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

Aleksandr Shishkin, Amanda Bleichrodt, Ruiyan Luo, Pavel Skums, Gerardo Chowell () and Alexander Kirpich ()
Additional contact information
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

Mathematics, 2024, vol. 12, issue 23, 1-17

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.

Keywords: forecasting; ARIMA; Prophet; GAM; mpox; monkeypox; Asia; China; South Korea; Japan; Thailand; model comparison; epidemiology; incidence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/23/3669/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/23/3669/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:23:p:3669-:d:1527597

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().