Identifying determinants of heterogeneous transmission dynamics of the Middle East respiratory syndrome (MERS) outbreak in the Republic of Korea, 2015: a retrospective epidemiological analysis
Identifieur interne : 000B00 ( Pmc/Curation ); précédent : 000A99; suivant : 000B01Identifying determinants of heterogeneous transmission dynamics of the Middle East respiratory syndrome (MERS) outbreak in the Republic of Korea, 2015: a retrospective epidemiological analysis
Auteurs : Hiroshi Nishiura [Japon] ; Akira Endo [Japon] ; Masaya Saitoh [Japon] ; Ryo Kinoshita [Japon] ; Ryo Ueno [Japon] ; Shinji Nakaoka [Japon] ; Yuichiro Miyamatsu [Japon] ; Yueping Dong [Japon] ; Gerardo Chowell [États-Unis] ; Kenji Mizumoto [Japon]Source :
- BMJ Open [ 2044-6055 ] ; 2016.
Abstract
To investigate the heterogeneous transmission patterns of Middle East respiratory syndrome (MERS) in the Republic of Korea, with a particular focus on epidemiological characteristics of superspreaders.
Retrospective epidemiological analysis.
Multiple healthcare facilities of secondary and tertiary care centres in an urban setting.
A total of 185 laboratory-confirmed cases with partially known dates of illness onset and most likely sources of infection.
Superspreaders were identified using the transmission tree. The reproduction number, that is, the average number of secondary cases produced by a single primary case, was estimated as a function of time and according to different types of hosts.
A total of five superspreaders were identified. The reproduction number throughout the course of the outbreak was estimated at 1.0 due to reconstruction of the transmission tree, while the variance of secondary cases generated by a primary case was 52.1. All of the superspreaders involved in this outbreak appeared to have generated a substantial number of contacts in multiple healthcare facilities (association: p<0.01), generating on average 4.0 (0.0–8.6) and 28.6 (0.0–63.9) secondary cases among patients who visited multiple healthcare facilities and others. The time-dependent reproduction numbers declined substantially below the value of 1 on and after 13 June 2015.
Superspreaders who visited multiple facilities drove the epidemic by generating a disproportionate number of secondary cases. Our findings underscore the need to limit the contacts in healthcare settings. Contact tracing efforts could assist early laboratory testing and diagnosis of suspected cases.
Url:
DOI: 10.1136/bmjopen-2015-009936
PubMed: 26908522
PubMed Central: 4769415
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<author><name sortKey="Nishiura, Hiroshi" sort="Nishiura, Hiroshi" uniqKey="Nishiura H" first="Hiroshi" last="Nishiura">Hiroshi Nishiura</name>
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<author><name sortKey="Endo, Akira" sort="Endo, Akira" uniqKey="Endo A" first="Akira" last="Endo">Akira Endo</name>
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<author><name sortKey="Saitoh, Masaya" sort="Saitoh, Masaya" uniqKey="Saitoh M" first="Masaya" last="Saitoh">Masaya Saitoh</name>
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</nlm:aff>
<country xml:lang="fr">Japon</country>
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<affiliation wicri:level="1"><nlm:aff id="af2"><institution>CREST, Japan Science and Technology Agency</institution>
,<addr-line>Saitama</addr-line>
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</nlm:aff>
<country xml:lang="fr">Japon</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<affiliation wicri:level="1"><nlm:aff id="af4"><institution>The Institute of Statistical Mathematics</institution>
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</nlm:aff>
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<author><name sortKey="Kinoshita, Ryo" sort="Kinoshita, Ryo" uniqKey="Kinoshita R" first="Ryo" last="Kinoshita">Ryo Kinoshita</name>
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,<addr-line>Tokyo</addr-line>
,<country>Japan</country>
</nlm:aff>
<country xml:lang="fr">Japon</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<affiliation wicri:level="1"><nlm:aff id="af2"><institution>CREST, Japan Science and Technology Agency</institution>
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</nlm:aff>
<country xml:lang="fr">Japon</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<affiliation wicri:level="1"><nlm:aff id="af3"><institution>Graduate School of Medicine, Hokkaido University</institution>
,<addr-line>Sapporo-shi, Hokkaido</addr-line>
