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Epidemic progression on networks based on disease generation time

We investigate the time evolution of disease spread on a network and present an analytical framework using the concept of disease generation time. Assuming a susceptible–infected–recovered epidemic process, this network-based framework enables us to calculate in detail the number of links (edges) wi...

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Detalles Bibliográficos
Autores principales: Davoudi, Bahman, Moser, Flavia, Brauer, Fred, Pourbohloul, Babak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3746462/
https://www.ncbi.nlm.nih.gov/pubmed/23889499
http://dx.doi.org/10.1080/17513758.2013.819127
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author Davoudi, Bahman
Moser, Flavia
Brauer, Fred
Pourbohloul, Babak
author_facet Davoudi, Bahman
Moser, Flavia
Brauer, Fred
Pourbohloul, Babak
author_sort Davoudi, Bahman
collection PubMed
description We investigate the time evolution of disease spread on a network and present an analytical framework using the concept of disease generation time. Assuming a susceptible–infected–recovered epidemic process, this network-based framework enables us to calculate in detail the number of links (edges) within the network that are capable of producing new infectious nodes (individuals), the number of links that are not transmitting the infection further (non-transmitting links), as well as the number of contacts that individuals have with their neighbours (also known as degree distribution) within each epidemiological class, for each generation period. Using several examples, we demonstrate very good agreement between our analytical calculations and the results of computer simulations.
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spelling pubmed-37464622013-08-22 Epidemic progression on networks based on disease generation time Davoudi, Bahman Moser, Flavia Brauer, Fred Pourbohloul, Babak J Biol Dyn Research Article We investigate the time evolution of disease spread on a network and present an analytical framework using the concept of disease generation time. Assuming a susceptible–infected–recovered epidemic process, this network-based framework enables us to calculate in detail the number of links (edges) within the network that are capable of producing new infectious nodes (individuals), the number of links that are not transmitting the infection further (non-transmitting links), as well as the number of contacts that individuals have with their neighbours (also known as degree distribution) within each epidemiological class, for each generation period. Using several examples, we demonstrate very good agreement between our analytical calculations and the results of computer simulations. Taylor & Francis 2013-07-29 2013-12 /pmc/articles/PMC3746462/ /pubmed/23889499 http://dx.doi.org/10.1080/17513758.2013.819127 Text en © 2013 The Author(s). Published by Taylor & Francis. http://www.informaworld.com/mpp/uploads/iopenaccess_tcs.pdf This is an open access article distributed under the Supplemental Terms and Conditions for iOpenAccess articles published in Taylor & Francis journals (http://www.informaworld.com/mpp/uploads/iopenaccess_tcs.pdf) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Davoudi, Bahman
Moser, Flavia
Brauer, Fred
Pourbohloul, Babak
Epidemic progression on networks based on disease generation time
title Epidemic progression on networks based on disease generation time
title_full Epidemic progression on networks based on disease generation time
title_fullStr Epidemic progression on networks based on disease generation time
title_full_unstemmed Epidemic progression on networks based on disease generation time
title_short Epidemic progression on networks based on disease generation time
title_sort epidemic progression on networks based on disease generation time
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3746462/
https://www.ncbi.nlm.nih.gov/pubmed/23889499
http://dx.doi.org/10.1080/17513758.2013.819127
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