Zhou Binggui, Yang Guanghua, Shi Zheng, Ma Shaodan
School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China.
State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering, University of Macau, 999078, Macao Special Administrative Region of China.
Appl Soft Comput. 2022 May;120:108691. doi: 10.1016/j.asoc.2022.108691. Epub 2022 Mar 9.
The worldwide outbreak of coronavirus disease 2019 (COVID-19) has triggered an unprecedented global health and economic crisis. Early and accurate forecasts of COVID-19 and evaluation of government interventions are crucial for governments to take appropriate interventions to contain the spread of COVID-19. In this work, we propose the Interpretable Temporal Attention Network (ITANet) for COVID-19 forecasting and inferring the importance of government interventions. The proposed model is with an encoder-decoder architecture and employs long short-term memory (LSTM) for temporal feature extraction and multi-head attention for long-term dependency caption. The model simultaneously takes historical information, a priori known future information, and pseudo future information into consideration, where the pseudo future information is learned with the covariate forecasting network (CFN) and multi-task learning (MTL). In addition, we also propose the degraded teacher forcing (DTF) method to train the model efficiently. Compared with other models, the ITANet is more effective in the forecasting of COVID-19 new confirmed cases. The importance of government interventions against COVID-19 is further inferred by the Temporal Covariate Interpreter (TCI) of the model.
2019年冠状病毒病（COVID-19）在全球范围内的爆发引发了一场前所未有的全球健康和经济危机。对COVID-19进行早期准确预测以及评估政府干预措施，对于各国政府采取适当干预措施遏制COVID-19传播至关重要。在这项工作中，我们提出了用于COVID-19预测和推断政府干预措施重要性的可解释时间注意力网络（ITANet）。所提出的模型采用编码器-解码器架构，使用长短期记忆（LSTM）进行时间特征提取，并使用多头注意力进行长期依赖关系描述。该模型同时考虑历史信息、先验已知的未来信息和伪未来信息，其中伪未来信息是通过协变量预测网络（CFN）和多任务学习（MTL）学习得到的。此外，我们还提出了退化教师强制（DTF）方法来高效训练该模型。与其他模型相比，ITANet在预测COVID-19新增确诊病例方面更有效。该模型的时间协变量解释器（TCI）进一步推断了政府针对COVID-19干预措施的重要性。
