登革热
卷积神经网络
深度学习
人工智能
建筑
计算机科学
人工神经网络
机器学习
病毒学
地理
医学
考古
作者
Xinxing Zhao,Kainan Li,Candice Ke En Ang,Kang Hao Cheong
标识
DOI:10.1016/j.chaos.2023.113170
摘要
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.
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