Forecasting the Monkeypox Outbreak Using Limited Data: A Case Study of Thailand
Sherif Eneye Shuaib1, Jirapond Muangprathub2, Arthit Intarasit1
1Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Mueang Pattani, Pattani 94000, Thailand.
Introduction:
On August 14, 2024, the World Health Organization (WHO) declared monkeypox (mpox) a Public Health Emergency of International Concern (PHEIC). Shortly thereafter, Thailand reported Asia's first confirmed case of the Clade Ib strain. Given Thailand's high international travel volume, anticipating short-term case trajectories is essential for situational awareness.
Methods:
We forecast monthly mpox cases using the Department of Disease Control (DDC) data from July 2022 to September 2024 and evaluate Poisson and negative binomial generalized linear models (GLMs), Holt-Winters exponential smoothing, NeuralProphet, and a stacked ensemble. Rolling-origin cross-validation was used to assess out-of-sample accuracy (MAE, RMSE, and MAPE) and empirical interval coverage (80% and 95%).
Results:
Holt-Winters provided the most accurate single-model forecasts by RMSE, while the stacked ensemble yielded the most reliable uncertainty calibration. Forecasts for October-December 2024 suggest relatively low case counts with non-negligible uncertainty, indicating the need for continued vigilance.
Discussion:
These findings are exploratory decision-support inputs that complement ongoing surveillance and may help inform adaptive planning under both low- and high-incidence scenarios.
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