structure and perspectives on “innovation management”: A bibliometric review," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    5. Godé, Cécile & Brion, Sébastien, 2024. "The affordance-actualization process of predictive analytics: Towards a configurational framework of a predictive policing system," Technological Forecasting and Social Change, Elsevier, vol. 204(C).

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