Roles of Different Transport Modes in the Spatial Spread of the 2009 Influenza A(H1N1) Pandemic in Mainland China
Identifieur interne : 000043 ( Pmc/Checkpoint ); précédent : 000042; suivant : 000044Roles of Different Transport Modes in the Spatial Spread of the 2009 Influenza A(H1N1) Pandemic in Mainland China
Auteurs : Jun Cai [République populaire de Chine] ; Bo Xu [République populaire de Chine] ; Karen Kie Yan Chan [République populaire de Chine] ; Xueying Zhang ; Bing Zhang ; Ziyue Chen ; Bing Xu [République populaire de Chine]Source :
- International Journal of Environmental Research and Public Health [ 1661-7827 ] ; 2019.
Abstract
There is increasing concern about another influenza pandemic in China. However, the understanding of the roles of transport modes in the 2009 influenza A(H1N1) pandemic spread across mainland China is limited. Herein, we collected 127,797 laboratory-confirmed cases of influenza A(H1N1)pdm09 in mainland China from May 2009 to April 2010. Arrival days and peak days were calculated for all 340 prefectures to characterize the dissemination patterns of the pandemic. We first evaluated the effects of airports and railway stations on arrival days and peak days, and then we applied quantile regressions to quantify the relationships between arrival days and air, rail, and road travel. Our results showed that early arrival of the virus was not associated with an early incidence peak. Airports and railway stations in prefectures significantly advanced arrival days but had no significant impact on peak days. The pandemic spread across mainland China from the southeast to the northwest in two phases that were split at approximately 1 August 2009. Both air and road travel played a significant role in accelerating the spread during phases I and II, but rail travel was only significant during phase II. In conclusion, in addition to air and road travel, rail travel also played a significant role in accelerating influenza A(H1N1)pdm09 spread between prefectures. Establishing a multiscale mobility network that considers the competitive advantage of rail travel for mid to long distances is essential for understanding the influenza pandemic transmission in China.
Url:
DOI: 10.3390/ijerph16020222
PubMed: 30646629
PubMed Central: 6352022
Affiliations:
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type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Xu, Bo" sort="Xu, Bo" uniqKey="Xu B" first="Bo" last="Xu">Bo Xu</name><affiliation><nlm:aff id="af1-ijerph-16-00222">Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;<email>cai-j12@mails.tsinghua.edu.cn</email> (J.C.);<email>xu-b15@mails.tsinghua.edu.cn</email> (B.X.);<email>cqe15@mails.tsinghua.edu.cn</email> (K.K.Y.C.)</nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af2-ijerph-16-00222">Joint Center for Global Change Studies, Beijing 100875, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Joint Center for Global Change Studies, Beijing 100875</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Chan, Karen Kie Yan" sort="Chan, Karen Kie Yan" uniqKey="Chan K" first="Karen Kie Yan" last="Chan">Karen Kie Yan Chan</name><affiliation><nlm:aff id="af1-ijerph-16-00222">Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;<email>cai-j12@mails.tsinghua.edu.cn</email> (J.C.);<email>xu-b15@mails.tsinghua.edu.cn</email> (B.X.);<email>cqe15@mails.tsinghua.edu.cn</email> (K.K.Y.C.)</nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af2-ijerph-16-00222">Joint Center for Global Change Studies, Beijing 100875, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Joint Center for Global Change Studies, Beijing 100875</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Zhang, Xueying" sort="Zhang, Xueying" uniqKey="Zhang X" first="Xueying" last="Zhang">Xueying Zhang</name><affiliation><nlm:aff id="af3-ijerph-16-00222">Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;<email>xueying.zhang@mssm.edu</email></nlm:aff></affiliation></author><author><name sortKey="Zhang, Bing" sort="Zhang, Bing" uniqKey="Zhang B" first="Bing" last="Zhang">Bing Zhang</name><affiliation><nlm:aff id="af4-ijerph-16-00222">School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China;<email>zhangbing4502431@outlook.com</email></nlm:aff></affiliation></author><author><name sortKey="Chen, Ziyue" sort="Chen, Ziyue" uniqKey="Chen Z" first="Ziyue" last="Chen">Ziyue Chen</name><affiliation><nlm:aff id="af5-ijerph-16-00222">State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China;<email>zychen@bnu.edu.cn</email></nlm:aff></affiliation></author><author><name sortKey="Xu, Bing" sort="Xu, Bing" uniqKey="Xu