Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, China.
Comput Biol Med. 2021 Jul;134:104482. doi: 10.1016/j.compbiomed.2021.104482. Epub 2021 May 13.
Influenza is a common respiratory disease that can cause human illness and death. Timely and accurate prediction of disease risk is of great importance for public health management and prevention. The influenza data belong to typical spatiotemporal data in that influenza transmission is influenced by regional and temporal interactions. Many existing methods only use the historical time series information for prediction, which ignores the effect of spatial correlations of neighboring regions and temporal correlations of different time periods. Mining spatiotemporal information for risk prediction is a significant and challenging issue. In this paper, we propose a new end-to-end spatiotemporal deep neural network structure for influenza risk prediction. The proposed model mainly consists of two parts. The first stage is the spatiotemporal feature extraction stage where two-stream convolutional and recurrent neural networks are constructed to extract the different regions and time granularity information. Then, a dynamically parametric-based fusion method is adopted to integrate the two-stream features and making predictions. In our work, we demonstrate that our method, tested on two influenza-like illness (ILI) datasets (US-HHS and SZ-HIC), achieved the best performance across all evaluation metrics. The results imply that our method has outstanding performance for spatiotemporal feature extraction and enables accurate predictions compared to other well-known influenza forecasting models.
流感是一种常见的呼吸道疾病，可导致人类患病和死亡。及时、准确地预测疾病风险对于公共卫生管理和预防至关重要。流感数据属于典型的时空数据，因为流感的传播受到区域和时间相互作用的影响。许多现有的方法仅使用历史时间序列信息进行预测，这忽略了相邻区域的空间相关性和不同时间段的时间相关性的影响。挖掘时空信息进行风险预测是一个重要而具有挑战性的问题。在本文中，我们提出了一种用于流感风险预测的新型端到端时空深度神经网络结构。所提出的模型主要由两部分组成。第一阶段是时空特征提取阶段，其中构建了两个双流卷积和循环神经网络来提取不同的区域和时间粒度信息。然后，采用基于动态参数的融合方法来整合双流特征并进行预测。在我们的工作中，我们证明了我们的方法在两个流感样疾病（ILI）数据集（美国 HHS 和深圳 HIC）上进行测试时，在所有评估指标上都取得了最佳性能。结果表明，与其他著名的流感预测模型相比，我们的方法在时空特征提取方面具有出色的性能，并且能够进行准确的预测。
