Dynamics of an SEIRV endemic model: effects of reinfection.

IF 2.7 4区 数学 Q3 ECOLOGY
Journal of Biological Dynamics Pub Date : 2026-12-31 Epub Date: 2026-08-08 DOI:10.1080/17513758.2026.2713881
Aditya Khan, Lin Wang, Juxin Liu, Momiao Xiong
{"title":"Dynamics of an SEIRV endemic model: effects of reinfection.","authors":"Aditya Khan, Lin Wang, Juxin Liu, Momiao Xiong","doi":"10.1080/17513758.2026.2713881","DOIUrl":null,"url":null,"abstract":"<p><p>The threshold interpretation of <math><msub><mi>R</mi><mn>0</mn></msub></math> can fail to describe elimination in epidemic models with imperfect vaccination and reinfection. We study an SEIRV model in which vaccinated individuals remain partially susceptible and recovered individuals may be reinfected. The main novelty is an explicit threshold account of how reinfection can sustain endemic equilibria even when the vaccinated-population reproduction number satisfies <math><mrow><msub><mi>R</mi><mn>0</mn></msub><mo><</mo><mn>1</mn></mrow></math>. We derive the disease-free equilibrium, <math><msub><mi>R</mi><mn>0</mn></msub></math>, and a sufficient condition for convergence to the disease-free state, showing why local disease-free stability and global elimination are distinct. We reduce the endemic-equilibrium problem to a scalar equation and identify a reinfection threshold <math><msubsup><mi>β</mi><mn>2</mn><mo>∗</mo></msubsup></math> determining the local direction of bifurcation at <math><mrow><msub><mi>R</mi><mn>0</mn></msub><mo>=</mo><mn>1</mn></mrow></math>. When <math><mrow><msub><mi>β</mi><mn>2</mn></msub><mo>></mo><msubsup><mi>β</mi><mn>2</mn><mo>∗</mo></msubsup></mrow></math>, we prove a lower critical threshold <math><mrow><msubsup><mi>R</mi><mn>0</mn><mi>C</mi></msubsup><mo><</mo><mn>1</mn></mrow></math> separating elimination from subcritical endemic persistence. We also give an efficient-vaccine approximation and general numerical algorithm for computing <math><msubsup><mi>R</mi><mn>0</mn><mi>C</mi></msubsup></math>. Simulations using an illustrative Omicron-era parameterisation show how these thresholds organise the model dynamics.</p>","PeriodicalId":48809,"journal":{"name":"Journal of Biological Dynamics","volume":"20 1","pages":"2713881"},"PeriodicalIF":2.7000,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biological Dynamics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1080/17513758.2026.2713881","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/8/8 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The threshold interpretation of R0 can fail to describe elimination in epidemic models with imperfect vaccination and reinfection. We study an SEIRV model in which vaccinated individuals remain partially susceptible and recovered individuals may be reinfected. The main novelty is an explicit threshold account of how reinfection can sustain endemic equilibria even when the vaccinated-population reproduction number satisfies R0<1. We derive the disease-free equilibrium, R0, and a sufficient condition for convergence to the disease-free state, showing why local disease-free stability and global elimination are distinct. We reduce the endemic-equilibrium problem to a scalar equation and identify a reinfection threshold β2∗ determining the local direction of bifurcation at R0=1. When β2>β2∗, we prove a lower critical threshold R0C<1 separating elimination from subcritical endemic persistence. We also give an efficient-vaccine approximation and general numerical algorithm for computing R0C. Simulations using an illustrative Omicron-era parameterisation show how these thresholds organise the model dynamics.

SEIRV地方性模型的动力学:再感染的影响。
在疫苗接种和再感染不完善的流行病模型中,R0的阈值解释可能无法描述消除。我们研究了一个SEIRV模型,其中接种疫苗的个体仍然部分易感,恢复后的个体可能再次感染。主要的新颖之处在于一个明确的阈值解释,即即使接种疫苗的群体繁殖数满足R01,再感染如何维持地方性平衡。我们推导了无病平衡点R0和收敛到无病状态的充分条件,说明为什么局部无病稳定性和全局消除是不同的。我们将地方性平衡问题简化为标量方程,并确定了在R0=1处决定局部分支方向的再感染阈值β2 *。当β2>β2 *时,我们证明了较低的临界阈值R0C1将消除与亚临界地方性持久性分开。我们还给出了计算R0C的有效疫苗近似和一般数值算法。使用说明性奥米克隆时代参数化的模拟显示了这些阈值是如何组织模型动力学的。
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 Biological Dynamics
Journal of Biological Dynamics ECOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
4.90
自引率
3.60%
发文量
28
审稿时长
33 weeks
期刊介绍: Journal of Biological Dynamics, an open access journal, publishes state of the art papers dealing with the analysis of dynamic models that arise from biological processes. The Journal focuses on dynamic phenomena at scales ranging from the level of individual organisms to that of populations, communities, and ecosystems in the fields of ecology and evolutionary biology, population dynamics, epidemiology, immunology, neuroscience, environmental science, and animal behavior. Papers in other areas are acceptable at the editors’ discretion. In addition to papers that analyze original mathematical models and develop new theories and analytic methods, the Journal welcomes papers that connect mathematical modeling and analysis to experimental and observational data. The Journal also publishes short notes, expository and review articles, book reviews and a section on open problems.
×
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:604180095
Book学术官方微信
小红书