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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study</title><author><name sortKey="Hu, Bisong" sort="Hu, Bisong" uniqKey="Hu B" first="Bisong" last="Hu">Bisong Hu</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.419897.a</institution-id><institution-id institution-id-type="ISNI">000000040369313X</institution-id><institution>Geography and Environment Department,</institution><institution>Jiangxi Normal University/Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education,</institution></institution-wrap>Nanchang, 330022 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation></author><author><name sortKey="Gong, Jianhua" sort="Gong, Jianhua" uniqKey="Gong J" first="Jianhua" last="Gong">Jianhua Gong</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff3">Zhejiang-CAS Application Center for Geoinformatics, Jiaxing, 314100 China</nlm:aff></affiliation></author><author><name sortKey="Zhou, Jieping" sort="Zhou, Jieping" uniqKey="Zhou J" first="Jieping" last="Zhou">Jieping Zhou</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff3">Zhejiang-CAS Application Center for Geoinformatics, Jiaxing, 314100 China</nlm:aff></affiliation></author><author><name sortKey="Sun, Jun" sort="Sun, Jun" uniqKey="Sun J" first="Jun" last="Sun">Jun Sun</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation></author><author><name sortKey="Yang, Liyang" sort="Yang, Liyang" uniqKey="Yang L" first="Liyang" last="Yang">Liyang Yang</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation></author><author><name sortKey="Xia, Yu" sort="Xia, Yu" uniqKey="Xia Y" first="Yu" last="Xia">Yu Xia</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.419897.a</institution-id><institution-id institution-id-type="ISNI">000000040369313X</institution-id><institution>Geography and Environment Department,</institution><institution>Jiangxi Normal University/Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education,</institution></institution-wrap>Nanchang, 330022 China</nlm:aff></affiliation></author><author><name sortKey="Ibrahim, Abdoul Nasser" sort="Ibrahim, Abdoul Nasser" uniqKey="Ibrahim A" first="Abdoul Nasser" last="Ibrahim">Abdoul Nasser Ibrahim</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff3">Zhejiang-CAS Application Center for Geoinformatics, Jiaxing, 314100 China</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32288762</idno><idno type="pmc">7104600</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104600</idno><idno type="RBID">PMC:7104600</idno><idno type="doi">10.1007/s11430-012-4479-z</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000464</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000464</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study</title><author><name sortKey="Hu, Bisong" sort="Hu, Bisong" uniqKey="Hu B" first="Bisong" last="Hu">Bisong Hu</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.419897.a</institution-id><institution-id institution-id-type="ISNI">000000040369313X</institution-id><institution>Geography and Environment Department,</institution><institution>Jiangxi Normal University/Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education,</institution></institution-wrap>Nanchang, 330022 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation></author><author><name sortKey="Gong, Jianhua" sort="Gong, Jianhua" uniqKey="Gong J" first="Jianhua" last="Gong">Jianhua Gong</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff3">Zhejiang-CAS Application Center for Geoinformatics, Jiaxing, 314100 China</nlm:aff></affiliation></author><author><name sortKey="Zhou, Jieping" sort="Zhou, Jieping" uniqKey="Zhou J" first="Jieping" last="Zhou">Jieping Zhou</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff3">Zhejiang-CAS Application Center for Geoinformatics, Jiaxing, 314100 China</nlm:aff></affiliation></author><author><name sortKey="Sun, Jun" sort="Sun, Jun" uniqKey="Sun J" first="Jun" last="Sun">Jun Sun</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation></author><author><name sortKey="Yang, Liyang" sort="Yang, Liyang" uniqKey="Yang L" first="Liyang" last="Yang">Liyang Yang</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation></author><author><name sortKey="Xia, Yu" sort="Xia, Yu" uniqKey="Xia Y" first="Yu" last="Xia">Yu Xia</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.419897.a</institution-id><institution-id institution-id-type="ISNI">000000040369313X</institution-id><institution>Geography and Environment Department,</institution><institution>Jiangxi Normal University/Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education,</institution></institution-wrap>Nanchang, 330022 China</nlm:aff></affiliation></author><author><name