Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks
Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks
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DOI：
10.1016/j.asoc.2020.106626
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发表时间：
2020-11-01
影响因子：
8.7
通讯作者：
Gupta, Deepak
中科院分区：
文献类型：
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作者：
Hazarika, Barenya Bikash;Gupta, Deepak
世界各地的研究人员正在应用COVID-19的各种预测模型，以做出明智的决策并采取适当的控制措施。由于高度的不确定性和缺乏必要的数据，传统的模型在长期预测中表现出较低的准确性。虽然文献中包含了几个试图解决这个问题，有必要提高现有模型的基本预测能力。因此，根据二零二零年七月十日的报告，本研究专注于对COVID-19在前五个受影响最严重国家的传播进行建模和预测。它们是巴西、印度、秘鲁、俄罗斯和美国。为此，将随机向量函数连接（RVFL）网络与一维离散小波变换相结合，提出了小波耦合RVFL（WCRVFL）网络。将该模型的预测性能与最新的支持向量回归（SVR）模型和传统的RVFL模型进行了比较。一个60天的每日预报也显示了所提出的模型。实验结果表明WCRVFL模型在COVID-19传播预测方面的潜力。(C)2020 Elsevier B. V.保留所有权利。
Researchers around the world are applying various prediction models for COVID-19 to make informed decisions and impose appropriate control measures. Because of a high degree of uncertainty and lack of necessary data, the traditional models showed low accuracy over the long term forecast. Although the literature contains several attempts to address this issue, there is a need to improve the essential prediction capability of existing models. Therefore, this study focuses on modelling and forecasting of COVID-19 spread in the top 5 worst-hit countries as per the reports on 10th July 2020. They are Brazil, India, Peru, Russia and the USA. For this purpose, the popular and powerful random vector functional link (RVFL) network is hybridized with 1-D discrete wavelet transform and a wavelet-coupled RVFL (WCRVFL) network is proposed. The prediction performance of the proposed model is compared with the state-of-the-art support vector regression (SVR) model and the conventional RVFL model. A 60 day ahead daily forecasting is also shown for the proposed model. Experimental results indicate the potential of the WCRVFL model for COVID-19 spread forecasting. (C) 2020 Elsevier B.V. All rights reserved.