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Forecasting dengue cases through time-series modeling with Google Trends and deep neural networks

Author

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  • Cheong, Kang Hao
  • Li, Kainan
  • Yu, Dengxiu
  • Zhao, Xinxing

Abstract

Dengue fever remains a persistent public health challenge in tropical regions, characterized by complex transmission dynamics and nonlinear outbreak patterns. In this study, we propose a data-driven forecasting framework that fuses real-time public interest signals captured through Google Trends, with a suite of advanced deep learning models. Our approach leverages the inherent nonlinearities in online search behavior to anticipate weekly dengue incidence, achieving state-of-the-art performance across multiple forecasting horizons. Remarkably, we find that a single search term (“dengue”) exhibits strong predictive power, outperforming multivariate feature sets in several models. The findings highlight the potential of low-cost, population-level digital traces as proxies for epidemiological signals and offer a practical, interpretable, and scalable methodology for early outbreak detection in complex systems.

Suggested Citation

  • Cheong, Kang Hao & Li, Kainan & Yu, Dengxiu & Zhao, Xinxing, 2025. "Forecasting dengue cases through time-series modeling with Google Trends and deep neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 201(P3).
    • Handle: RePEc:eee:chsofr:v:201:y:2025:i:p3:s0960077925013037
    DOI: 10.1016/j.chaos.2025.117290
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    References listed on IDEAS

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