Cargando…
Numerical identification of epidemic thresholds for susceptible-infected-recovered model on finite-size networks
Epidemic threshold has always been a very hot topic for studying epidemic dynamics on complex networks. The previous studies have provided different theoretical predictions of the epidemic threshold for the susceptible-infected-recovered (SIR) model, but the numerical verification of these theoretic...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AIP Publishing LLC
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112466/ https://www.ncbi.nlm.nih.gov/pubmed/26117098 http://dx.doi.org/10.1063/1.4922153 |
_version_ | 1783513481704636416 |
---|---|
author | Shu, Panpan Wang, Wei Tang, Ming Do, Younghae |
author_facet | Shu, Panpan Wang, Wei Tang, Ming Do, Younghae |
author_sort | Shu, Panpan |
collection | PubMed |
description | Epidemic threshold has always been a very hot topic for studying epidemic dynamics on complex networks. The previous studies have provided different theoretical predictions of the epidemic threshold for the susceptible-infected-recovered (SIR) model, but the numerical verification of these theoretical predictions is still lacking. Considering that the large fluctuation of the outbreak size occurs near the epidemic threshold, we propose a novel numerical identification method of SIR epidemic threshold by analyzing the peak of the epidemic variability. Extensive experiments on synthetic and real-world networks demonstrate that the variability measure can successfully give the numerical threshold for the SIR model. The heterogeneous mean-field prediction agrees very well with the numerical threshold, except the case that the networks are disassortative, in which the quenched mean-field prediction is relatively close to the numerical threshold. Moreover, the numerical method presented is also suitable for the susceptible-infected-susceptible model. This work helps to verify the theoretical analysis of epidemic threshold and would promote further studies on the phase transition of epidemic dynamics. |
format | Online Article Text |
id | pubmed-7112466 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | AIP Publishing LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-71124662020-04-02 Numerical identification of epidemic thresholds for susceptible-infected-recovered model on finite-size networks Shu, Panpan Wang, Wei Tang, Ming Do, Younghae Chaos Regular Articles Epidemic threshold has always been a very hot topic for studying epidemic dynamics on complex networks. The previous studies have provided different theoretical predictions of the epidemic threshold for the susceptible-infected-recovered (SIR) model, but the numerical verification of these theoretical predictions is still lacking. Considering that the large fluctuation of the outbreak size occurs near the epidemic threshold, we propose a novel numerical identification method of SIR epidemic threshold by analyzing the peak of the epidemic variability. Extensive experiments on synthetic and real-world networks demonstrate that the variability measure can successfully give the numerical threshold for the SIR model. The heterogeneous mean-field prediction agrees very well with the numerical threshold, except the case that the networks are disassortative, in which the quenched mean-field prediction is relatively close to the numerical threshold. Moreover, the numerical method presented is also suitable for the susceptible-infected-susceptible model. This work helps to verify the theoretical analysis of epidemic threshold and would promote further studies on the phase transition of epidemic dynamics. AIP Publishing LLC 2015-06 2015-06-04 /pmc/articles/PMC7112466/ /pubmed/26117098 http://dx.doi.org/10.1063/1.4922153 Text en © 2015 AIP Publishing LLC 1054-1500/2015/25(6)/063104/8/$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 Shu, Panpan Wang, Wei Tang, Ming Do, Younghae Numerical identification of epidemic thresholds for susceptible-infected-recovered model on finite-size networks |
title | Numerical identification of epidemic thresholds for
susceptible-infected-recovered model on finite-size networks |
title_full | Numerical identification of epidemic thresholds for
susceptible-infected-recovered model on finite-size networks |
title_fullStr | Numerical identification of epidemic thresholds for
susceptible-infected-recovered model on finite-size networks |
title_full_unstemmed | Numerical identification of epidemic thresholds for
susceptible-infected-recovered model on finite-size networks |
title_short | Numerical identification of epidemic thresholds for
susceptible-infected-recovered model on finite-size networks |
title_sort | numerical identification of epidemic thresholds for
susceptible-infected-recovered model on finite-size networks |
topic | Regular Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112466/ https://www.ncbi.nlm.nih.gov/pubmed/26117098 http://dx.doi.org/10.1063/1.4922153 |
work_keys_str_mv | AT shupanpan numericalidentificationofepidemicthresholdsforsusceptibleinfectedrecoveredmodelonfinitesizenetworks AT wangwei numericalidentificationofepidemicthresholdsforsusceptibleinfectedrecoveredmodelonfinitesizenetworks AT tangming numericalidentificationofepidemicthresholdsforsusceptibleinfectedrecoveredmodelonfinitesizenetworks AT doyounghae numericalidentificationofepidemicthresholdsforsusceptibleinfectedrecoveredmodelonfinitesizenetworks |