Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China
Identifieur interne : 001C35 ( Ncbi/Merge ); précédent : 001C34; suivant : 001C36Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China
Auteurs : Jin-Feng Wang [République populaire de Chine] ; George Christakos [États-Unis] ; Wei-Guo Han [États-Unis] ; Bin Meng [République populaire de Chine]Source :
- Journal of Public Health (Oxford, England) [ 1741-3842 ] ; 2008.
Abstract
Severe Acute Respiratory Syndrome (SARS) was first reported in November 2002 in China, and spreads to about 30 countries over the next few months. While the characteristics of epidemic transmission are individually assessed, there are also important implicit associations between them.
A novel methodological framework was developed to overcome barriers among separate epidemic statistics and identify distinctive SARS features. Individual statistics were pair-wise linked in terms of their common features, and an integrative epidemic network was formulated.
The study of associations between important SARS characteristics considerably enhanced the mainstream epidemic analysis and improved the understanding of the relationships between the observed epidemic determinants. The response of SARS transmission to various epidemic control factors was simulated, target areas were detected, critical time and relevant factors were determined.
It was shown that by properly accounting for links between different SARS statistics, a data-based analysis can efficiently reveal systematic associations between epidemic determinants. The analysis can predict the temporal trend of the epidemic given its spatial pattern, to estimate spatial exposure given temporal evolution, and to infer the driving forces of SARS transmission given the spatial exposure distribution.
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DOI: 10.1093/pubmed/fdn023
PubMed: 18441347
PubMed Central: 2518065
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State University</institution>,<addr-line>San Diego, CA 92182-4493</addr-line>,<country>USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Han, Wei Guo" sort="Han, Wei Guo" uniqKey="Han W" first="Wei-Guo" last="Han">Wei-Guo Han</name><affiliation wicri:level="1"><nlm:aff id="af3"><institution>Center for Spatial Information Science and System, George Mason University</institution>,<addr-line>6301 Ivy Lane, Greenbelt, MD 20770</addr-line>,<country>USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Meng, Bin" sort="Meng, Bin" uniqKey="Meng B" first="Bin" last="Meng">Bin Meng</name><affiliation wicri:level="1"><nlm:aff id="af4"><institution>College of Applied Sciences and Humanities, Beijing Union University</institution>,<addr-line>Beijing 100083</addr-line>,<country>China</country></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">18441347</idno><idno type="pmc">2518065</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2518065</idno><idno type="RBID">PMC:2518065</idno><idno type="doi">10.1093/pubmed/fdn023</idno><date when="2008">2008</date><idno type="wicri:Area/Pmc/Corpus">000C20</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000C20</idno><idno type="wicri:Area/Pmc/Curation">000C20</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000C20</idno><idno type="wicri:Area/Pmc/Checkpoint">000E32</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000E32</idno><idno type="wicri:Area/Ncbi/Merge">001C35</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China</title><author><name sortKey="Wang, Jin Feng" sort="Wang, Jin Feng" uniqKey="Wang J" first="Jin-Feng" last="Wang">Jin-Feng Wang</name><affiliation wicri:level="1"><nlm:aff id="af1"><institution>Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences</institution>,<addr-line>A11, Datun Rd, Anwai, Beijing 100101</addr-line>,<country>China</country></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Christakos, George" sort="Christakos, George" uniqKey="Christakos G" first="George" last="Christakos">George Christakos</name><affiliation wicri:level="1"><nlm:aff id="af2"><addr-line>Department of Geography</addr-line>,<institution>San Diego State University</institution>,<addr-line>San Diego, CA 92182-4493</addr-line>,<country>USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Han, Wei Guo" sort="Han, Wei Guo" uniqKey="Han W" first="Wei-Guo" last="Han">Wei-Guo Han</name><affiliation wicri:level="1"><nlm:aff id="af3"><institution>Center for Spatial Information Science and System, George Mason University</institution>,<addr-line>6301 Ivy Lane, Greenbelt, MD 20770</addr-line>,<country>USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Meng, Bin" sort="Meng, Bin" uniqKey="Meng B" first="Bin" last="Meng">Bin Meng</name><affiliation wicri:level="1"><nlm:aff id="af4"><institution>College of Applied Sciences and Humanities, Beijing Union University</institution>,<addr-line>Beijing 100083</addr-line>,<country>China</country></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author></analytic><series><title level="j">Journal of Public Health (Oxford, England)</title><idno type="ISSN">1741-3842</idno><idno type="eISSN">1741-3850</idno><imprint><date when="2008">2008</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Severe Acute Respiratory Syndrome (SARS) was first reported in November 2002 in China, and spreads to about 30 countries over the next few months. While the characteristics of epidemic transmission are individually assessed, there are also important implicit associations between them.</p></sec><sec sec-type="methods"><title>Methods</title><p>A novel methodological framework was developed to overcome barriers among separate epidemic statistics and identify distinctive SARS features. Individual statistics were pair-wise linked in terms of their common features, and an integrative epidemic network was formulated.</p></sec><sec><title>Results</title><p>The study of associations between important SARS characteristics considerably enhanced the mainstream epidemic analysis and improved the understanding of the relationships between the observed epidemic determinants. The response of SARS transmission to various epidemic control factors was simulated, target areas were detected, critical time and relevant factors were determined.