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

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 prope...

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Detalles Bibliográficos
Autores principales: Dottori, Martín, Fabricius, Gabriel
Formato: Articulo Preprint
Lenguaje:Inglés
Publicado: 2015
Materias:
Ciencias Exactas
Física
SIR
Network
Stochastic
Pertussis
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/128836
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-128836
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Exactas
Física
SIR
Network
Stochastic
Pertussis
spellingShingle Ciencias Exactas
Física
SIR
Network
Stochastic
Pertussis
Dottori, Martín
Fabricius, Gabriel
SIR model on a dynamical network and the endemic state of an infectious disease
topic_facet Ciencias Exactas
Física
SIR
Network
Stochastic
Pertussis
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.
format Articulo
Preprint
author Dottori, Martín
Fabricius, Gabriel
author_facet Dottori, Martín
Fabricius, Gabriel
author_sort Dottori, Martín
title SIR model on a dynamical network and the endemic state of an infectious disease
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
publishDate 2015
url http://sedici.unlp.edu.ar/handle/10915/128836
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bdutipo_str Repositorios
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