B" first="Bing" last="Xu">Bing Xu</name><affiliation><nlm:aff 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type="doi">10.3390/ijerph16020222</idno><date when="2019">2019</date><idno type="wicri:Area/Pmc/Corpus">000B00</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000B00</idno><idno type="wicri:Area/Pmc/Curation">000B00</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000B00</idno><idno type="wicri:Area/Pmc/Checkpoint">000043</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000043</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Roles of Different Transport Modes in the Spatial Spread of the 2009 Influenza A(H1N1) Pandemic in Mainland China</title><author><name sortKey="Cai, Jun" sort="Cai, Jun" uniqKey="Cai J" first="Jun" last="Cai">Jun Cai</name><affiliation><nlm:aff id="af1-ijerph-16-00222">Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;<email>cai-j12@mails.tsinghua.edu.cn</email> (J.C.);<email>xu-b15@mails.tsinghua.edu.cn</email> (B.X.);<email>cqe15@mails.tsinghua.edu.cn</email> (K.K.Y.C.)</nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af2-ijerph-16-00222">Joint Center for Global Change Studies, Beijing 100875, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Joint Center for Global Change Studies, Beijing 100875</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Xu, Bo" sort="Xu, Bo" uniqKey="Xu B" first="Bo" last="Xu">Bo Xu</name><affiliation><nlm:aff id="af1-ijerph-16-00222">Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;<email>cai-j12@mails.tsinghua.edu.cn</email> (J.C.);<email>xu-b15@mails.tsinghua.edu.cn</email> (B.X.);<email>cqe15@mails.tsinghua.edu.cn</email> (K.K.Y.C.)</nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af2-ijerph-16-00222">Joint Center for Global Change Studies, Beijing 100875, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Joint Center for Global Change Studies, Beijing 100875</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Chan, Karen Kie Yan" sort="Chan, Karen Kie Yan" uniqKey="Chan K" first="Karen Kie Yan" last="Chan">Karen Kie Yan Chan</name><affiliation><nlm:aff id="af1-ijerph-16-00222">Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;<email>cai-j12@mails.tsinghua.edu.cn</email> (J.C.);<email>xu-b15@mails.tsinghua.edu.cn</email> (B.X.);<email>cqe15@mails.tsinghua.edu.cn</email> (K.K.Y.C.)</nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af2-ijerph-16-00222">Joint Center for Global Change Studies, Beijing 100875, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Joint Center for Global Change Studies, Beijing 100875</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Zhang, Xueying" sort="Zhang, Xueying" uniqKey="Zhang X" first="Xueying" last="Zhang">Xueying Zhang</name><affiliation><nlm:aff id="af3-ijerph-16-00222">Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;<email>xueying.zhang@mssm.edu</email></nlm:aff></affiliation></author><author><name sortKey="Zhang, Bing" sort="Zhang, Bing" uniqKey="Zhang B" first="Bing" last="Zhang">Bing Zhang</name><affiliation><nlm:aff id="af4-ijerph-16-00222">School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China;<email>zhangbing4502431@outlook.com</email></nlm:aff></affiliation></author><author><name sortKey="Chen, Ziyue" sort="Chen, Ziyue" uniqKey="Chen Z" first="Ziyue" last="Chen">Ziyue Chen</name><affiliation><nlm:aff id="af5-ijerph-16-00222">State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China;<email>zychen@bnu.edu.cn</email></nlm:aff></affiliation></author><author><name sortKey="Xu, Bing" sort="Xu, Bing" uniqKey="Xu B" first="Bing" last="Xu">Bing Xu</name><affiliation><nlm:aff id="af1-ijerph-16-00222">Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;<email>cai-j12@mails.tsinghua.edu.cn</email> (J.C.);<email>xu-b15@mails.tsinghua.edu.cn</email> (B.X.);<email>cqe15@mails.tsinghua.edu.cn</email> (K.K.Y.C.)</nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af2-ijerph-16-00222">Joint Center for Global Change Studies, Beijing 100875, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Joint Center for Global Change Studies, Beijing 100875</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author></analytic><series><title level="j">International Journal of Environmental Research and Public Health</title><idno type="ISSN">1661-7827</idno><idno type="eISSN">1660-4601</idno><imprint><date when="2019">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>There is increasing concern about another influenza pandemic in China. However, the understanding of the roles of transport modes in the 2009 influenza A(H1N1) pandemic spread across mainland China is limited. Herein, we