一种求解COVID-19模型的可靠数值模拟技术

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Communications in Mathematical Biology and Neuroscience Pub Date : 2023-01-01 DOI:10.28919/cmbn/7959
Mahdi A. Sabea, M. A. Mohammed
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引用次数: 0

摘要

。流行病学模型的特点是其系数具有随机性,而经典方法或解析方法和数值方法处理的是固定系数的系统,这使得这些方法不适用于具有随时间变化的系数的流行病学系统的解。因此,处理时间变化的数值模拟方法比其他方法更合适。这项研究的目的是将其中一些方法应用于COVID-19系统。利用以往研究中常用的两种有效方法,即蒙特卡罗有限差分法和平均拉丁超立方有限差分法来求解该系统。为便于比较,本文采用有限差分法对该系统进行数值求解。我们已经取得了很好的结果,可以对Covid - 19流行病自开始以来的行为进行分析和印象,并预测其未来几年的行为。所有的结果都以图表和表格的形式写出来。
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A reliable numerical simulation technique for solving COVID-19 model
. The nature of epidemiological models is characterized by randomness in their coefficients, while the classical or analytical and numerical methods deal with systems with fixed coefficients, which makes these methods inappropriate for solutions of epidemiological systems that have coefficients that change with time. For that, the numerical simulation methods that deal with time change are more appropriate than other ways. The aim of the research is to apply some of these methods to the COVID-19 system. Two efficient methods used for previous studies are used to solve this system, which are Monte Carlo Finite Difference Method and Mean Latin Hypercube Finite Difference Method. For the sake of comparison, a numerical method, the finite difference method, is used to solve this system. We have reached good results that give an analysis and impression of the behavior of the Covid 19 epidemic since its inception and predict its behavior for the next years. All results have been written in graphs and tabulated.
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来源期刊
Communications in Mathematical Biology and Neuroscience
Communications in Mathematical Biology and Neuroscience COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.10
自引率
15.40%
发文量
80
期刊介绍: Communications in Mathematical Biology and Neuroscience (CMBN) is a peer-reviewed open access international journal, which is aimed to provide a publication forum for important research in all aspects of mathematical biology and neuroscience. This journal will accept high quality articles containing original research results and survey articles of exceptional merit.
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