Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries
Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries
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DOI：
10.1016/j.chaos.2020.110086
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发表时间：
2020-10-01
影响因子：
7.8
通讯作者：
Kumar, Jatinder
中科院分区：
文献类型：
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作者：
Singh, Sarbjit;Parmar, Kulwinder Singh;Kumar, Jatinder
关于最近发现的致命冠状病毒病（COVID-19）的讨论现在在地球仪很常见，该疾病于2019年12月起源于中国武汉。这是由严重急性呼吸道综合征冠状病毒2（SARS-CoV-2）引起的传染性甚至危及生命的疾病。它已从发源地迅速蔓延到其他国家，感染了全球数百万人。为了理解未来的现象，需要具有最小预测误差的强大数学模型。在本研究中，自回归积分移动平均（ARIMA）和最小二乘支持向量机（LS-SVM）模型被应用到由每日确诊病例的SARS-CoV-2在世界上受影响最严重的五个国家的数据建模和预测一个月内确诊病例。为了验证模型的正确性，将预测结果与试验数据进行了对比.结果显示，LS-SVM模型的准确性优于ARIMA模型，并且还表明在所有研究中的国家中，SARS-CoV-2确诊病例迅速增加。这一分析将有助于各国政府提前采取必要行动，包括准备隔离病房、提供药品和医务人员、决定封锁、培训志愿者和经济计划。(C)2020爱思唯尔有限公司保留所有权利。
Discussions about the recently identified deadly coronavirus disease (COVID-19) which originated in Wuhan, China in December 2019 are common around the globe now. This is an infectious and even life-threatening disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has rapidly spread to other countries from its originating place infecting millions of people globally. To understand future phenomena, strong mathematical models are required with the least prediction errors. In the present study, autoregressive integrated moving average (ARIMA) and least square support vector machine (LS-SVM) models are applied to the data consisting of daily confirmed cases of SARS-CoV-2 in the most affected five countries of the world for modeling and predicting one-month confirmed cases of this disease. To validate these models, the prediction results were tested by comparing it with testing data. The results revealed better accuracy of the LS-SVM model over the ARIMA model and also suggested a rapid rise of SARS-CoV-2 confirmed cases in all the countries under study. This analysis would help governments to take necessary actions in advance associated with the preparation of isolation wards, availability of medicines and medical staff, a decision on lockdown, training of volunteers, and economic plans. (C) 2020 Elsevier Ltd. All rights reserved.