</p></sec><sec><title>Conclusion</title><p>It was shown that by properly accounting for links between different SARS statistics, a data-based analysis can efficiently reveal systematic associations between epidemic determinants. The analysis can predict the temporal trend of the epidemic given its spatial pattern, to estimate spatial exposure given temporal evolution, and to infer the driving forces of SARS transmission given the spatial exposure distribution.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article" xml:lang="EN"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">J Public Health (Oxf)</journal-id><journal-id journal-id-type="publisher-id">pubmed</journal-id><journal-id journal-id-type="hwp">jphm</journal-id><journal-title>Journal of Public Health (Oxford, England)</journal-title><issn pub-type="ppub">1741-3842</issn><issn pub-type="epub">1741-3850</issn><publisher><publisher-name>Oxford University Press</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">18441347</article-id><article-id pub-id-type="pmc">2518065</article-id><article-id pub-id-type="doi">10.1093/pubmed/fdn023</article-id><article-id pub-id-type="publisher-id">fdn023</article-id><article-categories><subj-group subj-group-type="heading"><subject>Health Protection</subject></subj-group></article-categories><title-group><article-title>Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Wang</surname><given-names>Jin-Feng</given-names></name><role>Professor of Geoinformatics</role><xref ref-type="aff" rid="af1">1</xref><xref ref-type="corresp" rid="cor1"></xref></contrib><contrib contrib-type="author"><name><surname>Christakos</surname><given-names>George</given-names></name><role>Professor of Geography</role><xref ref-type="aff" rid="af2">2</xref></contrib><contrib contrib-type="author"><name><surname>Han</surname><given-names>Wei-Guo</given-names></name><role>Scientist of Computation</role><xref ref-type="aff" rid="af3">3</xref></contrib><contrib contrib-type="author"><name><surname>Meng</surname><given-names>Bin</given-names></name><role>Lecturer of Human Geography</role><xref ref-type="aff" rid="af4">4</xref></contrib></contrib-group><aff id="af1"><label>1</label><institution>Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences</institution>,<addr-line>A11, Datun Rd, Anwai, Beijing 100101</addr-line>,<country>China</country></aff><aff id="af2"><label>2</label><addr-line>Department of Geography</addr-line>,<institution>San Diego State University</institution>,<addr-line>San Diego, CA 92182-4493</addr-line>,<country>USA</country></aff><aff id="af3"><label>3</label><institution>Center for Spatial Information Science and System, George Mason University</institution>,<addr-line>6301 Ivy Lane, Greenbelt, MD 20770</addr-line>,<country>USA</country></aff><aff id="af4"><label>4</label><institution>College of Applied Sciences and Humanities, Beijing Union University</institution>,<addr-line>Beijing 100083</addr-line>,<country>China</country></aff><author-notes><corresp id="cor1">Address correspondence to Jin-Feng Wang, E-mail: <email>wangjf@igsnrr.ac.cn</email></corresp></author-notes><pub-date pub-type="ppub"><month>9</month><year>2008</year></pub-date><pub-date pub-type="epub"><day>26</day><month>4</month><year>2008</year></pub-date><pub-date pub-type="pmc-release"><day>26</day><month>4</month><year>2008</year></pub-date><volume>30</volume><issue>3</issue><fpage>234</fpage><lpage>244</lpage><copyright-statement>© The Author 2008. Published by Oxford University Press on behalf of the Faculty of Public Health. All rights reserved</copyright-statement><copyright-year>2008</copyright-year><license license-type="creative-commons" xlink:href="http://creativecommons.org/licenses/by-nc/2.0/uk/"><p>The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oupjournals.org</p></license><abstract><sec><title>Background</title><p>Severe Acute Respiratory Syndrome (SARS) was first reported in November 2002 in China, and spreads to about 30 countries over the next few months. While the characteristics of epidemic transmission are individually assessed, there are also important implicit associations between them.</p></sec><sec sec-type="methods"><title>Methods</title><p>A novel methodological framework was developed to overcome barriers among separate epidemic statistics and identify distinctive SARS features. Individual statistics were pair-wise linked in terms of their common features, and an integrative epidemic network was formulated.</p></sec><sec><title>Results</title><p>The study of associations between important SARS characteristics considerably enhanced the mainstream epidemic analysis and improved the understanding of the relationships between the observed epidemic determinants. The response of SARS transmission to various epidemic control factors was simulated, target areas were detected, critical time and relevant factors were determined.</p></sec><sec><title>Conclusion</title><p>It was shown that by properly accounting for links between different SARS statistics, a data-based analysis can efficiently reveal systematic associations between epidemic determinants. The analysis can predict the temporal trend of the epidemic given its spatial pattern, to estimate spatial exposure given temporal evolution, and to infer the driving forces of SARS transmission given the spatial exposure distribution.</p></sec></abstract><kwd-group><title>Keywords</title><kwd>associations</kwd><kwd>determinants</kwd><kwd>epidemic</kwd><kwd>SARS</kwd><kwd>spatial pattern</kwd><kwd>statistics</kwd><kwd>time evolution</kwd></kwd-group></article-meta></front></pmc><affiliations><list><country><li>République populaire de Chine</li><li>États-Unis</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Wang, Jin Feng" sort="Wang, Jin Feng" uniqKey="Wang J" first="Jin-Feng" last="Wang">Jin-Feng Wang</name></noRegion><name sortKey="Meng, Bin" sort="Meng, Bin" uniqKey="Meng B" first="Bin" last="Meng">Bin Meng</name></country><country name="États-Unis"><noRegion><name sortKey="Christakos, George" sort="Christakos, George" uniqKey="Christakos G" first="George" last="Christakos">George Christakos</name></noRegion><name sortKey="Han, Wei Guo" sort="Han, Wei Guo" uniqKey="Han W" first="Wei-Guo" last="Han">Wei-Guo Han</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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