ORIGINAL RESEARCH article
Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1634138
This article is part of the Research TopicAdvances in Mathematical Modelling for Infectious Disease Control and PreventionView all 3 articles
A Comparative Study of Time Series Foundation Models for Hand, Foot and Mouth Disease Forecasting: TimesFM, Moirai, and Traditional Approaches
Provisionally accepted- Children’s Hospital of Chongqing Medical University, Chongqing, China
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Background: Hand, foot, and mouth disease (HFMD) is a pediatric infectious disease prevalent in the Asia-Pacific region, requiring accurate forecasting for effective public health interventions. This study aims to compare the performance of time series foundation models (TimesFM and Moirai) with traditional methods (ARIMA and LSTM) in predicting HFMD outbreaks across various datasets and forecasting horizons. Methods: The study analyzed weekly HFMD incidence data from Korea (2015–2024), Singapore (2012–2018), and Chongqing, China (2015–2024). Zero-shot versions of TimesFM (200 M and 500 M) and Moirai models were assessed against ARIMA and LSTM using forecasting horizons of 1 week, 5 weeks, and 10 weeks. Lookback windows of 50 and 100 weeks were used across experiments. Performance was evaluated based on forecasting accuracy across all datasets. Computational resource requirements were also analyzed. Results: For 1-step predictions, ARIMA and Moirai delivered comparable results. TimesFM-500 M achieved the best performance for 5-step predictions with 100-week lookback windows across all datasets. For 10-step predictions, TimesFM-200 M performed well with 50-week lookback windows but showed weaker results with longer historical data. Foundation models demonstrated the potential for robust HFMD forecasting but required greater computational resources. Conclusions: Time series foundation models can effectively predict HFMD outbreaks. While these models require more computational resources, their zero-shot capabilities simplify the forecasting process by eliminating the need for retraining.
Keywords: HFMD, time series analysis, Foundation models, Epidemic prediction, Epidemiological modeling
Received: 23 May 2025; Accepted: 05 Sep 2025.
Copyright: © 2025 Wang, Huang, Chen, Qiu and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Li Qiu, Children’s Hospital of Chongqing Medical University, Chongqing, China
Ximing Xu, Children’s Hospital of Chongqing Medical University, Chongqing, China
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