Networked SIRS Epidemic Model With Opinion Evolutions: Stubborn Community and Maximum Infection Time

This paper is concerned with the co-evolution problem of epidemic and opinion over social networks. A networked SIRS epidemic model with opinion dynamics is proposed to analyze the impact of the community’s opinion on the epidemic spreading. By introducing stubborn communities, we give a...

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Main Authors: Li Ma, Junzhe Tang, Qingsong Liu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10471520/
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author Li Ma
Junzhe Tang
Qingsong Liu
author_facet Li Ma
Junzhe Tang
Qingsong Liu
author_sort Li Ma
collection DOAJ
description This paper is concerned with the co-evolution problem of epidemic and opinion over social networks. A networked SIRS epidemic model with opinion dynamics is proposed to analyze the impact of the community’s opinion on the epidemic spreading. By introducing stubborn communities, we give a sufficient condition to guarantee the epidemics converging the healthy state. Furthermore, the explicit relationship between the maximum infection time and the opinion based reproduction number is presented. Based on the Italy interactive network and the dataset collects tweets and accounts about vaccines on Twitter from March 1 to August 31, 2021, our proposed discrete-time epidemic-opinion model is employed to analyze the influence of vaccination on the epidemic spreading. It is shown that the vaccination can delay the peak of infections and reduce the overall infection rate in these communities.
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spelling doaj.art-e16e16f0171549cda1e0ae4ab43cb9c22024-03-28T23:00:32ZengIEEEIEEE Access2169-35362024-01-0112437894379510.1109/ACCESS.2024.337670510471520Networked SIRS Epidemic Model With Opinion Evolutions: Stubborn Community and Maximum Infection TimeLi Ma0Junzhe Tang1https://orcid.org/0009-0004-4748-9429Qingsong Liu2https://orcid.org/0000-0002-0862-9116School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, ChinaDepartment of Communication Engineering, Hunan Normal University, Changsha, ChinaSchool of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, ChinaThis paper is concerned with the co-evolution problem of epidemic and opinion over social networks. A networked SIRS epidemic model with opinion dynamics is proposed to analyze the impact of the community’s opinion on the epidemic spreading. By introducing stubborn communities, we give a sufficient condition to guarantee the epidemics converging the healthy state. Furthermore, the explicit relationship between the maximum infection time and the opinion based reproduction number is presented. Based on the Italy interactive network and the dataset collects tweets and accounts about vaccines on Twitter from March 1 to August 31, 2021, our proposed discrete-time epidemic-opinion model is employed to analyze the influence of vaccination on the epidemic spreading. It is shown that the vaccination can delay the peak of infections and reduce the overall infection rate in these communities.https://ieeexplore.ieee.org/document/10471520/Opinion dynamicsepidemic spreadingstubborn communitymaximum infection timesocial networks
spellingShingle Li Ma
Junzhe Tang
Qingsong Liu
Networked SIRS Epidemic Model With Opinion Evolutions: Stubborn Community and Maximum Infection Time
IEEE Access
Opinion dynamics
epidemic spreading
stubborn community
maximum infection time
social networks
title Networked SIRS Epidemic Model With Opinion Evolutions: Stubborn Community and Maximum Infection Time
title_full Networked SIRS Epidemic Model With Opinion Evolutions: Stubborn Community and Maximum Infection Time
title_fullStr Networked SIRS Epidemic Model With Opinion Evolutions: Stubborn Community and Maximum Infection Time
title_full_unstemmed Networked SIRS Epidemic Model With Opinion Evolutions: Stubborn Community and Maximum Infection Time
title_short Networked SIRS Epidemic Model With Opinion Evolutions: Stubborn Community and Maximum Infection Time
title_sort networked sirs epidemic model with opinion evolutions stubborn community and maximum infection time
topic Opinion dynamics
epidemic spreading
stubborn community
maximum infection time
social networks
url https://ieeexplore.ieee.org/document/10471520/
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AT junzhetang networkedsirsepidemicmodelwithopinionevolutionsstubborncommunityandmaximuminfectiontime
AT qingsongliu networkedsirsepidemicmodelwithopinionevolutionsstubborncommunityandmaximuminfectiontime