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</nlm:aff>
<country xml:lang="fr">Japon</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<author><name sortKey="Ueno, Ryo" sort="Ueno, Ryo" uniqKey="Ueno R" first="Ryo" last="Ueno">Ryo Ueno</name>
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<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<author><name sortKey="Nakaoka, Shinji" sort="Nakaoka, Shinji" uniqKey="Nakaoka S" first="Shinji" last="Nakaoka">Shinji Nakaoka</name>
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</nlm:aff>
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<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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</nlm:aff>
<country xml:lang="fr">Japon</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<author><name sortKey="Miyamatsu, Yuichiro" sort="Miyamatsu, Yuichiro" uniqKey="Miyamatsu Y" first="Yuichiro" last="Miyamatsu">Yuichiro Miyamatsu</name>
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<affiliation wicri:level="1"><nlm:aff id="af2"><institution>CREST, Japan Science and Technology Agency</institution>
,<addr-line>Saitama</addr-line>
,<country>Japan</country>
</nlm:aff>
<country xml:lang="fr">Japon</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<affiliation wicri:level="1"><nlm:aff id="af3"><institution>Graduate School of Medicine, Hokkaido University</institution>
,<addr-line>Sapporo-shi, Hokkaido</addr-line>
,<country>Japan</country>
</nlm:aff>
<country xml:lang="fr">Japon</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<author><name sortKey="Dong, Yueping" sort="Dong, Yueping" uniqKey="Dong Y" first="Yueping" last="Dong">Yueping Dong</name>
<affiliation wicri:level="1"><nlm:aff id="af1"><addr-line>Infectious Disease Epidemiology team</addr-line>
,<institution>Graduate School of Medicine, The University of Tokyo</institution>
,<addr-line>Tokyo</addr-line>
,<country>Japan</country>
</nlm:aff>
<country xml:lang="fr">Japon</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="af2"><institution>CREST, Japan Science and Technology Agency</institution>
,<addr-line>Saitama</addr-line>
,<country>Japan</country>
</nlm:aff>
<country xml:lang="fr">Japon</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
</affiliation>
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<author><name sortKey="Chowell, Gerardo" sort="Chowell, Gerardo" uniqKey="Chowell G" first="Gerardo" last="Chowell">Gerardo Chowell</name>
<affiliation wicri:level="1"><nlm:aff id="af5"><institution>School of Public Health</institution>
,<institution>Georgia State University</institution>
,<addr-line>Atlanta, Georgia</addr-line>
,<country>USA</country>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="af6"><addr-line>Division of International Epidemiology and Population Studies</addr-line>
,<institution>Fogarty International Center, National Institutes of Health</institution>
,<addr-line>Bethesda, Maryland</addr-line>
,<country>USA</country>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Mizumoto, Kenji" sort="Mizumoto, Kenji" uniqKey="Mizumoto K" first="Kenji" last="Mizumoto">Kenji Mizumoto</name>
<affiliation wicri:level="1"><nlm:aff id="af2"><institution>CREST, Japan Science and Technology Agency</institution>
,<addr-line>Saitama</addr-line>
,<country>Japan</country>
</nlm:aff>
<country xml:lang="fr">Japon</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="af3"><institution>Graduate School of Medicine, Hokkaido University</institution>
,<addr-line>Sapporo-shi, Hokkaido</addr-line>
,<country>Japan</country>
</nlm:aff>
<country xml:lang="fr">Japon</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="af7"><institution>Graduate School of Arts and Sciences, The University of Tokyo</institution>
,<addr-line>Tokyo</addr-line>
,<country>Japan</country>
</nlm:aff>
<country xml:lang="fr">Japon</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<front><div type="abstract" xml:lang="en"><sec><title>Objectives</title>
<p>To investigate the heterogeneous transmission patterns of Middle East respiratory syndrome (MERS) in the Republic of Korea, with a particular focus on epidemiological characteristics of superspreaders.</p>
</sec>
<sec><title>Design</title>
<p>Retrospective epidemiological analysis.</p>
</sec>
<sec><title>Setting</title>
<p>Multiple healthcare facilities of secondary and tertiary care centres in an urban setting.</p>
</sec>
<sec><title>Participants</title>
<p>A total of 185 laboratory-confirmed cases with partially known dates of illness onset and most likely sources of infection.</p>
</sec>
