Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing, China.
Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing University of Technology, Chaoyang District, Beijing, China.
Front Public Health. 2024 May 20;12:1397260. doi: 10.3389/fpubh.2024.1397260. eCollection 2024.
This study focuses on enhancing the precision of epidemic time series data prediction by integrating Gated Recurrent Unit (GRU) into a Graph Neural Network (GNN), forming the GRGNN. The accuracy of the GNN (Graph Neural Network) network with introduced GRU (Gated Recurrent Units) is validated by comparing it with seven commonly used prediction methods.
The GRGNN methodology involves multivariate time series prediction using a GNN (Graph Neural Network) network improved by the integration of GRU (Gated Recurrent Units). Additionally, Graphical Fourier Transform (GFT) and Discrete Fourier Transform (DFT) are introduced. GFT captures inter-sequence correlations in the spectral domain, while DFT transforms data from the time domain to the frequency domain, revealing temporal node correlations. Following GFT and DFT, outbreak data are predicted through one-dimensional convolution and gated linear regression in the frequency domain, graph convolution in the spectral domain, and GRU (Gated Recurrent Units) in the time domain. The inverse transformation of GFT and DFT is employed, and final predictions are obtained after passing through a fully connected layer. Evaluation is conducted on three datasets: the COVID-19 datasets of 38 African countries and 42 European countries from worldometers, and the chickenpox dataset of 20 Hungarian regions from Kaggle. Metrics include Average Root Mean Square Error (ARMSE) and Average Mean Absolute Error (AMAE).
For African COVID-19 dataset and Hungarian Chickenpox dataset, GRGNN consistently outperforms other methods in ARMSE and AMAE across various prediction step lengths. Optimal results are achieved even at extended prediction steps, highlighting the model's robustness.
GRGNN proves effective in predicting epidemic time series data with high accuracy, demonstrating its potential in epidemic surveillance and early warning applications. However, further discussions and studies are warranted to refine its application and judgment methods, emphasizing the ongoing need for exploration and research in this domain.
本研究通过将门控循环单元（GRU）集成到图神经网络（GNN）中形成 GRGNN，旨在提高传染病时间序列数据预测的精度。通过与七种常用预测方法进行比较，验证了引入 GRU 的 GNN（图神经网络）网络的准确性。
GRGNN 方法采用 GNN（图神经网络）网络对多元时间序列进行预测，该网络通过集成 GRU（门控循环单元）得到改进。此外，引入了图形傅里叶变换（GFT）和离散傅里叶变换（DFT）。GFT 在频域中捕捉序列间的相关性，而 DFT 将数据从时域转换到频域，揭示时间节点的相关性。在 GFT 和 DFT 之后，通过在频域中进行一维卷积和门控线性回归、在谱域中进行图卷积以及在时域中进行 GRU（门控循环单元）对爆发数据进行预测。然后对 GFT 和 DFT 进行逆变换，并在经过全连接层后获得最终预测结果。在三个数据集上进行了评估：来自 worldometers 的 38 个非洲国家和 42 个欧洲国家的 COVID-19 数据集，以及来自 Kaggle 的 20 个匈牙利地区的水痘数据集。评估指标包括平均根均方误差（ARMSE）和平均绝对误差（AMAE）。
对于非洲 COVID-19 数据集和匈牙利水痘数据集，GRGNN 在各种预测步长下的 ARMSE 和 AMAE 上均优于其他方法。即使在扩展的预测步骤中，也能获得最佳结果，突出了模型的稳健性。
GRGNN 在高精度预测传染病时间序列数据方面表现出有效性，表明其在传染病监测和预警应用中的潜力。然而，需要进一步的讨论和研究来完善其应用和判断方法，强调在该领域不断探索和研究的必要性。