collected 127,797 laboratory-confirmed cases of influenza A(H1N1)pdm09 in mainland China from May 2009 to April 2010. Arrival days and peak days were calculated for all 340 prefectures to characterize the dissemination patterns of the pandemic. We first evaluated the effects of airports and railway stations on arrival days and peak days, and then we applied quantile regressions to quantify the relationships between arrival days and air, rail, and road travel. Our results showed that early arrival of the virus was not associated with an early incidence peak. Airports and railway stations in prefectures significantly advanced arrival days but had no significant impact on peak days. The pandemic spread across mainland China from the southeast to the northwest in two phases that were split at approximately 1 August 2009. Both air and road travel played a significant role in accelerating the spread during phases I and II, but rail travel was only significant during phase II. In conclusion, in addition to air and road travel, rail travel also played a significant role in accelerating influenza A(H1N1)pdm09 spread between prefectures. Establishing a multiscale mobility network that considers the competitive advantage of rail travel for mid to long distances is essential for understanding the influenza pandemic transmission in China.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Dawood, F S" uniqKey="Dawood F">F.S. Dawood</name></author><author><name sortKey="Iuliano, A D" uniqKey="Iuliano A">A.D. Iuliano</name></author><author><name sortKey="Reed, C" uniqKey="Reed C">C. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Int J Environ Res Public Health</journal-id><journal-id journal-id-type="iso-abbrev">Int J Environ Res Public Health</journal-id><journal-id journal-id-type="publisher-id">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type="ppub">1661-7827</issn><issn pub-type="epub">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">30646629</article-id><article-id pub-id-type="pmc">6352022</article-id><article-id pub-id-type="doi">10.3390/ijerph16020222</article-id><article-id pub-id-type="publisher-id">ijerph-16-00222</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Roles of Different Transport Modes in the Spatial Spread of the 2009 Influenza A(H1N1) Pandemic in Mainland China</article-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0001-9495-1226</contrib-id><name><surname>Cai</surname><given-names>Jun</given-names></name><xref ref-type="aff" rid="af1-ijerph-16-00222">1</xref><xref ref-type="aff" rid="af2-ijerph-16-00222">2</xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0002-1920-5829</contrib-id><name><surname>Xu</surname><given-names>Bo</given-names></name><xref ref-type="aff" rid="af1-ijerph-16-00222">1</xref><xref ref-type="aff" rid="af2-ijerph-16-00222">2</xref></contrib><contrib contrib-type="author"><name><surname>Chan</surname><given-names>Karen Kie Yan</given-names></name><xref ref-type="aff" rid="af1-ijerph-16-00222">1</xref><xref ref-type="aff" rid="af2-ijerph-16-00222">2</xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0003-0806-3324</contrib-id><name><surname>Zhang</surname><given-names>Xueying</given-names></name><xref ref-type="aff" rid="af3-ijerph-16-00222">3</xref></contrib><contrib contrib-type="author"><name><surname>Zhang</surname><given-names>Bing</given-names></name><xref ref-type="aff" rid="af4-ijerph-16-00222">4</xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0003-4103-3792</contrib-id><name><surname>Chen</surname><given-names>Ziyue</given-names></name><xref ref-type="aff" rid="af5-ijerph-16-00222">5</xref></contrib><contrib contrib-type="author"><name><surname>Xu</surname><given-names>Bing</given-names></name><xref ref-type="aff" rid="af1-ijerph-16-00222">1</xref><xref ref-type="aff" rid="af2-ijerph-16-00222">2</xref><xref rid="c1-ijerph-16-00222" ref-type="corresp">*</xref></contrib></contrib-group><aff id="af1-ijerph-16-00222"><label>1</label>Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;<email>cai-j12@mails.tsinghua.edu.cn</email> (J.C.);<email>xu-b15@mails.tsinghua.edu.cn</email> (B.X.);<email>cqe15@mails.tsinghua.edu.cn</email> (K.K.Y.C.)