Coupled Information-Infection Dynamics Produce Information-Driven Shifts in Epidemic Regimes via Competing Vaccine Narratives.

IF 2.3 4区 数学 Q2 BIOLOGY
Asma Azizi, Caner Kazanci, Xiaohui Guo
{"title":"Coupled Information-Infection Dynamics Produce Information-Driven Shifts in Epidemic Regimes via Competing Vaccine Narratives.","authors":"Asma Azizi, Caner Kazanci, Xiaohui Guo","doi":"10.1007/s11538-026-01719-y","DOIUrl":null,"url":null,"abstract":"<p><p>Vaccine-related communication can be harnessed to curb infection; yet, in practice, it often undermines vaccine uptake and sustains transmission even when effective vaccines are available. Our goal is to understand how vaccine information dynamics may shape infection spread within a coupled information-infection modeling framework. We present a coupled information-infection modeling framework that links a standard SVIRS infection-spread model with an integrate-and-fire-inspired information-dissemination model. Vaccine-positive and vaccine-critical active groups disseminate competing information that shapes non-active/hesitant individuals' vaccine attitudes through direct peer influence, threshold-based acceptance, and persistence of engagement, while infection prevalence can feed back by amplifying caution. The evolving vaccine attitudes modulate vaccination uptake, and the model tracks the joint evolution of information dynamics and epidemic trajectories. Our analysis shows that, under the assumed coupling, information dynamics can shift the system among qualitatively distinct infection outcomes. In particular, reducing resistance to vaccine-positive information and sustaining vaccine-positive engagement can move the system toward lower-endemic regimes more reliably than changes focused only on weakening vaccine-critical engagement, for the parameter ranges considered here. These findings highlight the potential importance of information dynamics in epidemic modeling and suggest that sustained vaccine-positive engagement can be an important qualitative mechanism for reducing long-term infection burden.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 9","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2026-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of Mathematical Biology","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s11538-026-01719-y","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Vaccine-related communication can be harnessed to curb infection; yet, in practice, it often undermines vaccine uptake and sustains transmission even when effective vaccines are available. Our goal is to understand how vaccine information dynamics may shape infection spread within a coupled information-infection modeling framework. We present a coupled information-infection modeling framework that links a standard SVIRS infection-spread model with an integrate-and-fire-inspired information-dissemination model. Vaccine-positive and vaccine-critical active groups disseminate competing information that shapes non-active/hesitant individuals' vaccine attitudes through direct peer influence, threshold-based acceptance, and persistence of engagement, while infection prevalence can feed back by amplifying caution. The evolving vaccine attitudes modulate vaccination uptake, and the model tracks the joint evolution of information dynamics and epidemic trajectories. Our analysis shows that, under the assumed coupling, information dynamics can shift the system among qualitatively distinct infection outcomes. In particular, reducing resistance to vaccine-positive information and sustaining vaccine-positive engagement can move the system toward lower-endemic regimes more reliably than changes focused only on weakening vaccine-critical engagement, for the parameter ranges considered here. These findings highlight the potential importance of information dynamics in epidemic modeling and suggest that sustained vaccine-positive engagement can be an important qualitative mechanism for reducing long-term infection burden.

耦合信息-感染动力学通过相互竞争的疫苗叙述在流行病制度中产生信息驱动的转变。
可以利用与疫苗有关的传播来遏制感染;然而,在实践中,即使有有效的疫苗,它往往也会破坏疫苗的吸收并维持传播。我们的目标是了解疫苗信息动态如何在耦合信息-感染建模框架内塑造感染传播。我们提出了一个耦合的信息-感染建模框架,该框架将标准的SVIRS感染-传播模型与集成和启发的信息传播模型联系起来。疫苗阳性和疫苗关键活跃群体传播相互竞争的信息,这些信息通过直接的同伴影响、基于阈值的接受和持续参与来塑造非活跃/犹豫不决的个体对疫苗的态度,而感染流行可以通过放大谨慎来反馈。不断变化的疫苗态度调节了疫苗接种的摄取,该模型跟踪了信息动态和流行病轨迹的联合演变。我们的分析表明,在假设的耦合下,信息动力学可以在质量不同的感染结果之间改变系统。特别是,就本文所考虑的参数范围而言,减少对疫苗阳性信息的耐药性和维持疫苗阳性接触,可以比仅侧重于削弱疫苗关键接触的变化更可靠地将系统推向低流行制度。这些发现突出了信息动力学在流行病建模中的潜在重要性,并表明持续的疫苗阳性参与可能是减少长期感染负担的重要定性机制。
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
CiteScore
3.90
自引率
8.60%
发文量
123
审稿时长
7.5 months
期刊介绍: The Bulletin of Mathematical Biology, the official journal of the Society for Mathematical Biology, disseminates original research findings and other information relevant to the interface of biology and the mathematical sciences. Contributions should have relevance to both fields. In order to accommodate the broad scope of new developments, the journal accepts a variety of contributions, including: Original research articles focused on new biological insights gained with the help of tools from the mathematical sciences or new mathematical tools and methods with demonstrated applicability to biological investigations Research in mathematical biology education Reviews Commentaries Perspectives, and contributions that discuss issues important to the profession All contributions are peer-reviewed.
×
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