Pham Phu, Pedrycz Witold, Vo Bay
Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Viet Nam.
Department of Electrical & Computer Engineering, University of Alberta, Edmonton T6R 2V4, Canada.
Expert Syst Appl. 2022 Oct 1;203:117514. doi: 10.1016/j.eswa.2022.117514. Epub 2022 May 13.
For preventing the outbreaks of Covid-19 infection in different countries, many organizations and governments have extensively studied and applied different kinds of quarantine isolation policies, medical treatments as well as organized massive/fast vaccination strategy for over-18 citizens. There are several valuable lessons have been achieved in different countries this Covid-19 battle. These studies have presented the usefulness of prompt actions in testing, isolating confirmed infectious cases from community as well as social resource planning/optimization through data-driven anticipation. In recent times, many studies have demonstrated the effectiveness of short/long-term forecasting in number of new Covid-19 cases in forms of time-series data. These predictions have directly supported to effectively optimize the available healthcare resources as well as imposing suitable policies for slowing down the Covid-19 spreads, especially in high-populated cities/regions/nations. There are several progresses of deep neural architectures, such as recurrent neural network (RNN) have demonstrated significant improvements in analyzing and learning the time-series datasets for conducting better predictions. However, most of recent RNN-based techniques are considered as unable to handle chaotic/non-smooth sequential datasets. The consecutive disturbances and lagged observations from chaotic time-series dataset like as routine Covid-19 confirmed cases have led to the low performance in temporal feature learning process through recent RNN-based models. To meet this challenge, in this paper, we proposed a novel dual attention-based sequential auto-encoding architecture, called as: DAttAE. Our proposed model supports to effectively learn and predict the new Covid-19 cases in forms of chaotic and non-smooth time series dataset. Specifically, the integration between dual self-attention mechanism in a given Bi-LSTM based auto-encoder in our proposed model supports to directly focus the model on a specific time-range sequence in order to achieve better prediction. We evaluated the performance of our proposed DAttAE model by comparing with multiple traditional and state-of-the-art deep learning-based techniques for time-series prediction task upon different real-world datasets. Experimental outputs demonstrated the effectiveness of our proposed attention-based deep neural approach in comparing with state-of-the-art RNN-based architectures for time series based Covid-19 outbreak prediction task.
为防止新冠病毒在不同国家爆发，许多组织和政府广泛研究并应用了各种检疫隔离政策、医疗手段，还为18岁以上公民组织了大规模/快速接种疫苗策略。在这场抗击新冠疫情的战斗中，不同国家取得了一些宝贵经验。这些研究表明了迅速行动在检测、将确诊感染病例与社区隔离以及通过数据驱动预测进行社会资源规划/优化方面的作用。近来，许多研究以时间序列数据的形式证明了对新冠新增病例进行短期/长期预测的有效性。这些预测直接有助于有效优化可用医疗资源，并实施适当政策以减缓新冠病毒传播，特别是在人口密集的城市/地区/国家。深度神经架构有多项进展，比如递归神经网络（RNN）在分析和学习时间序列数据集以进行更好预测方面有显著改进。然而，最近大多数基于RNN的技术被认为无法处理混乱/不平稳的序列数据集。像日常新冠确诊病例这样的混乱时间序列数据集的连续干扰和滞后观测，导致通过最近基于RNN的模型在时间特征学习过程中表现不佳。为应对这一挑战，本文提出了一种新颖的基于双重注意力的序列自动编码架构，称为DAttAE。我们提出的模型支持以混乱和不平稳时间序列数据集的形式有效学习和预测新冠新增病例。具体而言，我们提出的模型中基于双向长短期记忆网络（Bi-LSTM）的自动编码器中的双重自注意力机制之间的整合，支持直接将模型聚焦于特定时间范围序列，以实现更好的预测。我们通过与多种传统和基于深度学习的先进技术在不同真实世界数据集上进行时间序列预测任务的比较，评估了我们提出的DAttAE模型的性能。实验结果表明，与基于RNN的先进架构相比，我们提出的基于注意力的深度神经方法在基于时间序列的新冠疫情爆发预测任务中是有效的。