</aff><aff id="af2-ijerph-16-00222"><label>2</label>Joint Center for Global Change Studies, Beijing 100875, China</aff><aff id="af3-ijerph-16-00222"><label>3</label>Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;<email>xueying.zhang@mssm.edu</email></aff><aff id="af4-ijerph-16-00222"><label>4</label>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China;<email>zhangbing4502431@outlook.com</email></aff><aff id="af5-ijerph-16-00222"><label>5</label>State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China;<email>zychen@bnu.edu.cn</email></aff><author-notes><corresp id="c1-ijerph-16-00222"><label>*</label>Correspondence: <email>bingxu@tsinghua.edu.cn</email>; Tel.: +86-010-6279-0189</corresp></author-notes><pub-date pub-type="epub"><day>14</day><month>1</month><year>2019</year></pub-date><pub-date pub-type="ppub"><month>1</month><year>2019</year></pub-date><volume>16</volume><issue>2</issue><elocation-id>222</elocation-id><history><date date-type="received"><day>29</day><month>11</month><year>2018</year></date><date date-type="accepted"><day>09</day><month>1</month><year>2019</year></date></history><permissions><copyright-statement>© 2019 by the authors.</copyright-statement><copyright-year>2019</copyright-year><license license-type="open-access"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) 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>).</license-p></license></permissions><abstract><p>There is increasing concern about another influenza pandemic in China. However, the understanding of the roles of transport modes in the 2009 influenza A(H1N1) pandemic spread across mainland China is limited. Herein, we collected 127,797 laboratory-confirmed cases of influenza A(H1N1)pdm09 in mainland China from May 2009 to April 2010. Arrival days and peak days were calculated for all 340 prefectures to characterize the dissemination patterns of the pandemic. We first evaluated the effects of airports and railway stations on arrival days and peak days, and then we applied quantile regressions to quantify the relationships between arrival days and air, rail, and road travel. Our results showed that early arrival of the virus was not associated with an early incidence peak. Airports and railway stations in prefectures significantly advanced arrival days but had no significant impact on peak days. The pandemic spread across mainland China from the southeast to the northwest in two phases that were split at approximately 1 August 2009. Both air and road travel played a significant role in accelerating the spread during phases I and II, but rail travel was only significant during phase II. In conclusion, in addition to air and road travel, rail travel also played a significant role in accelerating influenza A(H1N1)pdm09 spread between prefectures. Establishing a multiscale mobility network that considers the competitive advantage of rail travel for mid to long distances is essential for understanding the influenza pandemic transmission in China.</p></abstract><kwd-group><kwd>China</kwd><kwd>2009 influenza A(H1N1) pandemic</kwd><kwd>transport modes</kwd><kwd>rail travel</kwd><kwd>spatial spread</kwd><kwd>quantile regression</kwd></kwd-group></article-meta></front><floats-group><fig id="ijerph-16-00222-f001" orientation="portrait" position="float"><label>Figure 1</label><caption><p>The spatial and temporal distribution of all 127,797 laboratory-confirmed influenza A(H1N1)pdm09 cases reported to the China Information System for Disease Control and Prevention in mainland China from 10 May 2009 to 30 April 2010: (<bold>a</bold>) spatial distribution of geocoded residential addresses; (<bold>b</bold>) the daily epidemic curve from 10 May 2009 to 30 April 2010; (<bold>c</bold>) the enlarged daily epidemic curve from 10 May 2009 to 31 August 2009.</p></caption><graphic xlink:href="ijerph-16-00222-g001"></graphic></fig><fig id="ijerph-16-00222-f002" orientation="portrait" position="float"><label>Figure 2</label><caption><p>Illustration of arrival days and peak days using the daily epidemic curve of confirmed cases in Beijing. The gray, green, and red vertical dashed lines represent, respectively, the date of the first case in mainland China (10 May 2009), the date of the first case in Beijing (16 May 2009), and the date with the highest incidence in Beijing (23 October 2009).</p></caption><graphic xlink:href="ijerph-16-00222-g002"></graphic></fig><fig id="ijerph-16-00222-f003" orientation="portrait" position="float"><label>Figure 3</label><caption><p>The presence/absence (1/0) of (<bold>a</bold>) airports and (<bold>b</bold>) railway stations in prefectures, and the distributions of (<bold>c</bold>) arrival days and (<bold>d</bold>) peak days for prefectures. The green line in (<bold>c</bold>) indicates the Heihe-Tengchong (Hu) line, and the one in (<bold>d</bold>) indicates the Hukun Railway that connects Shanghai and Kunming. The arrows illustrate that arrival days (or peak days) between prefectures with and without airports (or railway stations) are compared using the Mann-Whitney U test, and their spatial heterogeneity stratified by airports (or railway stations) are detected using <italic>q</italic>-statistic test.