Understanding the spatial diffusion process of severe acute respiratory syndrome in Beijing
Identifieur interne : 001196 ( Pmc/Checkpoint ); précédent : 001195; suivant : 001197Understanding the spatial diffusion process of severe acute respiratory syndrome in Beijing
Auteurs : B. Meng [République populaire de Chine] ; J. Wang [République populaire de Chine] ; J. Liu [République populaire de Chine] ; J. Wu [République populaire de Chine] ; E. Zhong [République populaire de Chine]Source :
- Public Health [ 0033-3506 ] ; 2005.
Abstract
To measure the spatial contagion of severe acute respiratory syndrome (SARS) in Beijing and to test the different epidemic factors of the spread of SARS in different periods.
A join-count spatial statistic study was conducted and the given hypothetical processes of the spread of SARS in Beijing were tested using various definitions of ‘joins’.
The spatial statistics showed that of the six diffusion processes, the highest negative autocorrelation occurred in the doctor-number model (M-5) and the lowest negative autocorrelation was found in the population-amount model (M-3). The results also showed that in the whole 29-day research period, about hour or more days experienced a significant degree of contagion.
Spatial analysis is helpful in understanding the spatial diffusion process of an epidemic. The geographical relationships were important during the early phase of the SARS epidemic in Beijing. The statistic based on the number of doctors was significant and more informative than that of the number of hospitals. It reveals that doctors were important in the spread of SARS in Beijing, and hospitals were not as important as doctors in the contagion period. People are the key to the spread of SARS, but the population density was more significant than the population size, although they were both important throughout the whole period.
Url:
DOI: 10.1016/j.puhe.2005.02.003
PubMed: 16214187
PubMed Central: 7111650
Affiliations:
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<front><div type="abstract" xml:lang="en"><title>Summary</title>
<sec><title>Objectives</title>
<p>To measure the spatial contagion of severe acute respiratory syndrome (SARS) in Beijing and to test the different epidemic factors of the spread of SARS in different periods.</p>
</sec>
<sec><title>Methods</title>
<p>A join-count spatial statistic study was conducted and the given hypothetical processes of the spread of SARS in Beijing were tested using various definitions of ‘joins’.</p>
</sec>
<sec><title>Results</title>
<p>The spatial statistics showed that of the six diffusion processes, the highest negative autocorrelation occurred in the doctor-number model (M-5) and the lowest negative autocorrelation was found in the population-amount model (M-3). The results also showed that in the whole 29-day research period, about hour or more days experienced a significant degree of contagion.</p>
</sec>
<sec><title>Conclusions</title>
<p>Spatial analysis is helpful in understanding the spatial diffusion process of an epidemic. The geographical relationships were important during the early phase of the SARS epidemic in Beijing. The statistic based on the number of doctors was significant and more informative than that of the number of hospitals. It reveals that doctors were important in the spread of SARS in Beijing, and hospitals were not as important as doctors in the contagion period. People are the key to the spread of SARS, but the population density was more significant than the population size, although they were both important throughout the whole period.</p>
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<author><name sortKey="Ord, J K" uniqKey="Ord J">J.K. Ord</name>
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</author>
<author><name sortKey="Chen, R C H" uniqKey="Chen R">R.C.H. Chen</name>
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</author>
<author><name sortKey="Liu, P M" uniqKey="Liu P">P.M. Liu</name>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Public Health</journal-id>
<journal-id journal-id-type="iso-abbrev">Public Health</journal-id>
<journal-title-group><journal-title>Public Health</journal-title>
</journal-title-group>
<issn pub-type="ppub">0033-3506</issn>
<issn pub-type="epub">1476-5616</issn>
<publisher><publisher-name>The Royal Institute of Public Health. Published by Elsevier Ltd.</publisher-name>
</publisher>
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<article-meta><article-id pub-id-type="pmid">16214187</article-id>
<article-id pub-id-type="pmc">7111650</article-id>
<article-id pub-id-type="publisher-id">S0033-3506(05)00081-8</article-id>
<article-id pub-id-type="doi">10.1016/j.puhe.2005.02.003</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Understanding the spatial diffusion process of severe acute respiratory syndrome in Beijing</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Meng</surname>
