Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus
Identifieur interne : 000704 ( Pmc/Checkpoint ); précédent : 000703; suivant : 000705Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus
Auteurs : Ka Chun Chong ; Benny Chung Ying ZeeSource :
- BMC Infectious Diseases [ 1471-2334 ] ; 2012.
Abstract
During the early stages of a new influenza pandemic, travel restriction is an immediate and non-pharmaceutical means of retarding incidence growth. It extends the time frame of effective mitigation, especially when the characteristics of the emerging virus are unknown. In the present study, we used the 2009 influenza A pandemic as a case study to evaluate the impact of regulating air, sea, and land transport. Other government strategies, namely, antivirals and hospitalizations, were also evaluated.
Hong Kong arrivals from 44 countries via air, sea, and land transports were imported into a discrete stochastic Susceptible, Exposed, Infectious and Recovered (SEIR) host-flow model. The model allowed a number of latent and infectious cases to pass the border, which constitutes a source of local disease transmission. We also modeled antiviral and hospitalization prevention strategies to compare the effectiveness of these control measures. Baseline reproduction rate was estimated from routine surveillance data.
Regarding air travel, the main route connected to the influenza source area should be targeted for travel restrictions; imposing a 99% air travel restriction delayed the epidemic peak by up to two weeks. Once the pandemic was established in China, the strong land connection between Hong Kong and China rendered Hong Kong vulnerable. Antivirals and hospitalization were found to be more effective on attack rate reductions than travel restrictions. Combined strategies (with 99% restriction on all transport modes) deferred the peak for long enough to establish a vaccination program.
The findings will assist policy-makers with decisions on handling similar future pandemics. We also suggest regulating the extent of restriction and the transport mode, once restriction has been deemed necessary for pandemic control. Although travel restrictions have yet to gain social acceptance, they allow time for mitigation response when a new and highly intrusive virus emerges.
The online version of this article (doi:10.1186/1471-2334-12-309) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/1471-2334-12-309
PubMed: 23157818
PubMed Central: 3577649
Affiliations:
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<author><name sortKey="Chong, Ka Chun" sort="Chong, Ka Chun" uniqKey="Chong K" first="Ka Chun" last="Chong">Ka Chun Chong</name>
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<author><name sortKey="Ying Zee, Benny Chung" sort="Ying Zee, Benny Chung" uniqKey="Ying Zee B" first="Benny Chung" last="Ying Zee">Benny Chung Ying Zee</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus</title>
<author><name sortKey="Chong, Ka Chun" sort="Chong, Ka Chun" uniqKey="Chong K" first="Ka Chun" last="Chong">Ka Chun Chong</name>
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<author><name sortKey="Ying Zee, Benny Chung" sort="Ying Zee, Benny Chung" uniqKey="Ying Zee B" first="Benny Chung" last="Ying Zee">Benny Chung Ying Zee</name>
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<series><title level="j">BMC Infectious Diseases</title>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>During the early stages of a new influenza pandemic, travel restriction is an immediate and non-pharmaceutical means of retarding incidence growth. It extends the time frame of effective mitigation, especially when the characteristics of the emerging virus are unknown. In the present study, we used the 2009 influenza A pandemic as a case study to evaluate the impact of regulating air, sea, and land transport. Other government strategies, namely, antivirals and hospitalizations, were also evaluated.</p>
</sec>
<sec><title>Methods</title>
<p>Hong Kong arrivals from 44 countries via air, sea, and land transports were imported into a discrete stochastic Susceptible, Exposed, Infectious and Recovered (SEIR) host-flow model. The model allowed a number of latent and infectious cases to pass the border, which constitutes a source of local disease transmission. We also modeled antiviral and hospitalization prevention strategies to compare the effectiveness of these control measures. Baseline reproduction rate was estimated from routine surveillance data.</p>
</sec>
<sec><title>Results</title>
<p>Regarding air travel, the main route connected to the influenza source area should be targeted for travel restrictions; imposing a 99% air travel restriction delayed the epidemic peak by up to two weeks. Once the pandemic was established in China, the strong land connection between Hong Kong and China rendered Hong Kong vulnerable. Antivirals and hospitalization were found to be more effective on attack rate reductions than travel restrictions. Combined strategies (with 99% restriction on all transport modes) deferred the peak for long enough to establish a vaccination program.</p>
</sec>
<sec><title>Conclusion</title>
<p>The findings will assist policy-makers with decisions on handling similar future pandemics. We also suggest regulating the extent of restriction and the transport mode, once restriction has been deemed necessary for pandemic control. Although travel restrictions have yet to gain social acceptance, they allow time for mitigation response when a new and highly intrusive virus emerges.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/1471-2334-12-309) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
