SIR model on a dynamical network and the endemic state of an infectious disease

Autores
Dottori, Martin; Fabricius, Gabriel
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work we performed a numerical study of an epidemic model that mimics the endemic state of whooping cough in the pre-vaccine era. We considered a stochastic SIR model on dynamical networks that involve local and global contacts among individuals and analysed the influence of the network properties on the characterization of the quasi-stationary state. We computed probability density functions (PDF) for infected fraction of individuals and found that they are well fitted by gamma functions, excepted the tails of the distributions that are q-exponentials. We also computed the fluctuation power spectra of infective time series for different networks. We found that network effects can be partially absorbed by rescaling the rate of infective contacts of the model. An explicit relation between the effective transmission rate of the disease and the correlation of susceptible individuals with their infective nearest neighbours was obtained. This relation quantifies >the known screening of infective individuals observed in these networks. We finally discuss the goodness and limitations of the SIR model with homogeneous mixing and parameters taken from epidemiological data to describe the dynamic behaviour observed in the networks studied.
Fil: Dottori, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Fil: Fabricius, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de la Plata; Argentina
Materia
SIR
NETWORK
STOCHASTIC
PERTUSSIS
Nivel de accesibilidad
acceso embargado
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/5045
Acceder
id CONICETDig_9fd851fbe0a4752231750a2e91cd8d48
oai_identifier_str oai:ri.conicet.gov.ar:11336/5045
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling SIR model on a dynamical network and the endemic state of an infectious diseaseDottori, MartinFabricius, GabrielSIRNETWORKSTOCHASTICPERTUSSIShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1In this work we performed a numerical study of an epidemic model that mimics the endemic state of whooping cough in the pre-vaccine era. We considered a stochastic SIR model on dynamical networks that involve local and global contacts among individuals and analysed the influence of the network properties on the characterization of the quasi-stationary state. We computed probability density functions (PDF) for infected fraction of individuals and found that they are well fitted by gamma functions, excepted the tails of the distributions that are q-exponentials. We also computed the fluctuation power spectra of infective time series for different networks. We found that network effects can be partially absorbed by rescaling the rate of infective contacts of the model. An explicit relation between the effective transmission rate of the disease and the correlation of susceptible individuals with their infective nearest neighbours was obtained. This relation quantifies >the known screening of infective individuals observed in these networks. We finally discuss the goodness and limitations of the SIR model with homogeneous mixing and parameters taken from epidemiological data to describe the dynamic behaviour observed in the networks studied.Fil: Dottori, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Fabricius, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de la Plata; ArgentinaElsevier2015-04-18info:eu-repo/date/embargoEnd/2017-05-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/5045Dottori, Martin; Fabricius, Gabriel; SIR model on a dynamical network and the endemic state of an infectious disease; Elsevier; Physica A: Statistical Mechanics and its Applications; 434; 18-4-2015; 25-350378-4371enginfo:eu-repo/semantics/altIdentifier/arxiv/1410.1383info:eu-repo/semantics/altIdentifier/url/http://arxiv.org/abs/1410.1383info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0378437115003660info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2015.04.007info:eu-repo/semantics/embargoedAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:54:09Zoai:ri.conicet.gov.ar:11336/5045instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:54:09.948CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv SIR model on a dynamical network and the endemic state of an infectious disease
title SIR model on a dynamical network and the endemic state of an infectious disease
spellingShingle SIR model on a dynamical network and the endemic state of an infectious disease
Dottori, Martin
SIR
NETWORK
STOCHASTIC
PERTUSSIS
title_short SIR model on a dynamical network and the endemic state of an infectious disease
title_full SIR model on a dynamical network and the endemic state of an infectious disease
title_fullStr SIR model on a dynamical network and the endemic state of an infectious disease
title_full_unstemmed SIR model on a dynamical network and the endemic state of an infectious disease
title_sort SIR model on a dynamical network and the endemic state of an infectious disease
dc.creator.none.fl_str_mv Dottori, Martin
Fabricius, Gabriel
author Dottori, Martin
author_facet Dottori, Martin
Fabricius, Gabriel
author_role author
author2 Fabricius, Gabriel
author2_role author
dc.subject.none.fl_str_mv SIR
NETWORK
STOCHASTIC
PERTUSSIS
topic SIR
NETWORK
STOCHASTIC
PERTUSSIS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this work we performed a numerical study of an epidemic model that mimics the endemic state of whooping cough in the pre-vaccine era. We considered a stochastic SIR model on dynamical networks that involve local and global contacts among individuals and analysed the influence of the network properties on the characterization of the quasi-stationary state. We computed probability density functions (PDF) for infected fraction of individuals and found that they are well fitted by gamma functions, excepted the tails of the distributions that are q-exponentials. We also computed the fluctuation power spectra of infective time series for different networks. We found that network effects can be partially absorbed by rescaling the rate of infective contacts of the model. An explicit relation between the effective transmission rate of the disease and the correlation of susceptible individuals with their infective nearest neighbours was obtained. This relation quantifies >the known screening of infective individuals observed in these networks. We finally discuss the goodness and limitations of the SIR model with homogeneous mixing and parameters taken from epidemiological data to describe the dynamic behaviour observed in the networks studied.
Fil: Dottori, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Fil: Fabricius, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de la Plata; Argentina
description In this work we performed a numerical study of an epidemic model that mimics the endemic state of whooping cough in the pre-vaccine era. We considered a stochastic SIR model on dynamical networks that involve local and global contacts among individuals and analysed the influence of the network properties on the characterization of the quasi-stationary state. We computed probability density functions (PDF) for infected fraction of individuals and found that they are well fitted by gamma functions, excepted the tails of the distributions that are q-exponentials. We also computed the fluctuation power spectra of infective time series for different networks. We found that network effects can be partially absorbed by rescaling the rate of infective contacts of the model. An explicit relation between the effective transmission rate of the disease and the correlation of susceptible individuals with their infective nearest neighbours was obtained. This relation quantifies >the known screening of infective individuals observed in these networks. We finally discuss the goodness and limitations of the SIR model with homogeneous mixing and parameters taken from epidemiological data to describe the dynamic behaviour observed in the networks studied.
publishDate 2015
dc.date.none.fl_str_mv 2015-04-18
info:eu-repo/date/embargoEnd/2017-05-15
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/5045
Dottori, Martin; Fabricius, Gabriel; SIR model on a dynamical network and the endemic state of an infectious disease; Elsevier; Physica A: Statistical Mechanics and its Applications; 434; 18-4-2015; 25-35
0378-4371
url http://hdl.handle.net/11336/5045
identifier_str_mv Dottori, Martin; Fabricius, Gabriel; SIR model on a dynamical network and the endemic state of an infectious disease; Elsevier; Physica A: Statistical Mechanics and its Applications; 434; 18-4-2015; 25-35
0378-4371
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/arxiv/1410.1383
info:eu-repo/semantics/altIdentifier/url/http://arxiv.org/abs/1410.1383
info:eu-repo/semantics/altIdentifier/doi/
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0378437115003660
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2015.04.007
dc.rights.none.fl_str_mv info:eu-repo/semantics/embargoedAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv embargoedAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269268168671232
score 13.13397

Publicaciones similares