sortKey="Ibrahim, Abdoul Nasser" sort="Ibrahim, Abdoul Nasser" uniqKey="Ibrahim A" first="Abdoul Nasser" last="Ibrahim">Abdoul Nasser Ibrahim</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff3">Zhejiang-CAS Application Center for Geoinformatics, Jiaxing, 314100 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="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread network between regions, the epidemic spread mechanism of virus input and output was explored based on individuals and spatial regions. Three typical spatial information parameters including working unit/address, onset location and reporting unit were selected and SARS epidemic spread in-out flow in Beijing was defined based on the SARS epidemiological investigation data in China from 2002 to 2003 while its epidemiological characteristics were discussed. Furthermore, by the methods of spatial-temporal statistical analysis and network characteristic analysis, spatial-temporal high-risk hotspots and network structure characteristics of Beijing outer in-out flow were explored, and spatial autocorrelation/heterogeneity, spatial-temporal evolutive rules and structure characteristics of the spread network of Beijing inner in-out flow were comprehensively analyzed. The results show that (1) The outer input flow of SARS epidemic in Beijing concentrated on Shanxi and Guangdong provinces, but the outer output flow was disperse and mainly includes several north provinces such as Guangdong and Shandong. And the control measurement should focus on the early and interim progress of SARS breakout. (2) The inner output cases had significant positive autocorrelative characteristics in the whole studied region, and the high-risk population was young and middle-aged people with ages from 20 to 60 and occupations of medicine and civilian labourer. (3) The downtown districts were main high-risk hotspots of SARS epidemic in Beijing, the northwest suburban districts/counties were secondary high-risk hotspots, and northeast suburban areas were relatively safe. (4) The district/county nodes in inner spread network showed small-world characteristics and information/material flow had notable heterogeneity. The suburban Tongzhou and Changping districts were the underlying high-risk regions, and several suburban districts such as Shunyi and Huairou were the relatively low-risk safe regions as they carried out minority information/material flow. The exploration and analysis based on epidemic spread in-out flow help better detect and discover the potential spatial-temporal evolutive rules and characteristics of SARS epidemic, and provide a more effective theoretical basis for emergency/control measurements and decision-making.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Dye, C" uniqKey="Dye C">C. Dye</name></author><author><name sortKey="Gay, N" uniqKey="Gay N">N. Gay</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Shi, Y L" uniqKey="Shi Y">Y. L. Shi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jia, N" uniqKey="Jia N">N. Jia</name></author><author><name sortKey="Tsui, L" uniqKey="Tsui L">L. Tsui</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Anderson, R M" uniqKey="Anderson R">R. M. 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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>Springer Berlin Heidelberg</publisher-name><publisher-loc>Berlin/Heidelberg</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32288762</article-id><article-id pub-id-type="pmc">7104600</article-id><article-id pub-id-type="publisher-id">4479</article-id><article-id pub-id-type="doi">10.1007/s11430-012-4479-z</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Paper</subject></subj-group></article-categories><title-group><article-title>Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Hu</surname><given-names>BiSong</given-names></name><xref ref-type="aff" rid="Aff1">1</xref><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author" corresp="yes"><name><surname>Gong</surname><given-names>JianHua</given-names></name><address><email>jhgong@irsa.ac.cn</email></address><xref ref-type="aff" rid="Aff2">2</xref><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Zhou</surname><given-names>JiePing</given-names></name><xref ref-type="aff" rid="Aff2">2</xref><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Sun</surname><given-names>Jun</given-names></name><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Yang</surname><given-names>LiYang</given-names></name><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Xia</surname><given-names>Yu</given-names></name><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Ibrahim</surname><given-names>Abdoul Nasser</given-names></name><xref ref-type="aff" rid="Aff2">2</xref><xref