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  Impact of Opinion-Driven Adaptive Vigilance on Virus Spread and Opinion Evolution

Zhai, S., Yin, S., Sun, F., Kurths, J. (2025): Impact of Opinion-Driven Adaptive Vigilance on Virus Spread and Opinion Evolution. - IEEE Transactions on Systems, Man, and Cybernetics: Systems, 55, 8, 5596-5606.
https://doi.org/10.1109/TSMC.2025.3573298

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 Creators:
Zhai, Shidong1, Author
Yin, Shijie1, Author
Sun, Fenglan1, Author
Kurths, Jürgen2, Author           
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: In order to investigate how different levels of vigilance affect the spread of a virus and changes in public opinion, this article introduces a network-based susceptible-exposed-infectious-vigilant (SEIV)-Opinion model. The model incorporates vigilance influence functions that depend on infection and recovery rates, which are associated with opinion states. A basic reproduction number dependent on both the viral transmission state and public opinion dynamics is constructed to analyze the conditions for virus eradication or pandemic persistence. These findings indicate that during severe epidemics, people are very concerned about the epidemic, leading to an increased vigilance, thereby significantly slowing the spread of the virus. On the other hand, during milder epidemics, people do not respond adequately to the threat of the epidemic, and thus are less vigilant and have less impact on the spread of the virus. These insights correlate closely with real-world trends. This article uses numerical simulations to demonstrate and confirm these patterns under various conditions.

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Language(s): eng - English
 Dates: 2025-06-102025-08-01
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/TSMC.2025.3573298
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Health
Model / method: Machine Learning
 Degree: -

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Title: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Source Genre: Journal, SCI, Scopus
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Publ. Info: -
Pages: - Volume / Issue: 55 (8) Sequence Number: - Start / End Page: 5596 - 5606 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-systems-man-cybernetics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)