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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2013
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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. |
format | Online Article Text |
id | pubmed-3746462 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
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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