Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study
Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study
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DOI：
10.1016/j.chaos.2020.110121
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发表时间：
2020-11-01
影响因子：
7.8
通讯作者：
Sun, Ying
中科院分区：
文献类型：
--
作者：
Zeroual, Abdelhafid;Harrou, Fouzi;Sun, Ying
新型冠状病毒(新冠肺炎)已在全球显著传播，并向研究界提出了新的挑战。尽管各国政府采取了许多遏制和疏远社会的措施，但对医疗保健系统的需求急剧增加，对感染患者的有效管理成为医院面临的一个挑战性问题。因此，对新感染和恢复的病例数量进行准确的短期预测，对于优化现有资源和遏制或减缓此类疾病的发展至关重要。最近，深度学习模型在处理不同应用中的时间序列数据时表现出了重要的改进。本文提出了五种深度学习方法的比较研究，以预测新病例和恢复病例的数量。具体而言，简单递归神经网络、长短期记忆算法、双向L短时记忆算法、门控递归单元算法和变分自编码算法已被应用于基于小数据量的新冠肺炎全球预测。这项研究基于从意大利、西班牙、法国、中国、美国和澳大利亚等6个国家收集的每日确诊和恢复病例。结果表明，深度学习模型在新冠肺炎案例预测中具有很好的应用前景，并且与其他算法相比具有更好的性能。(C)2020爱思唯尔有限公司。保留所有权利。
The novel coronavirus (COVID-19) has significantly spread over the world and comes up with new challenges to the research community. Although governments imposing numerous containment and social distancing measures, the need for the healthcare systems has dramatically increased and the effective management of infected patients becomes a challenging problem for hospitals. Thus, accurate short-term forecasting of the number of new contaminated and recovered cases is crucial for optimizing the available resources and arresting or slowing down the progression of such diseases. Recently, deep learning models demonstrated important improvements when handling time-series data in different applications. This paper presents a comparative study of five deep learning methods to forecast the number of new cases and recovered cases. Specifically, simple Recurrent Neural Network (RNN), Long short-term memory (L STM), Bidirectional L STM (BiL STM), Gated recurrent units (GRUs) and Variational AutoEncoder (VAE) algorithms have been applied for global forecasting of COVID-19 cases based on a small volume of data. This study is based on daily confirmed and recovered cases collected from six countries namely Italy, Spain, France, China, USA, and Australia. Results demonstrate the promising potential of the deep learning model in forecasting COVID-19 cases and highlight the superior performance of the VAE compared to the other algorithms. (c) 2020 Elsevier Ltd. All rights reserved.