基于数学和动态神经网络模型的新型冠状病毒在伊拉克传播的回归与验证研究:以2020年前六个月为例

Q3 Agricultural and Biological Sciences
A. Khadom, A. K. Al-Jiboory, Mustafa S. Mahdi, Hameed B. Mahood
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引用次数: 0

摘要

新冠肺炎的急剧传播使整个世界处于危险之中。在这项工作中,研究了新冠肺炎在伊拉克的传播。由于这种疾病的阳性病例和死亡人数的增加,全球经济和世界专业人员面临巨大压力。因此,建立数学模型来预测这种严重疾病的发展是非常有用的。它有助于预测伊拉克未来的病例数。因此,已经开发了动态神经网络和曲线拟合技术来构建这样的模型。来自世界卫生组织(世界卫生组织)的数据被用作数学模型构建的来源。2020年2月25日至6月18日期间用于动态神经网络(DNN)的回归、验证和模型构建。九个样本(2020年6月19日至27日)用于预测未来的感染和死亡病例。描述性统计研究表明,与2020年前几个月相比,6月份的标准差变化很大。提出了三个数学模型。线性、多项式(二阶、三阶和四阶)和指数模型用于关联确诊感染病例(CIC)和确诊死亡病例(CDC),这些模型将因变量表示为时间的函数(自变量)。采用基于最小二乘法的非线性回归方法来估计模型的系数。指数模型最显著,CIC和CDC的相关系数分别为0.9964和0.9974。验证分析显示,使用指数模型的真实病例和预测病例之间存在显著偏差。然而,DNN模型显示出比指数模型更好的响应。客观和主观的比较证明了这一点。最后,CIC和CDC可能会随着时间的推移而增加,到2020年6月底分别接近50000和2000。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Regression and validation studies of the spread of novel COVID-19 in Iraq using mathematical and dynamic neural networks models: A case of the first six months of 2020
The dramatic spread of COVID-19 has put the entire world at risk. In this work, the spread of COVID-19 in Iraq has been studied. Due to the increase in the number of positive cases and deaths with this disease, huge pressure acts on the economy and world professionals worldwide. Therefore, building mathematical models to predict the growth of this serious disease is extremely useful. It helps to predict the future numbers of cases in Iraq. Therefore, dynamic neural networks and curve fitting techniques have been developed to construct such a model. Data from the World Health Organization (WHO) are used as a source for mathematical model construction. The period between 25, February to 18, June 2020 was used for regression, validation, and model construction of Dynamic Neural Networks (DNN). Nine samples (19 – 27 June 2020) were used for predicting the future infected and death cases. Descriptive statistical studies showed that the standard deviation varies sharply on June as compared with earlier months of 2020. Three mathematical models are proposed. Linear, polynomials (2nd, 3rd, and 4th orders), and exponential models are used to correlate confirmed infected cases (CIC) and confirmed death cases (CDC) that represent the dependent variables as function of time (independent variable). Nonlinear regression based on least-square method is used to estimate the coefficients of models.  Exponential models were the most significant with 0.9964 and 0.9974 correlation coefficients for CIC and CDC, respectively. Validation analysis showed a significant deviation between real and predicted cases using exponential models. However, DNN models showed better response than exponential models. This is evidenced by objective and subjective comparisons. Finally, the CIC and CDC may be increased with time to approach 50000 and 2000 respectively at the end of June 2020.
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来源期刊
caspian journal of environmental sciences
caspian journal of environmental sciences Environmental Science-Environmental Science (all)
CiteScore
2.30
自引率
0.00%
发文量
0
审稿时长
5 weeks
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