<sec><title>Primary and secondary outcome measures</title>
<p>Superspreaders were identified using the transmission tree. The reproduction number, that is, the average number of secondary cases produced by a single primary case, was estimated as a function of time and according to different types of hosts.</p>
</sec>
<sec><title>Results</title>
<p>A total of five superspreaders were identified. The reproduction number throughout the course of the outbreak was estimated at 1.0 due to reconstruction of the transmission tree, while the variance of secondary cases generated by a primary case was 52.1. All of the superspreaders involved in this outbreak appeared to have generated a substantial number of contacts in multiple healthcare facilities (association: p<0.01), generating on average 4.0 (0.0–8.6) and 28.6 (0.0–63.9) secondary cases among patients who visited multiple healthcare facilities and others. The time-dependent reproduction numbers declined substantially below the value of 1 on and after 13 June 2015.</p>
</sec>
<sec><title>Conclusions</title>
<p>Superspreaders who visited multiple facilities drove the epidemic by generating a disproportionate number of secondary cases. Our findings underscore the need to limit the contacts in healthcare settings. Contact tracing efforts could assist early laboratory testing and diagnosis of suspected cases.</p>
</sec>
</div>
</front>
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</listBibl>
</div1>
</back>
</TEI>
<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMJ Open</journal-id>
<journal-id journal-id-type="iso-abbrev">BMJ Open</journal-id>
<journal-id journal-id-type="hwp">bmjopen</journal-id>
<journal-id journal-id-type="publisher-id">bmjopen</journal-id>
<journal-title-group><journal-title>BMJ Open</journal-title>
</journal-title-group>
<issn pub-type="epub">2044-6055</issn>
<publisher><publisher-name>BMJ Publishing Group</publisher-name>
<publisher-loc>BMA House, Tavistock Square, London, WC1H 9JR</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">26908522</article-id>
<article-id pub-id-type="pmc">4769415</article-id>
<article-id pub-id-type="publisher-id">bmjopen-2015-009936</article-id>
<article-id pub-id-type="doi">10.1136/bmjopen-2015-009936</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Infectious Diseases</subject>
<subj-group><subject>Research</subject>
</subj-group>
</subj-group>
<subj-group subj-group-type="hwp-journal-coll"><subject>1506</subject>
<subject>1706</subject>
<subject>1692</subject>
<subject>1699</subject>
<subject>1724</subject>
<subject>1731</subject>
</subj-group>
</article-categories>
<title-group><article-title>Identifying determinants of heterogeneous transmission dynamics of the Middle East respiratory syndrome (MERS) outbreak in the Republic of Korea, 2015: a retrospective epidemiological analysis</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Nishiura</surname>
<given-names>Hiroshi</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="aff" rid="af3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Endo</surname>
<given-names>Akira</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Saitoh</surname>
<given-names>Masaya</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="aff" rid="af4">4</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Kinoshita</surname>
<given-names>Ryo</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="aff" rid="af3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ueno</surname>
<given-names>Ryo</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Nakaoka</surname>
<given-names>Shinji</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
<xref ref-type="aff" rid="af2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Miyamatsu</surname>
<given-names>Yuichiro</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="aff" rid="af3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Dong</surname>
<given-names>Yueping</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
<xref ref-type="aff" rid="af2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chowell</surname>
<given-names>Gerardo</given-names>
</name>
<xref ref-type="aff" rid="af5">5</xref>
<xref ref-type="aff" rid="af6">6</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Mizumoto</surname>
<given-names>Kenji</given-names>
</name>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="aff" rid="af3">3</xref>
<xref ref-type="aff" rid="af7">7</xref>
</contrib>
</contrib-group>
<aff id="af1"><label>1</label>
<addr-line>Infectious Disease Epidemiology team</addr-line>