</p></caption><graphic xlink:href="ijerph-16-00222-g003"></graphic></fig><fig id="ijerph-16-00222-f004" orientation="portrait" position="float"><label>Figure 4</label><caption><p>Violin plots of arrival days and peak days between prefectures with (in red) and without (in cyan) airports (or railway stations). Box plots are embedded into violin plots to add summary statistics. The dark red points represent the mean arrival days (or peak days). * <italic>p</italic> < 0.05; *** <italic>p</italic> < 0.001; NS., not significant for comparing arrival days (or peak days) between prefectures with and without transport hubs using a Mann-Whitney U test.</p></caption><graphic xlink:href="ijerph-16-00222-g004"></graphic></fig><fig id="ijerph-16-00222-f005" orientation="portrait" position="float"><label>Figure 5</label><caption><p>Quantile process regression of arrival days in 115 prefectures. In (<bold>a</bold>) the density plot of arrival days, the red, green, and blue vertical lines indicate, respectively, the <inline-formula><mml:math id="mm40"><mml:mrow><mml:mi>τ</mml:mi></mml:mrow></mml:math></inline-formula> = 0.25, 0.50, and 0.75 quantiles of arrival days, whereas the dark green vertical line indicates the mean arrival days. In quantile process plots for log-transformed air (<bold>b</bold>), rail (<bold>c</bold>), and road (<bold>d</bold>) passenger volumes, the blue curves and shaded areas represent the quantile regression coefficients and 95% confidence intervals.</p></caption><graphic xlink:href="ijerph-16-00222-g005"></graphic></fig><table-wrap id="ijerph-16-00222-t001" orientation="portrait" position="float"><object-id pub-id-type="pii">ijerph-16-00222-t001_Table 1</object-id><label>Table 1</label><caption><p>Summary statistics of arrival days and peak days for 340 affected prefectures in mainland China.</p></caption><table frame="hsides" rules="groups"><thead><tr><th rowspan="2" colspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin">Category</th><th colspan="5" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1">Arrival Day <sup>a</sup></th><th colspan="5" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1">Peak Day</th></tr><tr><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Min</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Q1</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Median</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Q3</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Max</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Min</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Q1</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Median</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Q3</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Max</th></tr></thead><tbody><tr><td colspan="2" align="center" valign="middle" style="border-bottom:solid thin" rowspan="1"><bold>All</bold> (340)</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">1</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">61</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">119</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">136</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">180</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">97</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">163</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">179</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">198</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">236</td></tr><tr><td rowspan="2" align="center" valign="middle" style="border-bottom:solid thin" colspan="1"><bold>Airport</bold></td><td align="center" valign="middle" rowspan="1" colspan="1">With (155)</td><td align="center" valign="middle" rowspan="1" colspan="1">4</td><td align="center" valign="middle" rowspan="1" colspan="1">41</td><td align="center" valign="middle" rowspan="1" colspan="1">106</td><td align="center" valign="middle" rowspan="1" colspan="1">134</td><td align="center" valign="middle" rowspan="1" colspan="1">180</td><td align="center" valign="middle" rowspan="1" colspan="1">113</td><td align="center" valign="middle" rowspan="1" colspan="1">162</td><td align="center" valign="middle" rowspan="1" colspan="1">180</td><td align="center" valign="middle" rowspan="1" colspan="1">196</td><td align="center" valign="middle" rowspan="1" colspan="1">228</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Without (185)</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">1</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">75</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">120</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">136</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">175</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">97</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">164</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">179</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">199</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">236</td></tr><tr><td rowspan="2" align="center" valign="middle" style="border-bottom:solid thin" colspan="1"><bold>Railway station</bold></td><td align="center" valign="middle" rowspan="1" colspan="1">With (234)</td><td align="center" valign="middle" rowspan="1" colspan="1">1</td><td align="center" valign="middle" rowspan="1" colspan="1">53</td><td align="center" valign="middle" rowspan="1" colspan="1">111</td><td align="center" valign="middle" rowspan="1" colspan="1">129</td><td align="center" valign="middle" rowspan="1" colspan="1">175</td><td align="center" valign="middle" rowspan="1" colspan="1">97</td><td align="center" valign="middle" rowspan="1" colspan="1">163</td><td align="center" valign="middle" rowspan="1" colspan="1">179</td><td align="center" valign="middle" rowspan="1" colspan="1">196</td><td align="center" valign="middle" rowspan="1" colspan="1">236</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Without (106)</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">17</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">93</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">128</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">140</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">180</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">117</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">164</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">181</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">199</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">236</td></tr></tbody></table><table-wrap-foot><fn><p><sup>a</sup> Arrival day and peak day are calculated as starting from 10 May 2009, the date of the first confirmed case reported in mainland China.</p></fn></table-wrap-foot></table-wrap><table-wrap id="ijerph-16-00222-t002" orientation="portrait" position="float"><object-id pub-id-type="pii">ijerph-16-00222-t002_Table 2</object-id><label>Table 2</label><caption><p>Pearson correlation coefficients between arrival days and log-transformed passenger volumes by transport modes in 334 prefectures.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Transport Modes (No. of Prefectures)</th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">log(<italic>PAir</italic>)</th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">log(<italic>PRail</italic>)</th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">log(<italic>PRoad</italic>)</th></tr></thead><tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1">Air + Rail + Road (115)</td><td align="center" valign="middle" rowspan="1" colspan="1">−0.58 ***</td><td align="center" valign="middle" rowspan="1" colspan="1">−0.47 ***</td><td align="center" valign="middle" rowspan="1" colspan="1">−0.60 ***</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Air + Road (20)</td><td align="center" valign="middle" rowspan="1" colspan="1">−0.32</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">−0.34</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Rail + Road (140)</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">−0.17 *</td><td align="center" valign="middle" rowspan="1" colspan="1">−0.25 **</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Road (59)</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">-</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">-</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−0.54 ***</td></tr></tbody></table><table-wrap-foot><fn><p>Note: log(<italic>PAir</italic>), log(<italic>PRail</italic>), and log(<italic>PRoad</italic>) are the log-transformed air, rail, and road passenger volumes (10,000 persons) in each prefecture. * Correlation coefficient is significant at the 0.05 level (2-tailed), ** for 0.01, and *** for 0.001. All values are rounded to two decimal places.</p></fn></table-wrap-foot></table-wrap><table-wrap id="ijerph-16-00222-t003" orientation="portrait" position="float"><object-id pub-id-type="pii">ijerph-16-00222-t003_Table 3</object-id><label>Table 3</label><caption><p>Multivariate quantile regression showing the associations between the 0.25, 0.50, and 0.75 quantiles of arrival days and passenger volumes of three transport modes in 115 prefectures.