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Short-Term Predictions of the Trajectory of Mpox in East Asian Countries, 2022–2023: A Comparative Study of Forecasting Approaches

Author

Listed:
  • Aleksandr Shishkin

    (Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30303, USA)

  • Amanda Bleichrodt

    (Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30303, USA)

  • Ruiyan Luo

    (Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30303, USA)

  • Pavel Skums

    (School of Computing, University of Connecticut, Storrs, CT 06269, USA)

  • Gerardo Chowell

    (Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30303, USA)

  • Alexander Kirpich

    (Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30303, USA)

Abstract

The 2022–2023 mpox outbreak exhibited an uneven global distribution. While countries such as the UK, Brazil, and the USA were most heavily affected in 2022, many Asian countries, specifically China, Japan, South Korea, and Thailand, experienced the outbreak later, in 2023, with significantly fewer reported cases relative to their populations. This variation in timing and scale distinguishes the outbreaks in these Asian countries from those in the first wave. This study evaluates the predictability of mpox outbreaks with smaller case counts in Asian countries using popular epidemic forecasting methods, including the ARIMA, Prophet, GLM, GAM, n -Sub-epidemic, and Sub-epidemic Wave frameworks. Despite the fact that the ARIMA and GAM models performed well for certain countries and prediction windows, their results were generally inconsistent and highly dependent on the country, i.e., the dataset, as well as the prediction interval length. In contrast, n -Sub-epidemic Ensembles demonstrated more reliable and robust performance across different datasets and predictions, indicating the effectiveness of this model on small datasets and its utility in the early stages of future pandemics.

Suggested Citation

  • Aleksandr Shishkin & Amanda Bleichrodt & Ruiyan Luo & Pavel Skums & Gerardo Chowell & Alexander Kirpich, 2024. "Short-Term Predictions of the Trajectory of Mpox in East Asian Countries, 2022–2023: A Comparative Study of Forecasting Approaches," Mathematics, MDPI, vol. 12(23), pages 1-17, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3669-:d:1527597
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    References listed on IDEAS

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    1. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    2. Johannes Bracher & Evan L Ray & Tilmann Gneiting & Nicholas G Reich, 2021. "Evaluating epidemic forecasts in an interval format," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-15, February.
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