ref-type="aff" rid="Aff3">3</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="GRID">grid.419897.a</institution-id><institution-id institution-id-type="ISNI">000000040369313X</institution-id><institution>Geography and Environment Department,</institution><institution>Jiangxi Normal University/Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education,</institution></institution-wrap>Nanchang, 330022 China</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="GRID">grid.458443.a</institution-id><institution-id institution-id-type="ISNI">0000 0001 0433 6474</institution-id><institution>Institute of Remote Sensing Applications,</institution><institution>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science,</institution></institution-wrap>Beijing, 100101 China</aff><aff id="Aff3"><label>3</label>Zhejiang-CAS Application Center for Geoinformatics, Jiaxing, 314100 China</aff></contrib-group><pub-date pub-type="epub"><day>27</day><month>10</month><year>2012</year></pub-date><pub-date pub-type="ppub"><year>2013</year></pub-date><volume>56</volume><issue>8</issue><fpage>1380</fpage><lpage>1397</lpage><history><date date-type="received"><day>27</day><month>2</month><year>2012</year></date><date date-type="accepted"><day>27</day><month>8</month><year>2012</year></date></history><permissions><copyright-statement>© Science China Press and Springer-Verlag Berlin Heidelberg 2012</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>For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread network between regions, the epidemic spread mechanism of virus input and output was explored based on individuals and spatial regions. Three typical spatial information parameters including working unit/address, onset location and reporting unit were selected and SARS epidemic spread in-out flow in Beijing was defined based on the SARS epidemiological investigation data in China from 2002 to 2003 while its epidemiological characteristics were discussed. Furthermore, by the methods of spatial-temporal statistical analysis and network characteristic analysis, spatial-temporal high-risk hotspots and network structure characteristics of Beijing outer in-out flow were explored, and spatial autocorrelation/heterogeneity, spatial-temporal evolutive rules and structure characteristics of the spread network of Beijing inner in-out flow were comprehensively analyzed. The results show that (1) The outer input flow of SARS epidemic in Beijing concentrated on Shanxi and Guangdong provinces, but the outer output flow was disperse and mainly includes several north provinces such as Guangdong and Shandong. And the control measurement should focus on the early and interim progress of SARS breakout. (2) The inner output cases had significant positive autocorrelative characteristics in the whole studied region, and the high-risk population was young and middle-aged people with ages from 20 to 60 and occupations of medicine and civilian labourer. (3) The downtown districts were main high-risk hotspots of SARS epidemic in Beijing, the northwest suburban districts/counties were secondary high-risk hotspots, and northeast suburban areas were relatively safe. (4) The district/county nodes in inner spread network showed small-world characteristics and information/material flow had notable heterogeneity. The suburban Tongzhou and Changping districts were the underlying high-risk regions, and several suburban districts such as Shunyi and Huairou were the relatively low-risk safe regions as they carried out minority information/material flow. The exploration and analysis based on epidemic spread in-out flow help better detect and discover the potential spatial-temporal evolutive rules and characteristics of SARS epidemic, and provide a more effective theoretical basis for emergency/control measurements and decision-making.</p></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>in-out flow</kwd><kwd>SARS</kwd><kwd>Beijing</kwd><kwd>epidemic spread network</kwd><kwd>spatial-temporal characteristics</kwd><kwd>control measurement</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 2013</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>Dye</surname><given-names>C.</given-names></name><name><surname>Gay</surname><given-names>N.</given-names></name></person-group><article-title>Epidemiology modeling the SARS epidemic</article-title><source>Science</source><year>2003</year><volume>300</volume><fpage>1884</fpage><lpage>1885</lpage><pub-id pub-id-type="doi">10.1126/science.1086925</pub-id><pub-id pub-id-type="pmid">12766208</pub-id></element-citation></ref><ref id="CR2"><label>2</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Shi</surname><given-names>Y. 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