Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study
Identifieur interne : 000275 ( Istex/Corpus ); précédent : 000274; suivant : 000276Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study
Auteurs : Bisong Hu ; Jianhua Gong ; Jieping Zhou ; Jun Sun ; Liyang Yang ; Yu Xia ; Abdoul Nasser IbrahimSource :
- Science China Earth Sciences [ 1674-7313 ] ; 2013-08-01.
English descriptors
- KwdEn :
Abstract
Abstract: 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.
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DOI: 10.1007/s11430-012-4479-z
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China Earth Sci.</title><idno type="ISSN">1674-7313</idno><idno type="eISSN">1869-1897</idno><imprint><publisher>Springer Berlin Heidelberg</publisher><pubPlace>Berlin/Heidelberg</pubPlace><date type="published" when="2013-08-01">2013-08-01</date><biblScope unit="volume">56</biblScope><biblScope unit="issue">8</biblScope><biblScope unit="page" from="1380">1380</biblScope><biblScope unit="page" to="1397">1397</biblScope></imprint><idno type="ISSN">1674-7313</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1674-7313</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Beijing</term><term>SARS</term><term>control measurement</term><term>epidemic spread network</term><term>in-out flow</term><term>spatial-temporal characteristics</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: 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.</div></front></TEI><istex><corpusName>springer-journals</corpusName><author><json:item><name>BiSong Hu</name><affiliations><json:string>Geography and Environment Department, Jiangxi Normal University/Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, 330022, Nanchang, China</json:string><json:string>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</json:string></affiliations></json:item><json:item><name>JianHua Gong</name><affiliations><json:string>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</json:string><json:string>Zhejiang-CAS Application Center for Geoinformatics, 314100, Jiaxing, China</json:string><json:string>E-mail: jhgong@irsa.ac.cn</json:string></affiliations></json:item><json:item><name>JiePing Zhou</name><affiliations><json:string>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</json:string><json:string>Zhejiang-CAS Application Center for Geoinformatics, 314100, Jiaxing, China</json:string></affiliations></json:item><json:item><name>Jun Sun</name><affiliations><json:string>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</json:string></affiliations></json:item><json:item><name>LiYang Yang</name><affiliations><json:string>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</json:string></affiliations></json:item><json:item><name>Yu Xia</name><affiliations><json:string>Geography and Environment Department, Jiangxi Normal University/Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, 330022, Nanchang, China</json:string></affiliations></json:item><json:item><name>Abdoul Nasser Ibrahim</name><affiliations><json:string>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</json:string><json:string>Zhejiang-CAS Application Center for Geoinformatics, 314100, Jiaxing, China</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>in-out flow</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>SARS</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Beijing</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>epidemic spread network</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>spatial-temporal characteristics</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>control measurement</value></json:item></subject><articleId><json:string>4479</json:string><json:string>s11430-012-4479-z</json:string></articleId><arkIstex>ark:/67375/VQC-XK6PBQ8L-V</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: 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. 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type="first">JiePing</forename><surname>Zhou</surname></persName><affiliation>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</affiliation><affiliation>Zhejiang-CAS Application Center for Geoinformatics, 314100, Jiaxing, China</affiliation></author><author xml:id="author-0003"><persName><forename type="first">Jun</forename><surname>Sun</surname></persName><affiliation>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</affiliation></author><author xml:id="author-0004"><persName><forename type="first">LiYang</forename><surname>Yang</surname></persName><affiliation>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</affiliation></author><author xml:id="author-0005"><persName><forename type="first">Yu</forename><surname>Xia</surname></persName><affiliation>Geography and Environment Department, Jiangxi Normal University/Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, 330022, Nanchang, China</affiliation></author><author xml:id="author-0006"><persName><forename type="first">Abdoul</forename><surname>Ibrahim</surname></persName><affiliation>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</affiliation><affiliation>Zhejiang-CAS Application Center for Geoinformatics, 314100, Jiaxing, China</affiliation></author><idno type="istex">31CA762D8E9FF88506AF5E1C9C325C7FBCC791AE</idno><idno type="ark">ark:/67375/VQC-XK6PBQ8L-V</idno><idno type="DOI">10.1007/s11430-012-4479-z</idno><idno type="article-id">4479</idno><idno type="article-id">s11430-012-4479-z</idno></analytic><monogr><title level="j">Science China Earth Sciences</title><title level="j" 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China Earth Sci.