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</TEI>
<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Infect Dis</journal-id>
<journal-id journal-id-type="iso-abbrev">BMC Infect. Dis</journal-id>
<journal-title-group><journal-title>BMC Infectious Diseases</journal-title>
</journal-title-group>
<issn pub-type="epub">1471-2334</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">23157818</article-id>
<article-id pub-id-type="pmc">3577649</article-id>
<article-id pub-id-type="publisher-id">2271</article-id>
<article-id pub-id-type="doi">10.1186/1471-2334-12-309</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Chong</surname>
<given-names>Ka Chun</given-names>
</name>
<address><email>marc@cct.cuhk.edu.hk</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ying Zee</surname>
<given-names>Benny Chung</given-names>
</name>
<address><email>bzee@cct.cuhk.edu.hk</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.10784.3a</institution-id>
<institution-id institution-id-type="ISNI">0000 0004 1937 0482</institution-id>
<institution>Division of Biostatistics, The Jockey Club School of Public Health and Primary Care,</institution>
<institution>The Chinese University of Hong Kong,</institution>
</institution-wrap>
Hong Kong SAR, China</aff>
</contrib-group>
<pub-date pub-type="epub"><day>19</day>
<month>11</month>
<year>2012</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>19</day>
<month>11</month>
<year>2012</year>
</pub-date>
<pub-date pub-type="collection"><year>2012</year>
</pub-date>
<volume>12</volume>
<elocation-id>309</elocation-id>
<history><date date-type="received"><day>9</day>
<month>11</month>
<year>2011</year>
</date>
<date date-type="accepted"><day>15</day>
<month>11</month>
<year>2012</year>
</date>
</history>
<permissions><copyright-statement>© Chong and Zee; licensee BioMed Central Ltd. 2012</copyright-statement>
<license license-type="OpenAccess"><license-p>This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0">http://creativecommons.org/licenses/by/2.0</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
</license>
</permissions>
<abstract id="Abs1"><sec><title>Background</title>
<p>During the early stages of a new influenza pandemic, travel restriction is an immediate and non-pharmaceutical means of retarding incidence growth. It extends the time frame of effective mitigation, especially when the characteristics of the emerging virus are unknown. In the present study, we used the 2009 influenza A pandemic as a case study to evaluate the impact of regulating air, sea, and land transport. Other government strategies, namely, antivirals and hospitalizations, were also evaluated.</p>
</sec>
<sec><title>Methods</title>
<p>Hong Kong arrivals from 44 countries via air, sea, and land transports were imported into a discrete stochastic Susceptible, Exposed, Infectious and Recovered (SEIR) host-flow model. The model allowed a number of latent and infectious cases to pass the border, which constitutes a source of local disease transmission. We also modeled antiviral and hospitalization prevention strategies to compare the effectiveness of these control measures. Baseline reproduction rate was estimated from routine surveillance data.</p>
</sec>
<sec><title>Results</title>
<p>Regarding air travel, the main route connected to the influenza source area should be targeted for travel restrictions; imposing a 99% air travel restriction delayed the epidemic peak by up to two weeks. Once the pandemic was established in China, the strong land connection between Hong Kong and China rendered Hong Kong vulnerable. Antivirals and hospitalization were found to be more effective on attack rate reductions than travel restrictions. Combined strategies (with 99% restriction on all transport modes) deferred the peak for long enough to establish a vaccination program.</p>
</sec>
<sec><title>Conclusion</title>
<p>The findings will assist policy-makers with decisions on handling similar future pandemics. We also suggest regulating the extent of restriction and the transport mode, once restriction has been deemed necessary for pandemic control. Although travel restrictions have yet to gain social acceptance, they allow time for mitigation response when a new and highly intrusive virus emerges.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/1471-2334-12-309) contains supplementary material, which is available to authorized users.</p>
</sec>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd>Influenza</kwd>
<kwd>Severe Acute Respiratory Syndrome</kwd>
<kwd>Influenza Pandemic</kwd>
<kwd>Transport Mode</kwd>
<kwd>Severe Acute Respiratory Syndrome</kwd>
</kwd-group>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2012</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations><list></list>
<tree><noCountry><name sortKey="Chong, Ka Chun" sort="Chong, Ka Chun" uniqKey="Chong K" first="Ka Chun" last="Chong">Ka Chun Chong</name>
<name sortKey="Ying Zee, Benny Chung" sort="Ying Zee, Benny Chung" uniqKey="Ying Zee B" first="Benny Chung" last="Ying Zee">Benny Chung Ying Zee</name>
</noCountry>
</tree>
</affiliations>
</record>
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