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Responsive immunization and intervention for infectious diseases in social networks
By using the microscopic Markov-chain approximation approach, we investigate the epidemic spreading and the responsive immunization in social networks. It is assumed that individual vaccination behavior depends on the local information of an epidemic. Our results suggest that the responsive immuniza...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Institute of Physics
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112455/ https://www.ncbi.nlm.nih.gov/pubmed/24985422 http://dx.doi.org/10.1063/1.4872177 |
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author | Wu, Qingchu Zhang, Haifeng Zeng, Guanghong |
author_facet | Wu, Qingchu Zhang, Haifeng Zeng, Guanghong |
author_sort | Wu, Qingchu |
collection | PubMed |
description | By using the microscopic Markov-chain approximation approach, we investigate the epidemic spreading and the responsive immunization in social networks. It is assumed that individual vaccination behavior depends on the local information of an epidemic. Our results suggest that the responsive immunization has negligible impact on the epidemic threshold and the critical value of initial epidemic outbreak, but it can effectively inhibit the outbreak of epidemic. We also analyze the influence of the intervention on the disease dynamics, where the vaccination is available only to those individuals whose number of neighbors is greater than a certain value. Simulation analysis implies that the intervention strategy can effectively reduce the vaccine use under the epidemic control. |
format | Online Article Text |
id | pubmed-7112455 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | American Institute of Physics |
record_format | MEDLINE/PubMed |
spelling | pubmed-71124552020-04-02 Responsive immunization and intervention for infectious diseases in social networks Wu, Qingchu Zhang, Haifeng Zeng, Guanghong Chaos Regular Articles By using the microscopic Markov-chain approximation approach, we investigate the epidemic spreading and the responsive immunization in social networks. It is assumed that individual vaccination behavior depends on the local information of an epidemic. Our results suggest that the responsive immunization has negligible impact on the epidemic threshold and the critical value of initial epidemic outbreak, but it can effectively inhibit the outbreak of epidemic. We also analyze the influence of the intervention on the disease dynamics, where the vaccination is available only to those individuals whose number of neighbors is greater than a certain value. Simulation analysis implies that the intervention strategy can effectively reduce the vaccine use under the epidemic control. American Institute of Physics 2014-06 2014-04-24 /pmc/articles/PMC7112455/ /pubmed/24985422 http://dx.doi.org/10.1063/1.4872177 Text en © 2014 AIP Publishing LLC 1054-1500/2014/24(2)/023108/8/$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 Wu, Qingchu Zhang, Haifeng Zeng, Guanghong Responsive immunization and intervention for infectious diseases in social networks |
title | Responsive immunization and intervention for infectious diseases in social
networks |
title_full | Responsive immunization and intervention for infectious diseases in social
networks |
title_fullStr | Responsive immunization and intervention for infectious diseases in social
networks |
title_full_unstemmed | Responsive immunization and intervention for infectious diseases in social
networks |
title_short | Responsive immunization and intervention for infectious diseases in social
networks |
title_sort | responsive immunization and intervention for infectious diseases in social
networks |
topic | Regular Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112455/ https://www.ncbi.nlm.nih.gov/pubmed/24985422 http://dx.doi.org/10.1063/1.4872177 |
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