Modelling input-output flows of severe acute respiratory syndrome in mainland China
Identifieur interne : 000691 ( Pmc/Checkpoint ); précédent : 000690; suivant : 000692Modelling input-output flows of severe acute respiratory syndrome in mainland China
Auteurs : Li Wang [République populaire de Chine] ; Jinfeng Wang [République populaire de Chine] ; Chengdong Xu [République populaire de Chine] ; Tiejun Liu [République populaire de Chine]Source :
- BMC Public Health [ 1471-2458 ] ; 2016.
Abstract
Severe acute respiratory syndrome (SARS) originated in China in 2002, and it spread to 26 provinces in mainland China and 32 countries across five continents in a matter of months. This outbreak resulted in 774 deaths. However, the spatial features and potential determinants of SARS input-output flows remain unclear.
We used an adjusted spatial interaction model to examine the spatial effects and potential factors associated with SARS input-output flows.
The presence of origin-based spatial dependence positively affected SARS input-output flows from the neighbours of the origin regions. Two components of the input-output flows, migrant and hospitalization flows, exhibited distinctive features. The origin-based and destination-based spatial dependence positively affected migrant flows (i.e., due to those seeking jobs) from the neighbours of origin and destination locations. Similarly, the destination-based spatial dependence also positively affected hospitalization flows (i.e., due to those seeking treatment) from the neighbours of destination regions. However, the origin-to-destination based spatial dependence negatively affected hospitalisation flows from the neighbours of origin-to-destination regions. The direct effects accounted for 78 % of the SARS input-output flows, which was 3.56-fold greater than the indirect effects. Differences in regional income drove the SARS input-output flows. Therefore, urban income had a positive effect, whereas rural income had a negative effect. Total interregional flows increased by 3.54 % with a 1 % increase in urban income, and intraregional flows increased by 8.35 %. In contrast, the total interregional flows decreased by 3.38 % with a 1 % increase in rural income, and intraregional flows declined by 2.29 %. Railway capacity, per person gross domestic product (PGDP), urban rate and the law of distance decay also affected the input-output flows.
Our results confirm that the SARS input-output flows presented significant geographic spatial heterogeneity and spatial effects. Income differences were the major cause of the flows between pairs of regions. Railway capacity, PGDP, and urban rate also played important roles. These findings provide valuable information for the Chinese government to control the future spread of nationwide epidemics.
The online version of this article (doi:10.1186/s12889-016-2867-6) contains supplementary material, which is available to authorized users.
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DOI: 10.1186/s12889-016-2867-6
PubMed: 26924026
PubMed Central: 4770707
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institution-id-type="ISNI">0000000417978419</institution-id><institution>University of Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100049 China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Beijing</wicri:regionArea></affiliation></author><author><name sortKey="Wang, Jinfeng" sort="Wang, Jinfeng" uniqKey="Wang J" first="Jinfeng" last="Wang">Jinfeng Wang</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.424975.9</institution-id><institution-id institution-id-type="ISNI">0000000086158685</institution-id><institution>LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100101 China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Beijing</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff 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xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Jiangsu</wicri:regionArea></affiliation></author><author><name sortKey="Liu, Tiejun" sort="Liu, Tiejun" uniqKey="Liu T" first="Tiejun" last="Liu">Tiejun Liu</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.424975.9</institution-id><institution-id institution-id-type="ISNI">0000000086158685</institution-id><institution>LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100101 China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Beijing</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.410726.6</institution-id><institution-id institution-id-type="ISNI">0000000417978419</institution-id><institution>University of Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100049 China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Beijing</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC Public Health</title><idno type="eISSN">1471-2458</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p id="Par1">Severe acute respiratory syndrome (SARS) originated in China in 2002, and it spread to 26 provinces in mainland China and 32 countries across five continents in a matter of months. This outbreak resulted in 774 deaths. However, the spatial features and potential determinants of SARS input-output flows remain unclear.</p></sec><sec><title>Methods</title><p id="Par2">We used an adjusted spatial interaction model to examine the spatial effects and potential factors associated with SARS input-output flows.