Enhancement of Collective Immunity in Tokyo Metropolitan Area by Selective Vaccination against an Emerging Influenza Pandemic
Identifieur interne : 000D72 ( Pmc/Curation ); précédent : 000D71; suivant : 000D73Enhancement of Collective Immunity in Tokyo Metropolitan Area by Selective Vaccination against an Emerging Influenza Pandemic
Auteurs : Masaya M. Saito [Japon] ; Seiya Imoto [Japon] ; Rui Yamaguchi [Japon] ; Masaharu Tsubokura [Japon] ; Masahiro Kami [Japon] ; Haruka Nakada [Japon] ; Hiroki Sato [Japon] ; Satoru Miyano [Japon] ; Tomoyuki Higuchi [Japon]Source :
- PLoS ONE [ 1932-6203 ] ; 2013.
Abstract
Vaccination is a preventive measure against influenza that does not require placing restrictions on social activities. However, since the stockpile of vaccine that can be prepared before the arrival of an emerging pandemic strain is generally quite limited, one has to select priority target groups to which the first stockpile is distributed. In this paper, we study a simulation-based priority target selection method with the goal of enhancing the collective immunity of the whole population. To model the region in which the disease spreads, we consider an urban area composed of suburbs and central areas connected by a single commuter train line. Human activity is modelled following an agent-based approach. The degree to which collective immunity is enhanced is judged by the attack rate in unvaccinated people. The simulation results show that if students and office workers are given exclusive priority in the first three months, the attack rate can be reduced from
Url:
DOI: 10.1371/journal.pone.0072866
PubMed: 24058445
PubMed Central: 3776821
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<front><div type="abstract" xml:lang="en"><p>Vaccination is a preventive measure against influenza that does not require placing restrictions on social activities. However, since the stockpile of vaccine that can be prepared before the arrival of an emerging pandemic strain is generally quite limited, one has to select priority target groups to which the first stockpile is distributed. In this paper, we study a simulation-based priority target selection method with the goal of enhancing the collective immunity of the whole population. To model the region in which the disease spreads, we consider an urban area composed of suburbs and central areas connected by a single commuter train line. Human activity is modelled following an agent-based approach. The degree to which collective immunity is enhanced is judged by the attack rate in unvaccinated people. The simulation results show that if students and office workers are given exclusive priority in the first three months, the attack rate can be reduced from <inline-formula><inline-graphic xlink:href="pone.0072866.e001.jpg"></inline-graphic>
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in the baseline case down to 1–2%. In contrast, random vaccination only slightly reduces the attack rate. It should be noted that giving preference to active social groups does not mean sacrificing those at high risk, which corresponds to the elderly in our simulation model. Compared with the random administration of vaccine to all social groups, this design successfully reduces the attack rate across all age groups.</p>
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<title-group><article-title>Enhancement of Collective Immunity in Tokyo Metropolitan Area by Selective Vaccination against an Emerging Influenza Pandemic</article-title>
<alt-title alt-title-type="running-head">Enhancement of Collective Immunity for Influenza</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Saito</surname>
<given-names>Masaya M.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor1"><sup>*</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Imoto</surname>
<given-names>Seiya</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Yamaguchi</surname>
<given-names>Rui</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Tsubokura</surname>
<given-names>Masaharu</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Kami</surname>
<given-names>Masahiro</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Nakada</surname>
<given-names>Haruka</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Sato</surname>
<given-names>Hiroki</given-names>
</name>
<xref ref-type="aff" rid="aff4"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Miyano</surname>
<given-names>Satoru</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Higuchi</surname>
<given-names>Tomoyuki</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
<addr-line>Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan</addr-line>
</aff>
<aff id="aff2"><label>2</label>
<addr-line>Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo, Japan</addr-line>
</aff>
<aff id="aff3"><label>3</label>
<addr-line>Division of Social Communication Systems for Advanced Clinical Research, Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo, Japan</addr-line>
</aff>
<aff id="aff4"><label>4</label>
<addr-line>Department of Preventive Medicine and Public Health, National Defense Medical College, Tokorozawa, Saitama, Japan</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Boni</surname>
<given-names>Maciej F.</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>University of Oxford, Viet Nam</addr-line>
</aff>
<author-notes><corresp id="cor1">* E-mail: <email>saitohm@ism.ac.jp</email>
</corresp>
<fn fn-type="COI-statement"><p><bold>Competing Interests: </bold>
The authors have declared that no competing interests exist.</p>
</fn>
<fn fn-type="con"><p>Conceived and designed simulations: SI RY TH SM. Performed the simulations: MMS. Wrote the paper: MMS SI RY. Contributed the development of the simulator: MMS. Gave medical advice on the simulation setting: MT MK HN HS.</p>
</fn>
</author-notes>
<pub-date pub-type="collection"><year>2013</year>
</pub-date>
<pub-date pub-type="epub"><day>18</day>
<month>9</month>
<year>2013</year>
</pub-date>
<volume>8</volume>
<issue>9</issue>
<elocation-id>e72866</elocation-id>
<history><date date-type="received"><day>21</day>
<month>6</month>
<year>2012</year>
</date>
<date date-type="accepted"><day>20</day>
<month>7</month>
<year>2013</year>
</date>
</history>
<permissions><copyright-statement>© 2013 Saito et al</copyright-statement>
<copyright-year>2013</copyright-year>
<copyright-holder>Saito et al</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.</license-p>
</license>
</permissions>
<abstract><p>Vaccination is a preventive measure against influenza that does not require placing restrictions on social activities. However, since the stockpile of vaccine that can be prepared before the arrival of an emerging pandemic strain is generally quite limited, one has to select priority target groups to which the first stockpile is distributed. In this paper, we study a simulation-based priority target selection method with the goal of enhancing the collective immunity of the whole population. To model the region in which the disease spreads, we consider an urban area composed of suburbs and central areas connected by a single commuter train line. Human activity is modelled following an agent-based approach. The degree to which collective immunity is enhanced is judged by the attack rate in unvaccinated people. The simulation results show that if students and office workers are given exclusive priority in the first three months, the attack rate can be reduced from <inline-formula><inline-graphic xlink:href="pone.0072866.e001.jpg"></inline-graphic>
</inline-formula>
in the baseline case down to 1–2%. In contrast, random vaccination only slightly reduces the attack rate. It should be noted that giving preference to active social groups does not mean sacrificing those at high risk, which corresponds to the elderly in our simulation model. Compared with the random administration of vaccine to all social groups, this design successfully reduces the attack rate across all age groups.</p>
</abstract>
<funding-group><funding-statement>The authors have no support or funding to report.</funding-statement>
</funding-group>
<counts><page-count count="9"></page-count>
</counts>
</article-meta>
</front>
</pmc>
</record>
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