On the accuracy of ARIMA based prediction of COVID-19 spread.
On the accuracy of ARIMA based prediction of COVID-19 spread.
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DOI：
10.1016/j.rinp.2021.104509
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发表时间：
2021-08
影响因子：
5.3
通讯作者：
Al-Anzi FS
中科院分区：
文献类型：
--
作者：
Alabdulrazzaq H;Alenezi MN;Rawajfih Y;Alghannam BA;Al-Hassan AA;Al-Anzi FS
COVID-19于二零二零年三月被世界卫生组织宣布为全球大流行病，截至二零二零年五月初，全球已感染逾400万人，死亡人数超过30万人。世界各地的许多研究人员采用了各种预测技术，如易感-感染-传播模型，易感-暴露-传播-传播模型和自回归综合移动平均模型（ARIMA）来预测这一流行病的传播。ARIMA技术并没有被研究人员大量用于预测COVID-19，因为它不适合在复杂和动态的背景下使用。本研究的目的是测试ARIMA最佳拟合模型预测的准确性与整个预测时间过去后报告的实际值。我们调查和验证的ARIMA模型的准确性在一个相对较长的时间内使用科威特作为案例研究。我们首先优化模型的参数，通过检查自相关函数和部分自相关函数图表以及不同的准确性度量来找到最佳拟合。然后，我们使用最佳拟合模型预测了科威特渐进式预防计划不同阶段的COVID-19确诊和康复病例。结果表明，尽管疾病的动态性质和科威特政府的不断修正，观察到的大部分时间内的实际值都在我们选择的ARIMA模型预测的95%置信区间内。皮尔森的预测点与实际记录数据的相关系数为0.996。这表明这两组数据高度相关。我们的ARIMA模型提供的预测精度是适当的和令人满意的。
COVID-19 was declared a global pandemic by the World Health Organization in March 2020, and has infected more than 4 million people worldwide with over 300,000 deaths by early May 2020. Many researchers around the world incorporated various prediction techniques such as Susceptible–Infected–Recovered model, Susceptible–Exposed–Infected–Recovered model, and Auto Regressive Integrated Moving Average model (ARIMA) to forecast the spread of this pandemic. The ARIMA technique was not heavily used in forecasting COVID-19 by researchers due to the claim that it is not suitable for use in complex and dynamic contexts. The aim of this study is to test how accurate the ARIMA best-fit model predictions were with the actual values reported after the entire time of the prediction had elapsed. We investigate and validate the accuracy of an ARIMA model over a relatively long period of time using Kuwait as a case study. We started by optimizing the parameters of our model to find a best-fit through examining auto-correlation function and partial auto correlation function charts, as well as different accuracy measures. We then used the best-fit model to forecast confirmed and recovered cases of COVID-19 throughout the different phases of Kuwait’s gradual preventive plan. The results show that despite the dynamic nature of the disease and constant revisions made by the Kuwaiti government, the actual values for most of the time period observed were well within bounds of our selected ARIMA model prediction at 95% confidence interval. Pearson’s correlation coefficient for the forecast points with the actual recorded data was found to be 0.996. This indicates that the two sets are highly correlated. The accuracy of the prediction provided by our ARIMA model is both appropriate and satisfactory.
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影响因子：
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作者：
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通讯作者：
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DOI：
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发表时间：
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影响因子：
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