The diffusive epidemic process on Barabasi–Albert networks.

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorAlves, Tayroni Francisco de Alencar-
Autor(es): dc.creatorAlves, Gladstone de Alencar-
Autor(es): dc.creatorMacedo Filho, Antonio de-
Autor(es): dc.creatorFerreira, Ronan Silva-
Autor(es): dc.creatorLima, Francisco Welington de Sousa-
Data de aceite: dc.date.accessioned2025-08-21T15:09:50Z-
Data de disponibilização: dc.date.available2025-08-21T15:09:50Z-
Data de envio: dc.date.issued2022-02-25-
Data de envio: dc.date.issued2022-02-25-
Data de envio: dc.date.issued2020-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/jspui/handle/123456789/14576-
Fonte completa do material: dc.identifierhttps://iopscience.iop.org/article/10.1088/1742-5468/abefe4-
Fonte completa do material: dc.identifierhttps://doi.org/10.1088/1742-5468/abefe4-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1004020-
Descrição: dc.descriptionWe present a modified diffusive epidemic process (DEP) that has a finite threshold on scale-free graphs, motivated by the COVID-19 pandemic. The DEP describes the epidemic spreading of a disease in a non-sedentary population, which can describe the spreading of a real disease. Our main modification is to use the Gillespie algorithm with a reaction time tmax, exponentially distributed with mean inversely proportional to the node population in order to model the individuals’ interactions. Our simulation results of the modified model on Barabasi–Albert networks are compatible with a continuous absorbing-active phase transition when increasing the average concentration. The transition obeys the mean-field critical exponents β = 1, γ = 0 and ν⊥ = 1/2. In addition, the system presents logarithmic corrections with pseudo-exponents β = γ = −3/2 on the order parameter and its fluctuations, respectively. The most evident implication of our simulation results is if the individuals avoid social interactions in order to not spread a disease, this leads the system to have a finite threshold in scale-free graphs.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsrestrito-
Palavras-chave: dc.subjectAbsorbing states-
Palavras-chave: dc.subjectAgent-based models-
Palavras-chave: dc.subjectCritical exponents and amplitudes-
Palavras-chave: dc.subjectRandom graphs-
Palavras-chave: dc.subjectNetworks-
Título: dc.titleThe diffusive epidemic process on Barabasi–Albert networks.-
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