An unconditional boundary and dynamics preserving scheme for the stochastic epidemic model

IF 1.4 2区 数学 Q1 MATHEMATICS
Calcolo Pub Date : 2024-08-31 DOI:10.1007/s10092-024-00606-z
Ruishu Liu, Xiaojie Wang, Lei Dai
{"title":"An unconditional boundary and dynamics preserving scheme for the stochastic epidemic model","authors":"Ruishu Liu, Xiaojie Wang, Lei Dai","doi":"10.1007/s10092-024-00606-z","DOIUrl":null,"url":null,"abstract":"<p>In the present article, we construct a logarithm transformation based Milstein-type method for the stochastic susceptible-infected-susceptible (SIS) epidemic model evolving in the domain (0, <i>N</i>). The new scheme is explicit and unconditionally boundary and dynamics preserving, when used to solve the stochastic SIS epidemic model. Also, it is proved that the scheme has a strong convergence rate of order one. Different from existing time discretization schemes, the newly proposed scheme for any time step size <span>\\(h&gt;0\\)</span>, not only produces numerical approximations living in the entire domain (0, <i>N</i>), but also unconditionally reproduces the extinction and persistence behavior of the original model, with no additional requirements imposed on the model parameters. Numerical experiments are presented to verify our theoretical findings.</p>","PeriodicalId":9522,"journal":{"name":"Calcolo","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Calcolo","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s10092-024-00606-z","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0

Abstract

In the present article, we construct a logarithm transformation based Milstein-type method for the stochastic susceptible-infected-susceptible (SIS) epidemic model evolving in the domain (0, N). The new scheme is explicit and unconditionally boundary and dynamics preserving, when used to solve the stochastic SIS epidemic model. Also, it is proved that the scheme has a strong convergence rate of order one. Different from existing time discretization schemes, the newly proposed scheme for any time step size \(h>0\), not only produces numerical approximations living in the entire domain (0, N), but also unconditionally reproduces the extinction and persistence behavior of the original model, with no additional requirements imposed on the model parameters. Numerical experiments are presented to verify our theoretical findings.

Abstract Image

随机流行病模型的无条件边界和动态保护方案
在本文中,我们构建了一种基于对数变换的米尔斯坦型方法,用于求解在域(0,N)中演化的随机易感-感染-易感(SIS)流行病模型。新方法用于求解随机 SIS 流行病模型时是显式的、无条件地保持边界和动态的。此外,还证明了该方案具有一阶的强收敛率。与现有的时间离散化方案不同,新提出的方案对于任意时间步长(h>0\),不仅产生了活在整个域(0,N)的数值近似,而且无条件地再现了原始模型的消亡和持续行为,对模型参数没有额外要求。我们将通过数值实验来验证我们的理论发现。
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
Calcolo
Calcolo 数学-数学
CiteScore
2.40
自引率
11.80%
发文量
36
审稿时长
>12 weeks
期刊介绍: Calcolo is a quarterly of the Italian National Research Council, under the direction of the Institute for Informatics and Telematics in Pisa. Calcolo publishes original contributions in English on Numerical Analysis and its Applications, and on the Theory of Computation. The main focus of the journal is on Numerical Linear Algebra, Approximation Theory and its Applications, Numerical Solution of Differential and Integral Equations, Computational Complexity, Algorithmics, Mathematical Aspects of Computer Science, Optimization Theory. Expository papers will also appear from time to time as an introduction to emerging topics in one of the above mentioned fields. There will be a "Report" section, with abstracts of PhD Theses, news and reports from conferences and book reviews. All submissions will be carefully refereed.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信