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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Spatio-temporal evolution of Beijing 2003 SARS epidemic</title><author><name sortKey="Cao, Zhidong" sort="Cao, Zhidong" uniqKey="Cao Z" first="Zhidong" last="Cao">Zhidong Cao</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.9227.e</institution-id><institution-id institution-id-type="ISNI">0000000119573309</institution-id><institution>Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation,</institution><institution>Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100190 China</nlm:aff></affiliation></author><author><name sortKey="Zeng, Dajun" sort="Zeng, Dajun" uniqKey="Zeng D" first="Dajun" last="Zeng">Dajun Zeng</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.9227.e</institution-id><institution-id institution-id-type="ISNI">0000000119573309</institution-id><institution>Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation,</institution><institution>Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100190 China</nlm:aff></affiliation></author><author><name sortKey="Zheng, Xiaolong" sort="Zheng, Xiaolong" uniqKey="Zheng X" first="Xiaolong" last="Zheng">Xiaolong Zheng</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.9227.e</institution-id><institution-id institution-id-type="ISNI">0000000119573309</institution-id><institution>Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation,</institution><institution>Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100190 China</nlm:aff></affiliation></author><author><name sortKey="Wang, Quanyi" sort="Wang, Quanyi" uniqKey="Wang Q" first="Quanyi" last="Wang">Quanyi Wang</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.198530.6</institution-id><institution-id institution-id-type="ISNI">0000000088032373</institution-id><institution>Beijing Center for Disease Control and Prevention,</institution></institution-wrap>Beijing, 100013 China</nlm:aff></affiliation></author><author><name sortKey="Wang, Feiyue" sort="Wang, Feiyue" uniqKey="Wang F" first="Feiyue" last="Wang">Feiyue Wang</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.9227.e</institution-id><institution-id institution-id-type="ISNI">0000000119573309</institution-id><institution>Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation,</institution><institution>Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100190 China</nlm:aff></affiliation></author><author><name sortKey="Wang, Jinfeng" sort="Wang, Jinfeng" uniqKey="Wang J" first="Jinfeng" last="Wang">Jinfeng Wang</name><affiliation><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="GRID">grid.9227.e</institution-id><institution-id institution-id-type="ISNI">0000000119573309</institution-id><institution>State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research,</institution><institution>Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation></author><author><name sortKey="Wang, Xiaoli" sort="Wang, Xiaoli" uniqKey="Wang X" first="Xiaoli" last="Wang">Xiaoli Wang</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.198530.6</institution-id><institution-id institution-id-type="ISNI">0000000088032373</institution-id><institution>Beijing Center for Disease Control and Prevention,</institution></institution-wrap>Beijing, 100013 China</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmc">7104599</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104599</idno><idno type="RBID">PMC:7104599</idno><idno type="doi">10.1007/s11430-010-0043-x</idno><idno type="pmid">NONE</idno><date when="2010">2010</date><idno type="wicri:Area/Pmc/Corpus">000463</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000463</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Spatio-temporal evolution of Beijing 2003 SARS epidemic</title><author><name sortKey="Cao, Zhidong" sort="Cao, Zhidong" uniqKey="Cao Z" first="Zhidong" last="Cao">Zhidong Cao</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.9227.e</institution-id><institution-id institution-id-type="ISNI">0000000119573309</institution-id><institution>Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation,</institution><institution>Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100190 China</nlm:aff></affiliation></author><author><name sortKey="Zeng, Dajun" sort="Zeng, Dajun" uniqKey="Zeng D" first="Dajun" last="Zeng">Dajun Zeng</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.9227.e</institution-id><institution-id institution-id-type="ISNI">0000000119573309</institution-id><institution>Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation,</institution><institution>Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100190 China</nlm:aff></affiliation></author><author><name sortKey="Zheng, Xiaolong" sort="Zheng, Xiaolong" uniqKey="Zheng X" first="Xiaolong" last="Zheng">Xiaolong Zheng</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.9227.e</institution-id><institution-id institution-id-type="ISNI">0000000119573309</institution-id><institution>Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation,</institution><institution>Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100190 