</p></sec><sec><title>Results</title><p id="Par3">The presence of origin-based spatial dependence positively affected SARS input-output flows from the neighbours of the origin regions. Two components of the input-output flows, migrant and hospitalization flows, exhibited distinctive features. The origin-based and destination-based spatial dependence positively affected migrant flows (i.e., due to those seeking jobs) from the neighbours of origin and destination locations. Similarly, the destination-based spatial dependence also positively affected hospitalization flows (i.e., due to those seeking treatment) from the neighbours of destination regions. However, the origin-to-destination based spatial dependence negatively affected hospitalisation flows from the neighbours of origin-to-destination regions. The direct effects accounted for 78 % of the SARS input-output flows, which was 3.56-fold greater than the indirect effects. Differences in regional income drove the SARS input-output flows. Therefore, urban income had a positive effect, whereas rural income had a negative effect. Total interregional flows increased by 3.54 % with a 1 % increase in urban income, and intraregional flows increased by 8.35 %. In contrast, the total interregional flows decreased by 3.38 % with a 1 % increase in rural income, and intraregional flows declined by 2.29 %. Railway capacity, per person gross domestic product (PGDP), urban rate and the law of distance decay also affected the input-output flows.</p></sec><sec><title>Conclusions</title><p id="Par4">Our results confirm that the SARS input-output flows presented significant geographic spatial heterogeneity and spatial effects. Income differences were the major cause of the flows between pairs of regions. Railway capacity, PGDP, and urban rate also played important roles. These findings provide valuable information for the Chinese government to control the future spread of nationwide epidemics.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s12889-016-2867-6) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Frenk, J" uniqKey="Frenk J">J Frenk</name></author><author><name sortKey="G Mez Dantes, O" uniqKey="G Mez Dantes O">O Gómez-Dantés</name></author><author><name sortKey="Knaul, Fm" uniqKey="Knaul F">FM Knaul</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Morens, Dm" uniqKey="Morens D">DM Morens</name></author><author><name sortKey="Folkers, Gk" uniqKey="Folkers G">GK Folkers</name></author><author><name sortKey="Fauci, As" uniqKey="Fauci A">AS 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<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Public Health</journal-id><journal-id journal-id-type="iso-abbrev">BMC Public Health</journal-id><journal-title-group><journal-title>BMC Public Health</journal-title></journal-title-group><issn pub-type="epub">1471-2458</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">26924026</article-id><article-id pub-id-type="pmc">4770707</article-id><article-id pub-id-type="publisher-id">2867</article-id><article-id pub-id-type="doi">10.1186/s12889-016-2867-6</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>Modelling input-output flows of severe acute respiratory syndrome in mainland China</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Wang</surname><given-names>Li</given-names></name><address><email>wangli@lreis.ac.cn</email></address><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>Wang</surname><given-names>Jinfeng</given-names></name><address><email>wangjf@Lreis.ac.cn</email></address><xref ref-type="aff" rid="Aff1">1</xref><xref ref-type="aff" rid="Aff3">3</xref><xref ref-type="aff" rid="Aff4">4</xref></contrib><contrib contrib-type="author"><name><surname>Xu</surname><given-names>Chengdong</given-names></name><address><email>xucd@lreis.ac.cn</email></address><xref ref-type="aff" rid="Aff1">1</xref><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Liu</surname><given-names>Tiejun</given-names></name><address><email>liutj@lreis.ac.cn</email></address><xref ref-type="aff" rid="Aff1">1</xref><xref ref-type="aff" rid="Aff2">2</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="GRID">grid.424975.9</institution-id><institution-id institution-id-type="ISNI">0000000086158685</institution-id><institution>LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100101 China</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="GRID">grid.410726.6</institution-id><institution-id institution-id-type="ISNI">0000000417978419</institution-id><institution>University of Chinese Academy of Sciences,</institution></institution-wrap>Beijing, 100049 China</aff><aff id="Aff3"><label>3</label><institution-wrap><institution-id institution-id-type="GRID">grid.198530.6</institution-id><institution-id institution-id-type="ISNI">0000000088032373</institution-id><institution>Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention,</institution></institution-wrap>Jiangsu, China</aff><aff id="Aff4"><label>4</label>Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Jiangsu, China</aff></contrib-group><pub-date pub-type="epub"><day>29</day><month>2</month><year>2016</year></pub-date><pub-date pub-type="pmc-release"><day>29</day><month>2</month><year>2016</year></pub-date><pub-date pub-type="collection"><year>2016</year></pub-date><volume>16</volume><elocation-id>191</elocation-id><history><date date-type="received"><day>11</day><month>7</month><year>2015</year></date><date date-type="accepted"><day>15</day><month>2</month><year>2016</year></date></history><permissions><copyright-statement>© Wang et al. 2016</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold>This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p id="Par1">Severe acute respiratory syndrome (SARS) originated in China in 2002, and it spread to 26 provinces in mainland China and 32 countries across five continents in a matter of months. This outbreak resulted in 774 deaths. However, the spatial features and potential determinants of SARS input-output flows remain unclear.