</title><idno type="pISSN">1674-7313</idno><idno type="eISSN">1869-1897</idno><idno type="journal-ID">true</idno><idno type="issue-article-count">17</idno><idno type="volume-issue-count">12</idno><imprint><publisher>Springer Berlin Heidelberg</publisher><pubPlace>Berlin/Heidelberg</pubPlace><date type="published" when="2013-08-01"></date><biblScope unit="volume">56</biblScope><biblScope unit="issue">8</biblScope><biblScope unit="page" from="1380">1380</biblScope><biblScope unit="page" to="1397">1397</biblScope></imprint></monogr></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2012-02-27</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Abstract: 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><textClass xml:lang="en"><keywords scheme="keyword"><list><head>Keywords</head><item><term>in-out flow</term></item><item><term>SARS</term></item><item><term>Beijing</term></item><item><term>epidemic spread network</term></item><item><term>spatial-temporal characteristics</term></item><item><term>control measurement</term></item></list></keywords></textClass><textClass><keywords scheme="Journal-Subject-Collection"><list><label>SC7</label><item><term>Earth and Environmental Science</term></item></list></keywords></textClass><textClass><keywords scheme="Journal-Subject-Group"><list><label>SCG</label><item><term>Earth Sciences</term></item><label>SCG00002</label><item><term>Earth Sciences, general</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2012-02-27">Created</change><change when="2013-08-01">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/ark:/67375/VQC-XK6PBQ8L-V/fulltext.txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="corpus springer-journals not found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//Springer-Verlag//DTD A++ V2.4//EN" URI="http://devel.springer.de/A++/V2.4/DTD/A++V2.4.dtd" name="istex:docType"></istex:docType><istex:document><Publisher><PublisherInfo><PublisherName>Springer Berlin Heidelberg</PublisherName><PublisherLocation>Berlin/Heidelberg</PublisherLocation><PublisherImprintName>Science China Press, co-published with Springer</PublisherImprintName></PublisherInfo><Journal OutputMedium="All"><JournalInfo JournalProductType="ArchiveJournal" NumberingStyle="Unnumbered"><JournalID>11430</JournalID><JournalPrintISSN>1674-7313</JournalPrintISSN><JournalElectronicISSN>1869-1897</JournalElectronicISSN><JournalTitle>Science China Earth Sciences</JournalTitle><JournalAbbreviatedTitle>Sci. China Earth Sci.</JournalAbbreviatedTitle><JournalSubjectGroup><JournalSubject Code="SCG" Type="Primary">Earth Sciences</JournalSubject><JournalSubject Code="SCG00002" Priority="1" Type="Secondary">Earth Sciences, general</JournalSubject><SubjectCollection Code="SC7">Earth and Environmental Science</SubjectCollection></JournalSubjectGroup></JournalInfo><Volume OutputMedium="All"><VolumeInfo TocLevels="0" VolumeType="Regular"><VolumeIDStart>56</VolumeIDStart><VolumeIDEnd>56</VolumeIDEnd><VolumeIssueCount>12</VolumeIssueCount></VolumeInfo><Issue IssueType="Regular" OutputMedium="All"><IssueInfo IssueType="Regular" TocLevels="0"><IssueIDStart>8</IssueIDStart><IssueIDEnd>8</IssueIDEnd><IssueArticleCount>17</IssueArticleCount><IssueHistory><OnlineDate><Year>2013</Year><Month>7</Month><Day>31</Day></OnlineDate><CoverDate><Year>2013</Year><Month>8</Month></CoverDate><PricelistYear>2013</PricelistYear></IssueHistory><IssueCopyright><CopyrightHolderName>Science China Press and Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2013</CopyrightYear></IssueCopyright></IssueInfo><Article ID="s11430-012-4479-z" OutputMedium="All"><ArticleInfo ArticleType="OriginalPaper" ContainsESM="No" Language="En" NumberingStyle="Unnumbered" TocLevels="0"><ArticleID>4479</ArticleID><ArticleDOI>10.1007/s11430-012-4479-z</ArticleDOI><ArticleSequenceNumber>10</ArticleSequenceNumber><ArticleTitle Language="En">Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study</ArticleTitle><ArticleCategory>Research Paper</ArticleCategory><ArticleFirstPage>1380</ArticleFirstPage><ArticleLastPage>1397</ArticleLastPage><ArticleHistory><RegistrationDate><Year>2012</Year><Month>10</Month><Day>27</Day></RegistrationDate><Received><Year>2012</Year><Month>2</Month><Day>27</Day></Received><Accepted><Year>2012</Year><Month>8</Month><Day>27</Day></Accepted><OnlineDate><Year>2012</Year><Month>10</Month><Day>27</Day></OnlineDate></ArticleHistory><ArticleCopyright><CopyrightHolderName>Science China Press and Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2012</CopyrightYear></ArticleCopyright><ArticleGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ArticleGrants></ArticleInfo><ArticleHeader><AuthorGroup><Author AffiliationIDS="Aff1 Aff2"><AuthorName DisplayOrder="Western"><GivenName>BiSong</GivenName><FamilyName>Hu</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff2 Aff3" CorrespondingAffiliationID="Aff2"><AuthorName DisplayOrder="Western"><GivenName>JianHua</GivenName><FamilyName>Gong</FamilyName></AuthorName><Contact><Email>jhgong@irsa.ac.cn</Email></Contact></Author><Author AffiliationIDS="Aff2 Aff3"><AuthorName DisplayOrder="Western"><GivenName>JiePing</GivenName><FamilyName>Zhou</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>Jun</GivenName><FamilyName>Sun</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>LiYang</GivenName><FamilyName>Yang</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Yu</GivenName><FamilyName>Xia</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff2 Aff3"><AuthorName DisplayOrder="Western"><GivenName>Abdoul</GivenName><GivenName>Nasser</GivenName><FamilyName>Ibrahim</FamilyName></AuthorName></Author><Affiliation ID="Aff1"><OrgDivision>Geography and Environment Department</OrgDivision><OrgName>Jiangxi Normal University/Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education</OrgName><OrgAddress><City>Nanchang</City><Postcode>330022</Postcode><Country Code="CN">China</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgDivision>Institute of Remote Sensing Applications</OrgDivision><OrgName>Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science</OrgName><OrgAddress><City>Beijing</City><Postcode>100101</Postcode><Country Code="CN">China</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>Zhejiang-CAS Application Center for Geoinformatics</OrgName><OrgAddress><City>Jiaxing</City><Postcode>314100</Postcode><Country Code="CN">China</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>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.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>in-out flow</Keyword><Keyword>SARS</Keyword><Keyword>Beijing</Keyword><Keyword>epidemic spread network</Keyword><Keyword>spatial-temporal characteristics</Keyword><Keyword>control measurement</Keyword></KeywordGroup></ArticleHeader><NoBody></NoBody></Article></Issue></Volume></Journal></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study</title></titleInfo><name type="personal"><namePart type="given">BiSong</namePart><namePart type="family">Hu</namePart><affiliation>Geography and Environment Department, Jiangxi Normal University/Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, 330022, Nanchang, China</affiliation><affiliation>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal" displayLabel="corresp"><namePart type="given">JianHua</namePart><namePart type="family">Gong</namePart><affiliation>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</affiliation><affiliation>Zhejiang-CAS Application Center for Geoinformatics, 314100, Jiaxing, China</affiliation><affiliation>E-mail: jhgong@irsa.ac.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">JiePing</namePart><namePart type="family">Zhou</namePart><affiliation>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</affiliation><affiliation>Zhejiang-CAS Application Center for Geoinformatics, 314100, Jiaxing, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jun</namePart><namePart type="family">Sun</namePart><affiliation>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">LiYang</namePart><namePart type="family">Yang</namePart><affiliation>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Yu</namePart><namePart type="family">Xia</namePart><affiliation>Geography and Environment Department, Jiangxi Normal University/Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, 330022, Nanchang, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Abdoul</namePart><namePart type="given">Nasser</namePart><namePart type="family">Ibrahim</namePart><affiliation>Institute of Remote Sensing Applications, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, 100101, Beijing, China</affiliation><affiliation>Zhejiang-CAS Application Center for Geoinformatics, 314100, Jiaxing, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin/Heidelberg</placeTerm></place><dateCreated encoding="w3cdtf">2012-02-27</dateCreated><dateIssued encoding="w3cdtf">2013-08-01</dateIssued><copyrightDate encoding="w3cdtf">2012</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: 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.</abstract><note>Research Paper</note><subject lang="en"><genre>Keywords</genre><topic>in-out flow</topic><topic>SARS</topic><topic>Beijing</topic><topic>epidemic spread network</topic><topic>spatial-temporal characteristics</topic><topic>control measurement</topic></subject><relatedItem type="host"><titleInfo><title>Science China Earth Sciences</title></titleInfo><titleInfo type="abbreviated"><title>Sci. 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