Domain preserving and strongly converging explicit scheme for the stochastic SIS epidemic model

IF 2.6 2区 数学 Q1 MATHEMATICS, APPLIED Journal of Computational and Applied Mathematics Pub Date : 2025-03-01 Epub Date: 2024-08-22 DOI:10.1016/j.cam.2024.116219
Yiannis Kiouvrekis , Ioannis S. Stamatiou
{"title":"Domain preserving and strongly converging explicit scheme for the stochastic SIS epidemic model","authors":"Yiannis Kiouvrekis ,&nbsp;Ioannis S. Stamatiou","doi":"10.1016/j.cam.2024.116219","DOIUrl":null,"url":null,"abstract":"<div><p>In this article, we construct a numerical method for a stochastic version of the Susceptible–Infected–Susceptible (SIS) epidemic model, expressed by a suitable stochastic differential equation (SDE), by using the semi-discrete method to a suitable transformed process. We prove the strong convergence of the proposed method, with order 1, and examine its stability properties. Since SDEs generally lack analytical solutions, numerical techniques are commonly employed. Hence, the research will seek numerical solutions for existing stochastic models by constructing suitable numerical schemes and comparing them with other schemes. The objective is to achieve a qualitative and efficient approach to solving the equations. Additionally, for models that have not yet been proposed for stochastic modeling using SDEs, the research will formulate them appropriately, conduct theoretical analysis of the model properties, and subsequently solve the corresponding SDEs.</p></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"456 ","pages":"Article 116219"},"PeriodicalIF":2.6000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational and Applied Mathematics","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0377042724004680","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/22 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0

Abstract

In this article, we construct a numerical method for a stochastic version of the Susceptible–Infected–Susceptible (SIS) epidemic model, expressed by a suitable stochastic differential equation (SDE), by using the semi-discrete method to a suitable transformed process. We prove the strong convergence of the proposed method, with order 1, and examine its stability properties. Since SDEs generally lack analytical solutions, numerical techniques are commonly employed. Hence, the research will seek numerical solutions for existing stochastic models by constructing suitable numerical schemes and comparing them with other schemes. The objective is to achieve a qualitative and efficient approach to solving the equations. Additionally, for models that have not yet been proposed for stochastic modeling using SDEs, the research will formulate them appropriately, conduct theoretical analysis of the model properties, and subsequently solve the corresponding SDEs.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
随机 SIS 流行病模型的保域和强收敛显式方案
在本文中,我们通过对合适的变换过程使用半离散方法,为由合适的随机微分方程(SDE)表示的随机版易感-感染-易感(SIS)流行病模型构建了一种数值方法。我们证明了所提方法的强收敛性(阶次为 1),并检验了其稳定性。由于 SDE 通常缺乏解析解,因此通常采用数值技术。因此,本研究将通过构建合适的数值方案并与其他方案进行比较,为现有的随机模型寻求数值解。目的是实现定性和高效的方程求解方法。此外,对于尚未提出使用 SDE 进行随机建模的模型,研究将对其进行适当表述,对模型特性进行理论分析,然后求解相应的 SDE。
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 去求助
来源期刊
CiteScore
5.40
自引率
4.20%
发文量
437
审稿时长
3.0 months
期刊介绍: The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.
期刊最新文献
New relaxation modulus-based iterative method for large and sparse implicit complementarity problem Enhancing efficiency of proximal gradient method with predicted and corrected step sizes Optimal alignment of Lorentz orientation and generalization to matrix Lie groups A novel twin extreme learning machine for regression problems The alternating Halpern-Mann iteration for families of maps
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1