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Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading

The interaction between epidemic spreading and information diffusion is an interdisciplinary research problem. During an epidemic, people tend to take self-protective measures to reduce the infection risk. However, with the diffusion of rumor, people may be difficult to make an appropriate choice. H...

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Autores principales: Huang, He, Chen, Yahong, Ma, Yefeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382352/
https://www.ncbi.nlm.nih.gov/pubmed/32834190
http://dx.doi.org/10.1016/j.amc.2020.125536
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author Huang, He
Chen, Yahong
Ma, Yefeng
author_facet Huang, He
Chen, Yahong
Ma, Yefeng
author_sort Huang, He
collection PubMed
description The interaction between epidemic spreading and information diffusion is an interdisciplinary research problem. During an epidemic, people tend to take self-protective measures to reduce the infection risk. However, with the diffusion of rumor, people may be difficult to make an appropriate choice. How to reduce the negative impact of rumor and to control epidemic has become a critical issue in the social network. Elaborate mathematical model is instructive to understand such complex dynamics. In this paper, we develop a two-layer network to model the interaction between the spread of epidemic and the competitive diffusions of information. The results show that knowledge diffusion can eradicate both rumor and epidemic, where the penetration intensity of knowledge into rumor plays a vital role. Specifically, the penetration intensity of knowledge significantly increases the thresholds for rumor and epidemic to break out, even when the self-protective measure is not perfectly effective. But eradicating rumor shouldn’t be equated with eradicating epidemic. The epidemic can be eradicated with rumor still diffusing, and the epidemic may keep spreading with rumor being eradicated. Moreover, the communication-layer network structure greatly affects the spread of epidemic in the contact-layer network. When people have more connections in the communication-layer network, the knowledge is more likely to diffuse widely, and the rumor and epidemic can be eradicated more efficiently. When the communication-layer network is sparse, a larger penetration intensity of knowledge into rumor is required to promote the diffusion of knowledge.
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spelling pubmed-73823522020-07-28 Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading Huang, He Chen, Yahong Ma, Yefeng Appl Math Comput Article The interaction between epidemic spreading and information diffusion is an interdisciplinary research problem. During an epidemic, people tend to take self-protective measures to reduce the infection risk. However, with the diffusion of rumor, people may be difficult to make an appropriate choice. How to reduce the negative impact of rumor and to control epidemic has become a critical issue in the social network. Elaborate mathematical model is instructive to understand such complex dynamics. In this paper, we develop a two-layer network to model the interaction between the spread of epidemic and the competitive diffusions of information. The results show that knowledge diffusion can eradicate both rumor and epidemic, where the penetration intensity of knowledge into rumor plays a vital role. Specifically, the penetration intensity of knowledge significantly increases the thresholds for rumor and epidemic to break out, even when the self-protective measure is not perfectly effective. But eradicating rumor shouldn’t be equated with eradicating epidemic. The epidemic can be eradicated with rumor still diffusing, and the epidemic may keep spreading with rumor being eradicated. Moreover, the communication-layer network structure greatly affects the spread of epidemic in the contact-layer network. When people have more connections in the communication-layer network, the knowledge is more likely to diffuse widely, and the rumor and epidemic can be eradicated more efficiently. When the communication-layer network is sparse, a larger penetration intensity of knowledge into rumor is required to promote the diffusion of knowledge. Elsevier Inc. 2021-01-01 2020-07-25 /pmc/articles/PMC7382352/ /pubmed/32834190 http://dx.doi.org/10.1016/j.amc.2020.125536 Text en © 2020 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Huang, He
Chen, Yahong
Ma, Yefeng
Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading
title Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading
title_full Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading
title_fullStr Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading
title_full_unstemmed Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading
title_short Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading
title_sort modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382352/
https://www.ncbi.nlm.nih.gov/pubmed/32834190
http://dx.doi.org/10.1016/j.amc.2020.125536
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