<given-names>B.</given-names>
</name>
<email>mengb@lreis.ac.cn</email>
<xref rid="aff1" ref-type="aff">a</xref>
<xref rid="aff2" ref-type="aff">b</xref>
<xref rid="cor1" ref-type="corresp">⁎</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Wang</surname>
<given-names>J.</given-names>
</name>
<xref rid="aff1" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Liu</surname>
<given-names>J.</given-names>
</name>
<xref rid="aff1" ref-type="aff">a</xref>
<xref rid="aff2" ref-type="aff">b</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Wu</surname>
<given-names>J.</given-names>
</name>
<xref rid="aff1" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Zhong</surname>
<given-names>E.</given-names>
</name>
<xref rid="aff1" ref-type="aff">a</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>a</label>
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China</aff>
<aff id="aff2"><label>b</label>
College of Arts & Science of Beijing Union University, Beijing, China</aff>
<author-notes><corresp id="cor1"><label>⁎</label>
Corresponding author. Address: State Key Laboratory of Resources & Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Anwai, Beijing 100101, China. Tel.: +86 10 6200 4530; fax: +86 10 6200 4530. <email>mengb@lreis.ac.cn</email>
</corresp>
</author-notes>
<pub-date pub-type="pmc-release"><day>7</day>
<month>10</month>
<year>2005</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="ppub"><month>12</month>
<year>2005</year>
</pub-date>
<pub-date pub-type="epub"><day>7</day>
<month>10</month>
<year>2005</year>
</pub-date>
<volume>119</volume>
<issue>12</issue>
<fpage>1080</fpage>
<lpage>1087</lpage>
<history><date date-type="received"><day>10</day>
<month>5</month>
<year>2004</year>
</date>
<date date-type="rev-recd"><day>29</day>
<month>9</month>
<year>2004</year>
</date>
<date date-type="accepted"><day>8</day>
<month>2</month>
<year>2005</year>
</date>
</history>
<permissions><copyright-statement>Copyright © 2005 The Royal Institute of Public Health. Published by Elsevier Ltd. All rights reserved.</copyright-statement>
<copyright-year>2005</copyright-year>
<copyright-holder>The Royal Institute of Public Health</copyright-holder>
<license><license-p>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.</license-p>
</license>
</permissions>
<abstract><title>Summary</title>
<sec><title>Objectives</title>
<p>To measure the spatial contagion of severe acute respiratory syndrome (SARS) in Beijing and to test the different epidemic factors of the spread of SARS in different periods.</p>
</sec>
<sec><title>Methods</title>
<p>A join-count spatial statistic study was conducted and the given hypothetical processes of the spread of SARS in Beijing were tested using various definitions of ‘joins’.</p>
</sec>
<sec><title>Results</title>
<p>The spatial statistics showed that of the six diffusion processes, the highest negative autocorrelation occurred in the doctor-number model (M-5) and the lowest negative autocorrelation was found in the population-amount model (M-3). The results also showed that in the whole 29-day research period, about hour or more days experienced a significant degree of contagion.</p>
</sec>
<sec><title>Conclusions</title>
<p>Spatial analysis is helpful in understanding the spatial diffusion process of an epidemic. The geographical relationships were important during the early phase of the SARS epidemic in Beijing. The statistic based on the number of doctors was significant and more informative than that of the number of hospitals. It reveals that doctors were important in the spread of SARS in Beijing, and hospitals were not as important as doctors in the contagion period. People are the key to the spread of SARS, but the population density was more significant than the population size, although they were both important throughout the whole period.</p>
</sec>
</abstract>
<kwd-group><title>Keywords</title>
<kwd>SARS</kwd>
<kwd>Spatial analysis</kwd>
<kwd>Spatial diffusion</kwd>
<kwd>Beijing</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>République populaire de Chine</li>
</country>
<settlement><li>Pékin</li>
</settlement>
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<name sortKey="Liu, J" sort="Liu, J" uniqKey="Liu J" first="J." last="Liu">J. Liu</name>
<name sortKey="Liu, J" sort="Liu, J" uniqKey="Liu J" first="J." last="Liu">J. Liu</name>
<name sortKey="Meng, B" sort="Meng, B" uniqKey="Meng B" first="B." last="Meng">B. Meng</name>
<name sortKey="Wang, J" sort="Wang, J" uniqKey="Wang J" first="J." last="Wang">J. Wang</name>
<name sortKey="Wu, J" sort="Wu, J" uniqKey="Wu J" first="J." last="Wu">J. Wu</name>
<name sortKey="Zhong, E" sort="Zhong, E" uniqKey="Zhong E" first="E." last="Zhong">E. Zhong</name>
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