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Estimating the epidemic threshold on networks by deterministic connections
For many epidemic networks some connections between nodes are treated as deterministic, while the remainder are random and have different connection probabilities. By applying spectral analysis to several constructed models, we find that one can estimate the epidemic thresholds of these networks by...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AIP Publishing LLC
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112486/ https://www.ncbi.nlm.nih.gov/pubmed/25554044 http://dx.doi.org/10.1063/1.4901334 |
_version_ | 1783513485000310784 |
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author | Li, Kezan Fu, Xinchu Small, Michael Zhu, Guanghu |
author_facet | Li, Kezan Fu, Xinchu Small, Michael Zhu, Guanghu |
author_sort | Li, Kezan |
collection | PubMed |
description | For many epidemic networks some connections between nodes are treated as deterministic, while the remainder are random and have different connection probabilities. By applying spectral analysis to several constructed models, we find that one can estimate the epidemic thresholds of these networks by investigating information from only the deterministic connections. Nonetheless, in these models, generic nonuniform stochastic connections and heterogeneous community structure are also considered. The estimation of epidemic thresholds is achieved via inequalities with upper and lower bounds, which are found to be in very good agreement with numerical simulations. Since these deterministic connections are easier to detect than those stochastic connections, this work provides a feasible and effective method to estimate the epidemic thresholds in real epidemic networks. |
format | Online Article Text |
id | pubmed-7112486 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | AIP Publishing LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-71124862020-04-02 Estimating the epidemic threshold on networks by deterministic connections Li, Kezan Fu, Xinchu Small, Michael Zhu, Guanghu Chaos Regular Articles For many epidemic networks some connections between nodes are treated as deterministic, while the remainder are random and have different connection probabilities. By applying spectral analysis to several constructed models, we find that one can estimate the epidemic thresholds of these networks by investigating information from only the deterministic connections. Nonetheless, in these models, generic nonuniform stochastic connections and heterogeneous community structure are also considered. The estimation of epidemic thresholds is achieved via inequalities with upper and lower bounds, which are found to be in very good agreement with numerical simulations. Since these deterministic connections are easier to detect than those stochastic connections, this work provides a feasible and effective method to estimate the epidemic thresholds in real epidemic networks. AIP Publishing LLC 2014-12 2014-11-12 /pmc/articles/PMC7112486/ /pubmed/25554044 http://dx.doi.org/10.1063/1.4901334 Text en © 2014 AIP Publishing LLC 1054-1500/2014/24(4)/043124/9/$30.00 All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Regular Articles Li, Kezan Fu, Xinchu Small, Michael Zhu, Guanghu Estimating the epidemic threshold on networks by deterministic connections |
title | Estimating the epidemic threshold on networks by deterministic
connections |
title_full | Estimating the epidemic threshold on networks by deterministic
connections |
title_fullStr | Estimating the epidemic threshold on networks by deterministic
connections |
title_full_unstemmed | Estimating the epidemic threshold on networks by deterministic
connections |
title_short | Estimating the epidemic threshold on networks by deterministic
connections |
title_sort | estimating the epidemic threshold on networks by deterministic
connections |
topic | Regular Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112486/ https://www.ncbi.nlm.nih.gov/pubmed/25554044 http://dx.doi.org/10.1063/1.4901334 |
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