Stochastic SIR epidemic model dynamics on scale-free networks

This study introduces a stochastic SIR (Susceptible–Infectious–Recovered) model on complex networks, utilizing a scale-free network to represent inter-human contacts. The model incorporates a threshold parameter, denoted as , which plays a decisive role in determining whether the disease will persis...

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Detalles Bibliográficos
Autores: Settati, A., Caraballo Garrido, Tomás, Lahrouz, A., Bouzalmat, I., Assadouq, A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/164394
Acceso en línea:https://hdl.handle.net/11441/164394
https://doi.org/10.1016/j.matcom.2024.09.027
Access Level:acceso abierto
Palabra clave:Stochastic SIR model
Scale-free network
Extinction
Persistence
Stationary distribution
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spelling Stochastic SIR epidemic model dynamics on scale-free networksSettati, A.Caraballo Garrido, TomásLahrouz, A.Bouzalmat, I.Assadouq, A.Stochastic SIR modelScale-free networkExtinctionPersistenceStationary distributionThis study introduces a stochastic SIR (Susceptible–Infectious–Recovered) model on complex networks, utilizing a scale-free network to represent inter-human contacts. The model incorporates a threshold parameter, denoted as , which plays a decisive role in determining whether the disease will persist or become extinct. When , the disease exhibits exponential decay and eventually disappear. Conversely, when , the disease persists. The critical case of is also examined. Furthermore, we establish a unique stationary distribution for . Our findings highlight the significance of network topology in modeling disease spread, emphasizing the role of social networks in epidemiology. Additionally, we present computational simulations that consider the scale-free network’s topology, offering comprehensive insights into the behavior of the stochastic SIR model on complex networks. These results have substantial implications for public health policy, disease control strategies, and epidemic modeling in diverse contexts.ElsevierEcuaciones Diferenciales y Análisis NuméricoFQM314: Análisis Estocástico de Sistemas Diferenciales2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlhttps://hdl.handle.net/11441/164394https://doi.org/10.1016/j.matcom.2024.09.027reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésMathematics and Computers in Simulation, 229 (March), 246-259.https://doi.org/10.1016/j.matcom.2024.09.027info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1643942026-06-17T12:51:07Z
dc.title.none.fl_str_mv Stochastic SIR epidemic model dynamics on scale-free networks
title Stochastic SIR epidemic model dynamics on scale-free networks
spellingShingle Stochastic SIR epidemic model dynamics on scale-free networks
Settati, A.
Stochastic SIR model
Scale-free network
Extinction
Persistence
Stationary distribution
title_short Stochastic SIR epidemic model dynamics on scale-free networks
title_full Stochastic SIR epidemic model dynamics on scale-free networks
title_fullStr Stochastic SIR epidemic model dynamics on scale-free networks
title_full_unstemmed Stochastic SIR epidemic model dynamics on scale-free networks
title_sort Stochastic SIR epidemic model dynamics on scale-free networks
dc.creator.none.fl_str_mv Settati, A.
Caraballo Garrido, Tomás
Lahrouz, A.
Bouzalmat, I.
Assadouq, A.
author Settati, A.
author_facet Settati, A.
Caraballo Garrido, Tomás
Lahrouz, A.
Bouzalmat, I.
Assadouq, A.
author_role author
author2 Caraballo Garrido, Tomás
Lahrouz, A.
Bouzalmat, I.
Assadouq, A.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ecuaciones Diferenciales y Análisis Numérico
FQM314: Análisis Estocástico de Sistemas Diferenciales
dc.subject.none.fl_str_mv Stochastic SIR model
Scale-free network
Extinction
Persistence
Stationary distribution
topic Stochastic SIR model
Scale-free network
Extinction
Persistence
Stationary distribution
description This study introduces a stochastic SIR (Susceptible–Infectious–Recovered) model on complex networks, utilizing a scale-free network to represent inter-human contacts. The model incorporates a threshold parameter, denoted as , which plays a decisive role in determining whether the disease will persist or become extinct. When , the disease exhibits exponential decay and eventually disappear. Conversely, when , the disease persists. The critical case of is also examined. Furthermore, we establish a unique stationary distribution for . Our findings highlight the significance of network topology in modeling disease spread, emphasizing the role of social networks in epidemiology. Additionally, we present computational simulations that consider the scale-free network’s topology, offering comprehensive insights into the behavior of the stochastic SIR model on complex networks. These results have substantial implications for public health policy, disease control strategies, and epidemic modeling in diverse contexts.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/164394
https://doi.org/10.1016/j.matcom.2024.09.027
url https://hdl.handle.net/11441/164394
https://doi.org/10.1016/j.matcom.2024.09.027
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Mathematics and Computers in Simulation, 229 (March), 246-259.
https://doi.org/10.1016/j.matcom.2024.09.027
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
text/html
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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