,<institution>Graduate School of Medicine, The University of Tokyo</institution>
,<addr-line>Tokyo</addr-line>
,<country>Japan</country>
</aff>
<aff id="af2"><label>2</label>
<institution>CREST, Japan Science and Technology Agency</institution>
,<addr-line>Saitama</addr-line>
,<country>Japan</country>
</aff>
<aff id="af3"><label>3</label>
<institution>Graduate School of Medicine, Hokkaido University</institution>
,<addr-line>Sapporo-shi, Hokkaido</addr-line>
,<country>Japan</country>
</aff>
<aff id="af4"><label>4</label>
<institution>The Institute of Statistical Mathematics</institution>
,<addr-line>Tokyo</addr-line>
,<country>Japan</country>
</aff>
<aff id="af5"><label>5</label>
<institution>School of Public Health</institution>
,<institution>Georgia State University</institution>
,<addr-line>Atlanta, Georgia</addr-line>
,<country>USA</country>
</aff>
<aff id="af6"><label>6</label>
<addr-line>Division of International Epidemiology and Population Studies</addr-line>
,<institution>Fogarty International Center, National Institutes of Health</institution>
,<addr-line>Bethesda, Maryland</addr-line>
,<country>USA</country>
</aff>
<aff id="af7"><label>7</label>
<institution>Graduate School of Arts and Sciences, The University of Tokyo</institution>
,<addr-line>Tokyo</addr-line>
,<country>Japan</country>
</aff>
<author-notes><corresp><label>Correspondence to</label>
Dr Hiroshi Nishiura; <email>nishiurah@gmail.com</email>
</corresp>
</author-notes>
<pub-date pub-type="collection"><year>2016</year>
</pub-date>
<pub-date pub-type="epub"><day>22</day>
<month>2</month>
<year>2016</year>
</pub-date>
<volume>6</volume>
<issue>2</issue>
<elocation-id>e009936</elocation-id>
<history><date date-type="received"><day>9</day>
<month>9</month>
<year>2015</year>
</date>
<date date-type="rev-recd"><day>30</day>
<month>12</month>
<year>2015</year>
</date>
<date date-type="accepted"><day>1</day>
<month>2</month>
<year>2016</year>
</date>
</history>
<permissions><copyright-statement>Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/</copyright-statement>
<copyright-year>2016</copyright-year>
<license license-type="open-access"><license-p>This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/4.0/">http://creativecommons.org/licenses/by-nc/4.0/</ext-link>
</license-p>
</license>
</permissions>
<self-uri xlink:title="pdf" xlink:href="bmjopen-2015-009936.pdf"></self-uri>
<abstract><sec><title>Objectives</title>
<p>To investigate the heterogeneous transmission patterns of Middle East respiratory syndrome (MERS) in the Republic of Korea, with a particular focus on epidemiological characteristics of superspreaders.</p>
</sec>
<sec><title>Design</title>
<p>Retrospective epidemiological analysis.</p>
</sec>
<sec><title>Setting</title>
<p>Multiple healthcare facilities of secondary and tertiary care centres in an urban setting.</p>
</sec>
<sec><title>Participants</title>
<p>A total of 185 laboratory-confirmed cases with partially known dates of illness onset and most likely sources of infection.</p>
</sec>
<sec><title>Primary and secondary outcome measures</title>
<p>Superspreaders were identified using the transmission tree. The reproduction number, that is, the average number of secondary cases produced by a single primary case, was estimated as a function of time and according to different types of hosts.</p>
</sec>
<sec><title>Results</title>
<p>A total of five superspreaders were identified. The reproduction number throughout the course of the outbreak was estimated at 1.0 due to reconstruction of the transmission tree, while the variance of secondary cases generated by a primary case was 52.1. All of the superspreaders involved in this outbreak appeared to have generated a substantial number of contacts in multiple healthcare facilities (association: p<0.01), generating on average 4.0 (0.0–8.6) and 28.6 (0.0–63.9) secondary cases among patients who visited multiple healthcare facilities and others. The time-dependent reproduction numbers declined substantially below the value of 1 on and after 13 June 2015.</p>
</sec>
<sec><title>Conclusions</title>
<p>Superspreaders who visited multiple facilities drove the epidemic by generating a disproportionate number of secondary cases. Our findings underscore the need to limit the contacts in healthcare settings. Contact tracing efforts could assist early laboratory testing and diagnosis of suspected cases.</p>
</sec>
</abstract>
<kwd-group><kwd>EPIDEMIOLOGY</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
</record>
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