A deep learning based hybrid architecture for weekly dengue incidences forecasting

Xinxing Zhao, Kainan Li, Candice Ke En Ang and Kang Hao Cheong

Chaos, Solitons & Fractals, 2023, vol. 168, issue C

Abstract: Dengue is a mosquito-borne viral disease widely spread in tropical and subtropical regions. Its adverse impact on the human health and global economies cannot be overstated. In order to implement more effective vector control measures, mechanisms that can more accurately forecast dengue cases are needed more urgently than before. In this paper, a novel hybrid architecture which has the advantages of both convolutional neural networks and recurrent neural networks is being proposed to forecast weekly dengue incidence. The forecasting performance of this architecture reveals that the deep hybrid architecture outperforms other frequently used deep learning models in dengue forecasting tasks. We have also evaluated the proposed models against state-of-the-art studies in the literature, demonstrating that our proposed hybrid models utilizing recurrent networks with convolutional layers can provide a significant boost in dengue forecasting.

Keywords: Epidemiology; Time series forecasting; Dengue incidences forecasting; Deep learning; Hybrid models (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:168:y:2023:i:c:s0960077923000711

DOI: 10.1016/j.chaos.2023.113170

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