China</nlm:aff></affiliation></author><author><name sortKey="Wang, Quanyi" sort="Wang, Quanyi" uniqKey="Wang Q" first="Quanyi" last="Wang">Quanyi Wang</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.198530.6</institution-id><institution-id institution-id-type="ISNI">0000000088032373</institution-id><institution>Beijing Center for Disease Control and Prevention,</institution></institution-wrap>Beijing, 100013 China</nlm:aff></affiliation></author><author><name sortKey="Wang, Feiyue" sort="Wang, Feiyue" uniqKey="Wang F" first="Feiyue" last="Wang">Feiyue Wang</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.9227.e</institution-id><institution-id institution-id-type="ISNI">0000000119573309</institution-id><institution>Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation,</institution><institution>Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100190 China</nlm:aff></affiliation></author><author><name sortKey="Wang, Jinfeng" sort="Wang, Jinfeng" uniqKey="Wang J" first="Jinfeng" last="Wang">Jinfeng Wang</name><affiliation><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="GRID">grid.9227.e</institution-id><institution-id institution-id-type="ISNI">0000000119573309</institution-id><institution>State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research,</institution><institution>Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation></author><author><name sortKey="Wang, Xiaoli" sort="Wang, Xiaoli" uniqKey="Wang X" first="Xiaoli" last="Wang">Xiaoli Wang</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.198530.6</institution-id><institution-id institution-id-type="ISNI">0000000088032373</institution-id><institution>Beijing Center for Disease Control and Prevention,</institution></institution-wrap>Beijing, 100013 China</nlm:aff></affiliation></author></analytic><series><title level="j">Science China. Earth Sciences</title><idno type="ISSN">1674-7313</idno><idno type="eISSN">1869-1897</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Studying spatio-temporal evolution of epidemics can uncover important aspects of interaction among people, infectious diseases, and the environment, providing useful insights and modeling support to facilitate public health response and possibly prevention measures. This paper presents an empirical spatio-temporal analysis of epidemiological data concerning 2321 SARS-infected patients in Beijing in 2003. We mapped the SARS morbidity data with the spatial data resolution at the level of street and township. Two smoothing methods, Bayesian adjustment and spatial smoothing, were applied to identify the spatial risks and spatial transmission trends. Furthermore, we explored various spatial patterns and spatio-temporal evolution of Beijing 2003 SARS epidemic using spatial statistics such as Moran’s I and LISA. Part of this study is targeted at evaluating the effectiveness of public health control measures implemented during the SARS epidemic. The main findings are as follows. (1) The diffusion speed of SARS in the northwest-southeast direction is weaker than that in northeast-southwest direction. (2) SARS’s spread risk is positively spatially associated and the strength of this spatial association has experienced changes from weak to strong and then back to weak during the lifetime of the Beijing SARS epidemic. (3) Two spatial clusters of disease cases are identified: one in the city center and the other in the eastern suburban area. These two clusters followed different evolutionary paths but interacted with each other as well. (4) Although the government missed the opportunity to contain the early outbreak of SARS in March 2003, the response strategies implemented after the mid of April were effective. These response measures not only controlled the growth of the disease cases, but also mitigated the spatial diffusion.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Oxford, J S" uniqKey="Oxford J">J. S. Oxford</name></author><author><name sortKey="Bossuyt, S" uniqKey="Bossuyt S">S. Bossuyt</name></author><author><name sortKey="Lambkin, R" uniqKey="Lambkin R">R. Lambkin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Marra, M A" uniqKey="Marra M">M. A. Marra</name></author><author><name sortKey="Jones, S J" uniqKey="Jones S">S. J. Jones</name></author><author><name sortKey="Astell, C R" uniqKey="Astell C">C. R. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Sci China Earth Sci</journal-id><journal-id journal-id-type="iso-abbrev">Sci China Earth Sci</journal-id><journal-title-group><journal-title>Science China. Earth Sciences</journal-title></journal-title-group><issn pub-type="ppub">1674-7313</issn><issn pub-type="epub">1869-1897</issn><publisher><publisher-name>SP Science China Press</publisher-name><publisher-loc>Heidelberg</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmc">7104599</article-id><article-id pub-id-type="publisher-id">43</article-id><article-id pub-id-type="doi">10.1007/s11430-010-0043-x</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Paper</subject></subj-group></article-categories><title-group><article-title>Spatio-temporal evolution of Beijing 2003 SARS epidemic</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Cao</surname><given-names>ZhiDong</given-names></name><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author" corresp="yes"><name><surname>Zeng</surname><given-names>DaJun</given-names></name><address><email>dajun.zeng@ia.ac.cn</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Zheng</surname><given-names>XiaoLong</given-names></name><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Wang</surname><given-names>QuanYi</given-names></name><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Wang</surname><given-names>FeiYue</given-names></name><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Wang</surname><given-names>JinFeng</given-names></name><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Wang</surname><given-names>XiaoLi</given-names></name><xref ref-type="aff" rid="Aff2">2</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="GRID">grid.9227.e</institution-id><institution-id institution-id-type="ISNI">0000000119573309</institution-id><institution>Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation,</institution><institution>Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100190 China</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="GRID">grid.198530.6</institution-id><institution-id institution-id-type="ISNI">0000000088032373</institution-id><institution>Beijing Center for Disease Control and Prevention,</institution></institution-wrap>Beijing, 100013 China</aff><aff id="Aff3"><label>3</label><institution-wrap><institution-id institution-id-type="GRID">grid.9227.e</institution-id><institution-id institution-id-type="ISNI">0000000119573309</institution-id><institution>State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research,</institution><institution>Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100101 China</aff></contrib-group><pub-date pub-type="epub"><day>12</day><month>5</month><year>2010</year></pub-date><pub-date pub-type="ppub"><year>2010</year></pub-date><volume>53</volume><issue>7</issue><fpage>1017</fpage><lpage>1028</lpage><history><date date-type="received"><day>19</day><month>5</month><year>2009</year></date><date date-type="accepted"><day>28</day><month>10</month><year>2009</year></date></history><permissions><copyright-statement>© Science China Press and Springer-Verlag Berlin Heidelberg 2010</copyright-statement><license><license-p>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.</license-p></license></permissions><abstract id="Abs1"><p>Studying spatio-temporal evolution of epidemics can uncover important aspects of interaction among people, infectious diseases, and the environment, providing useful insights and modeling support to facilitate public health response and possibly prevention measures. This paper presents an empirical spatio-temporal analysis of epidemiological data concerning 2321 SARS-infected patients in Beijing in 2003. We mapped the SARS morbidity data with the spatial data resolution at the level of street and township. Two smoothing methods, Bayesian adjustment and spatial smoothing, were applied to identify the spatial risks and spatial transmission trends. Furthermore, we explored various spatial patterns and spatio-temporal evolution of Beijing 2003 SARS epidemic using spatial statistics such as Moran’s I and LISA. Part of this study is targeted at evaluating the effectiveness of public health control measures implemented during the SARS epidemic. The main findings are as follows. (1) The diffusion speed of SARS in the northwest-southeast direction is weaker than that in northeast-southwest direction. (2) SARS’s spread risk is positively spatially associated and the strength of this spatial association has experienced changes from weak to strong and then back to weak during the lifetime of the Beijing SARS epidemic. (3) Two spatial clusters of disease cases are identified: one in the city center and the other in the eastern suburban area. These two clusters followed different evolutionary paths but interacted with each other as well. (4) Although the government missed the opportunity to contain the early outbreak of SARS in March 2003, the response strategies implemented after the mid of April were effective. These response measures not only controlled the growth of the disease cases, but also mitigated the spatial diffusion.</p></abstract><kwd-group xml:lang="en"><title>Keyword</title><kwd>severe acute respiratory syndrome (SARS)</kwd><kwd>Beijing</kwd><kwd>morbidity rate</kwd><kwd>spatial analysis</kwd><kwd>spatio-temporal evolution</kwd><kwd>control measures</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© Science China Press and Springer-Verlag Berlin Heidelberg 2010</meta-value></custom-meta></custom-meta-group></article-meta></front><back><ref-list id="Bib1"><title>References</title><ref id="CR1"><label>[1]</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Oxford</surname><given-names>J. 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