Numerical solution of the biological SIR model for COVID-19 with convergence analysis

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2025-09-17 DOI:10.1016/j.jocs.2025.102704

Walid Remili , Wen-Xiu Ma

引用次数: 0

Abstract

This study investigates the numerical solution of the biological Susceptible–Infectious–Recovered model for COVID-19 over extended time intervals using the shifted Chebyshev polynomial collocation method. Initially, the original problem is reformulated into a nonlinear Volterra integral equation for the susceptible population. The shifted Chebyshev polynomials are then employed to derive the numerical solution. A comprehensive convergence analysis of the collocation method is conducted to ensure the reliability and accuracy of the proposed approach. Finally, numerical simulations are performed for various parameter configurations that influence the system’s coefficients. Our method is compared with existing approaches, providing insights into the model’s dynamics under different conditions.

查看原文本刊更多论文

基于收敛分析的COVID-19生物SIR模型数值解

本文利用移位切比雪夫多项式搭配法研究了延长时间间隔的COVID-19生物易感-感染-恢复模型的数值解。首先，将原问题转化为敏感群体的非线性Volterra积分方程。然后利用移位的切比雪夫多项式推导出数值解。为了保证所提方法的可靠性和准确性，对配置方法进行了全面的收敛性分析。最后，对影响系统系数的各种参数配置进行了数值模拟。我们的方法与现有方法进行了比较，提供了不同条件下模型动力学的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).