College of Intelligence and Computing, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, China.
Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China.
BMC Bioinformatics. 2019 Nov 25;20(Suppl 18):575. doi: 10.1186/s12859-019-3131-8.
Influenza is an infectious respiratory disease that can cause serious public health hazard. Due to its huge threat to the society, precise real-time forecasting of influenza outbreaks is of great value to our public.
In this paper, we propose a new deep neural network structure that forecasts a real-time influenza-like illness rate (ILI%) in Guangzhou, China. Long short-term memory (LSTM) neural networks is applied to precisely forecast accurateness due to the long-term attribute and diversity of influenza epidemic data. We devise a multi-channel LSTM neural network that can draw multiple information from different types of inputs. We also add attention mechanism to improve forecasting accuracy. By using this structure, we are able to deal with relationships between multiple inputs more appropriately. Our model fully consider the information in the data set, targetedly solving practical problems of the Guangzhou influenza epidemic forecasting.
We assess the performance of our model by comparing it with different neural network structures and other state-of-the-art methods. The experimental results indicate that our model has strong competitiveness and can provide effective real-time influenza epidemic forecasting.
流感是一种传染性呼吸道疾病，可对公众健康造成严重危害。由于流感对社会的巨大威胁，对流感爆发进行精确的实时预测对我们的公众具有重要意义。
本文提出了一种新的深度神经网络结构，用于预测中国广州的实时流感样疾病发病率（ILI%）。长短期记忆（LSTM）神经网络由于流感流行数据的长期属性和多样性，被应用于精确预测精度。我们设计了一个多通道 LSTM 神经网络，可以从不同类型的输入中提取多种信息。我们还添加了注意力机制以提高预测精度。通过使用这种结构，我们能够更适当地处理多个输入之间的关系。我们的模型充分考虑了数据集中的信息，有针对性地解决了广州流感流行预测的实际问题。
我们通过将我们的模型与不同的神经网络结构和其他最先进的方法进行比较来评估模型的性能。实验结果表明，我们的模型具有较强的竞争力，可以提供有效的实时流感流行预测。