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Variables <sup>a</sup></th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1"><inline-formula><mml:math id="mm41"><mml:mrow><mml:mi mathvariant="bold-italic">τ</mml:mi></mml:mrow></mml:math></inline-formula> = 0.25</th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1"><inline-formula><mml:math id="mm42"><mml:mrow><mml:mi mathvariant="bold-italic">τ</mml:mi></mml:mrow></mml:math></inline-formula> = 0.50</th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1"><inline-formula><mml:math id="mm43"><mml:mrow><mml:mi mathvariant="bold-italic">τ</mml:mi></mml:mrow></mml:math></inline-formula> = 0.75</th></tr></thead><tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1">Intercept</td><td align="center" valign="middle" rowspan="1" colspan="1">274.08 (222.13, 344.09) <sup>b</sup></td><td align="center" valign="middle" rowspan="1" colspan="1">295.84 (201.19, 381.45)</td><td align="center" valign="middle" rowspan="1" colspan="1">256.25 (189.21, 457.03)</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1"><italic>Lat</italic></td><td align="center" valign="middle" rowspan="1" colspan="1">1.95 (1.30, 3.69)</td><td align="center" valign="middle" rowspan="1" colspan="1">1.78 (1.36, 3.34)</td><td align="center" valign="middle" rowspan="1" colspan="1">2.29 (1.00, 4.06)</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1"><italic>Lng</italic></td><td align="center" valign="middle" rowspan="1" colspan="1">−1.38 (−1.83, −0.46)</td><td align="center" valign="middle" rowspan="1" colspan="1">−1.15 (−2.22, −0.55)</td><td align="center" valign="middle" rowspan="1" colspan="1">−0.80 (−2.33, −0.33)</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">log(<italic>PAir</italic>)</td><td align="center" valign="middle" rowspan="1" colspan="1">−7.23 (−10.75, −0.38)</td><td align="center" valign="middle" rowspan="1" colspan="1">−9.54 (−15.13, −4.81)</td><td align="center" valign="middle" rowspan="1" colspan="1">−12.10 (−14.90, −6.12)</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">log(<italic>PRail</italic>)</td><td align="center" valign="middle" rowspan="1" colspan="1">−1.99 (−12.41, 0.97)</td><td align="center" valign="middle" rowspan="1" colspan="1">−6.18 (−14.41, 1.99)</td><td align="center" valign="middle" rowspan="1" colspan="1">−5.42 (−16.80, −0.45)</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">log(<italic>PRoad</italic>)</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−9.04 (−15.52, −3.05)</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−7.58 (−15.41, −1.37)</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−6.73 (−15.35, −0.23)</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">R<sup>2 c</sup></td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.33</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.45</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.41</td></tr></tbody></table><table-wrap-foot><fn><p><sup>a</sup><italic>Lat</italic> and <italic>Lng</italic> are prefectural latitude and longitude coordinates. log(<italic>PAir</italic>), log(<italic>PRail</italic>), and log(<italic>PRoad</italic>) are the log-transformed air, rail, and road passenger volumes (10,000 persons) in each prefecture. <sup>b</sup> All regression coefficients are rounded to two decimal places. Numbers in parentheses are 95% confidence intervals. <sup>c</sup> Pseudo R<sup>2</sup> are reported for quantile regression.</p></fn></table-wrap-foot></table-wrap></floats-group></pmc><affiliations><list><country><li>République populaire de Chine</li></country><settlement><li>Pékin</li></settlement></list><tree><noCountry><name sortKey="Chen, Ziyue" sort="Chen, Ziyue" uniqKey="Chen Z" first="Ziyue" last="Chen">Ziyue Chen</name><name sortKey="Zhang, Bing" sort="Zhang, Bing" uniqKey="Zhang B" first="Bing" last="Zhang">Bing Zhang</name><name sortKey="Zhang, Xueying" sort="Zhang, Xueying" uniqKey="Zhang X" first="Xueying" last="Zhang">Xueying Zhang</name></noCountry><country name="République populaire de Chine"><noRegion><name sortKey="Cai, Jun" sort="Cai, Jun" uniqKey="Cai J" first="Jun" last="Cai">Jun Cai</name></noRegion><name sortKey="Chan, Karen Kie Yan" sort="Chan, Karen Kie Yan" uniqKey="Chan K" first="Karen Kie Yan" last="Chan">Karen Kie Yan Chan</name><name sortKey="Xu, Bing" sort="Xu, Bing" uniqKey="Xu B" first="Bing" last="Xu">Bing Xu</name><name sortKey="Xu, Bo" sort="Xu, Bo" uniqKey="Xu B" first="Bo" last="Xu">Bo Xu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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