Cargando…
Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment
We propose a new four state model for disease transmission and illustrate the model with data from the 2003 SARS epidemic in Hong Kong. The critical feature of this model is that the community is modelled as a small-world network of interconnected nodes. Each node is linked to a fixed number of imme...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2005
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7126158/ https://www.ncbi.nlm.nih.gov/pubmed/32288075 http://dx.doi.org/10.1016/j.physa.2005.01.009 |
_version_ | 1783516088964743168 |
---|---|
author | Small, Michael Tse, C.K. |
author_facet | Small, Michael Tse, C.K. |
author_sort | Small, Michael |
collection | PubMed |
description | We propose a new four state model for disease transmission and illustrate the model with data from the 2003 SARS epidemic in Hong Kong. The critical feature of this model is that the community is modelled as a small-world network of interconnected nodes. Each node is linked to a fixed number of immediate neighbors and a random number of geographically remote nodes. Transmission can only propagate between linked nodes. This model exhibits two features typical of SARS transmission: geographically localized outbreaks and “super-spreaders”. Neither of these features are evident in standard susceptible-infected-removed models of disease transmission. Our analysis indicates that “super-spreaders” may occur even if the infectiousness of all infected individuals is constant. Moreover, we find that nosocomial transmission in Hong Kong directly contributed to the severity of the outbreak and that by limiting individual exposure time to 3–5 days the extent of the SARS epidemic would have been minimal. |
format | Online Article Text |
id | pubmed-7126158 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2005 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71261582020-04-08 Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment Small, Michael Tse, C.K. Physica A Article We propose a new four state model for disease transmission and illustrate the model with data from the 2003 SARS epidemic in Hong Kong. The critical feature of this model is that the community is modelled as a small-world network of interconnected nodes. Each node is linked to a fixed number of immediate neighbors and a random number of geographically remote nodes. Transmission can only propagate between linked nodes. This model exhibits two features typical of SARS transmission: geographically localized outbreaks and “super-spreaders”. Neither of these features are evident in standard susceptible-infected-removed models of disease transmission. Our analysis indicates that “super-spreaders” may occur even if the infectiousness of all infected individuals is constant. Moreover, we find that nosocomial transmission in Hong Kong directly contributed to the severity of the outbreak and that by limiting individual exposure time to 3–5 days the extent of the SARS epidemic would have been minimal. Elsevier B.V. 2005-06-15 2005-01-26 /pmc/articles/PMC7126158/ /pubmed/32288075 http://dx.doi.org/10.1016/j.physa.2005.01.009 Text en Copyright © 2005 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Small, Michael Tse, C.K. Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment |
title | Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment |
title_full | Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment |
title_fullStr | Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment |
title_full_unstemmed | Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment |
title_short | Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment |
title_sort | clustering model for transmission of the sars virus: application to epidemic control and risk assessment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7126158/ https://www.ncbi.nlm.nih.gov/pubmed/32288075 http://dx.doi.org/10.1016/j.physa.2005.01.009 |
work_keys_str_mv | AT smallmichael clusteringmodelfortransmissionofthesarsvirusapplicationtoepidemiccontrolandriskassessment AT tseck clusteringmodelfortransmissionofthesarsvirusapplicationtoepidemiccontrolandriskassessment |