</p></sec><sec><title>Methods</title><p id="Par2">We used an adjusted spatial interaction model to examine the spatial effects and potential factors associated with SARS input-output flows.</p></sec><sec><title>Results</title><p id="Par3">The presence of origin-based spatial dependence positively affected SARS input-output flows from the neighbours of the origin regions. Two components of the input-output flows, migrant and hospitalization flows, exhibited distinctive features. The origin-based and destination-based spatial dependence positively affected migrant flows (i.e., due to those seeking jobs) from the neighbours of origin and destination locations. Similarly, the destination-based spatial dependence also positively affected hospitalization flows (i.e., due to those seeking treatment) from the neighbours of destination regions. However, the origin-to-destination based spatial dependence negatively affected hospitalisation flows from the neighbours of origin-to-destination regions. The direct effects accounted for 78 % of the SARS input-output flows, which was 3.56-fold greater than the indirect effects. Differences in regional income drove the SARS input-output flows. Therefore, urban income had a positive effect, whereas rural income had a negative effect. Total interregional flows increased by 3.54 % with a 1 % increase in urban income, and intraregional flows increased by 8.35 %. In contrast, the total interregional flows decreased by 3.38 % with a 1 % increase in rural income, and intraregional flows declined by 2.29 %. Railway capacity, per person gross domestic product (PGDP), urban rate and the law of distance decay also affected the input-output flows.</p></sec><sec><title>Conclusions</title><p id="Par4">Our results confirm that the SARS input-output flows presented significant geographic spatial heterogeneity and spatial effects. Income differences were the major cause of the flows between pairs of regions. Railway capacity, PGDP, and urban rate also played important roles. These findings provide valuable information for the Chinese government to control the future spread of nationwide epidemics.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s12889-016-2867-6) contains supplementary material, which is available to authorized users.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Input-output flows</kwd><kwd>Spatial interaction model</kwd><kwd>Spatial dependence</kwd><kwd>Spillover effects</kwd><kwd>Income difference</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100002855</institution-id><institution>Ministry of Science and Technology of the People's Republic of China (CN)</institution></institution-wrap></funding-source><award-id>2012CB955503</award-id><award-id>2012ZX10004-201</award-id><award-id>201202006</award-id><principal-award-recipient><name><surname>Wang</surname><given-names>Jinfeng</given-names></name></principal-award-recipient></award-group></funding-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100001809</institution-id><institution>National Natural Science Foundation of China (CN)</institution></institution-wrap></funding-source><award-id>41271404</award-id></award-group></funding-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100002858</institution-id><institution>China Postdoctoral Science Foundation (CN)</institution></institution-wrap></funding-source><award-id>2014 M550817</award-id><principal-award-recipient><name><surname>Xu</surname><given-names>Chengdong</given-names></name></principal-award-recipient></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2016</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Wang, Li" sort="Wang, Li" uniqKey="Wang L" first="Li" last="Wang">Li Wang</name></noRegion><name sortKey="Liu, Tiejun" sort="Liu, Tiejun" uniqKey="Liu T" first="Tiejun" last="Liu">Tiejun Liu</name><name sortKey="Liu, Tiejun" sort="Liu, Tiejun" uniqKey="Liu T" first="Tiejun" last="Liu">Tiejun Liu</name><name sortKey="Wang, Jinfeng" sort="Wang, Jinfeng" uniqKey="Wang J" first="Jinfeng" last="Wang">Jinfeng Wang</name><name sortKey="Wang, Jinfeng" sort="Wang, Jinfeng" uniqKey="Wang J" first="Jinfeng" last="Wang">Jinfeng Wang</name><name sortKey="Wang, Jinfeng" sort="Wang, Jinfeng" uniqKey="Wang J" first="Jinfeng" last="Wang">Jinfeng Wang</name><name sortKey="Wang, Li" sort="Wang, Li" uniqKey="Wang L" first="Li" last="Wang">Li Wang</name><name sortKey="Xu, Chengdong" sort="Xu, Chengdong" uniqKey="Xu C" first="Chengdong" last="Xu">Chengdong Xu</name><name sortKey="Xu, Chengdong" sort="Xu, Chengdong" uniqKey="Xu C" first="Chengdong" last="Xu">Chengdong Xu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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