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Incorporating Geographical Contacts into Social Network Analysis for Contact Tracing in Epidemiology: A Study on Taiwan SARS Data
In epidemiology, contact tracing is a process to control the spread of an infectious disease and identify individuals who were previously exposed to patients with the disease. After the emergence of AIDS, Social Network Analysis (SNA) was demonstrated to be a good supplementary tool for contact trac...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122638/ http://dx.doi.org/10.1007/978-3-540-72608-1_3 |
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author | Chen, Yi-Da Tseng, Chunju King, Chwan-Chuen Wu, Tsung-Shu Joseph Chen, Hsinchun |
author_facet | Chen, Yi-Da Tseng, Chunju King, Chwan-Chuen Wu, Tsung-Shu Joseph Chen, Hsinchun |
author_sort | Chen, Yi-Da |
collection | PubMed |
description | In epidemiology, contact tracing is a process to control the spread of an infectious disease and identify individuals who were previously exposed to patients with the disease. After the emergence of AIDS, Social Network Analysis (SNA) was demonstrated to be a good supplementary tool for contact tracing. Traditionally, social networks for disease investigations are constructed only with personal contacts. However, for diseases which transmit not only through personal contacts, incorporating geographical contacts into SNA has been demonstrated to reveal potential contacts among patients. In this research, we use Taiwan SARS data to investigate the differences in connectivity between personal and geographical contacts in the construction of social networks for these diseases. According to our results, geographical contacts, which increase the average degree of nodes from 0 to 108.62 and decrease the number of components from 961 to 82, provide much higher connectivity than personal contacts. Therefore, including geographical contacts is important to understand the underlying context of the transmission of these diseases. We further explore the differences in network topology between one-mode networks with only patients and multi-mode networks with patients and geographical locations for disease investigation. We find that including geographical locations as nodes in a social network provides a good way to see the role that those locations play in the disease transmission and reveal potential bridges among those geographical locations and households. |
format | Online Article Text |
id | pubmed-7122638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
record_format | MEDLINE/PubMed |
spelling | pubmed-71226382020-04-06 Incorporating Geographical Contacts into Social Network Analysis for Contact Tracing in Epidemiology: A Study on Taiwan SARS Data Chen, Yi-Da Tseng, Chunju King, Chwan-Chuen Wu, Tsung-Shu Joseph Chen, Hsinchun Intelligence and Security Informatics: Biosurveillance Article In epidemiology, contact tracing is a process to control the spread of an infectious disease and identify individuals who were previously exposed to patients with the disease. After the emergence of AIDS, Social Network Analysis (SNA) was demonstrated to be a good supplementary tool for contact tracing. Traditionally, social networks for disease investigations are constructed only with personal contacts. However, for diseases which transmit not only through personal contacts, incorporating geographical contacts into SNA has been demonstrated to reveal potential contacts among patients. In this research, we use Taiwan SARS data to investigate the differences in connectivity between personal and geographical contacts in the construction of social networks for these diseases. According to our results, geographical contacts, which increase the average degree of nodes from 0 to 108.62 and decrease the number of components from 961 to 82, provide much higher connectivity than personal contacts. Therefore, including geographical contacts is important to understand the underlying context of the transmission of these diseases. We further explore the differences in network topology between one-mode networks with only patients and multi-mode networks with patients and geographical locations for disease investigation. We find that including geographical locations as nodes in a social network provides a good way to see the role that those locations play in the disease transmission and reveal potential bridges among those geographical locations and households. 2007 /pmc/articles/PMC7122638/ http://dx.doi.org/10.1007/978-3-540-72608-1_3 Text en © Springer Berlin Heidelberg 2007 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Chen, Yi-Da Tseng, Chunju King, Chwan-Chuen Wu, Tsung-Shu Joseph Chen, Hsinchun Incorporating Geographical Contacts into Social Network Analysis for Contact Tracing in Epidemiology: A Study on Taiwan SARS Data |
title | Incorporating Geographical Contacts into Social Network Analysis for Contact Tracing in Epidemiology: A Study on Taiwan SARS Data |
title_full | Incorporating Geographical Contacts into Social Network Analysis for Contact Tracing in Epidemiology: A Study on Taiwan SARS Data |
title_fullStr | Incorporating Geographical Contacts into Social Network Analysis for Contact Tracing in Epidemiology: A Study on Taiwan SARS Data |
title_full_unstemmed | Incorporating Geographical Contacts into Social Network Analysis for Contact Tracing in Epidemiology: A Study on Taiwan SARS Data |
title_short | Incorporating Geographical Contacts into Social Network Analysis for Contact Tracing in Epidemiology: A Study on Taiwan SARS Data |
title_sort | incorporating geographical contacts into social network analysis for contact tracing in epidemiology: a study on taiwan sars data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122638/ http://dx.doi.org/10.1007/978-3-540-72608-1_3 |
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