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  Coevolution of epidemic spreading and opinion dynamics in a two-layer network with media influence

Zhai, S., Li, H., Sun, F., Kurths, J. (2026): Coevolution of epidemic spreading and opinion dynamics in a two-layer network with media influence. - Physica A: Statistical Mechanics and its Applications, 696, 131676.
https://doi.org/10.1016/j.physa.2026.131676

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 Creators:
Zhai, Shidong1, Author
Li, Haolin1, 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: This paper investigates the coupled interplay among public opinion, mass media, and epidemic spreading through a co-evolutionary multilayer network framework. We develop a Susceptible-Alert-Infected-Susceptible (SAIS) model on the physical layer, coupled with a dynamic opinion layer that captures groups’ perceived severity of the epidemic. A key feature of the model is a parameter-level coupling mechanism, whereby opinions—shaped by mass media and social interactions-directly modulate infection and recovery rates. The opinion dynamics evolve on a directed signed graph, incorporating both cooperative and antagonistic inter-group interactions as well as media influence. We rigorously establish the well-posedness of the system and derive opinion-dependent reproduction numbers to characterize epidemic thresholds. Analytical and numerical results reveal that the interaction between media-driven alertness and social influence generates rich dynamical behaviors, leading to multiple stable equilibria. By examining different regimes of the reproduction numbers, we identify diverse epidemic-opinion scenarios and discuss their potential strategic implications.

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Language(s): eng - English
 Dates: 2026-05-242026-08-15
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.physa.2026.131676
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Health
 Degree: -

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Title: Physica A: Statistical Mechanics and its Applications
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 696 Sequence Number: 131676 Start / End Page: - Identifier: Publisher: Elsevier
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1402122