A deep learning based hybrid architecture for weekly dengue incidences forecasting. (March 2023) - Zhao, Xinxing; Li, Kainan; Ang, Candice Ke En; Cheong, Kang Hao
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. Highlights: Hybrid deep learning models are developed for dengue incidence forecasting. The models give excellent forecasting accuracy up to 87.72% for weekly cases. Up to 4 weeks in advance forecasting is provided. The effects of different number of deep layers and used features are discussed.