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Searching for Superspreaders: Identifying Epidemic Patterns Associated with Superspreading Events in Stochastic Models

Identifieur interne : 000468 ( Pmc/Checkpoint ); précédent : 000467; suivant : 000469

Searching for Superspreaders: Identifying Epidemic Patterns Associated with Superspreading Events in Stochastic Models

Auteurs :

Source :

RBID : PMC:7123311

Abstract

The importance of host transmissibility in disease emergence has been demonstrated in historical and recent pandemics that involve infectious individuals, known as superspreaders, who are capable of transmitting the infection to a large number of susceptible individuals. To investigate the impact of superspreaders on epidemic dynamics, we formulate deterministic and stochastic models that incorporate differences in superspreaders versus nonsuperspreaders. In particular, continuous-time Markov chain models are used to investigate epidemic features associated with the presence of superspreaders in a population. We parameterize the models for two case studies, Middle East respiratory syndrome (MERS) and Ebola. Through mathematical analysis and numerical simulations, we find that the probability of outbreaks increases and time to outbreaks decreases as the prevalence of superspreaders increases in the population. In particular, as disease outbreaks occur more rapidly and more frequently when initiated by superspreaders, our results emphasize the need for expeditious public health interventions.


Url:
DOI: 10.1007/978-3-319-98083-6_1
PubMed: NONE
PubMed Central: 7123311


Affiliations:


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PMC:7123311

Le document en format XML

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<p id="Par1">The importance of host transmissibility in disease emergence has been demonstrated in historical and recent pandemics that involve infectious individuals, known as superspreaders, who are capable of transmitting the infection to a large number of susceptible individuals. To investigate the impact of superspreaders on epidemic dynamics, we formulate deterministic and stochastic models that incorporate differences in superspreaders versus nonsuperspreaders. In particular, continuous-time Markov chain models are used to investigate epidemic features associated with the presence of superspreaders in a population. We parameterize the models for two case studies, Middle East respiratory syndrome (MERS) and Ebola. Through mathematical analysis and numerical simulations, we find that the probability of outbreaks increases and time to outbreaks decreases as the prevalence of superspreaders increases in the population. In particular, as disease outbreaks occur more rapidly and more frequently when initiated by superspreaders, our results emphasize the need for expeditious public health interventions.</p>
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<name>
<surname>Radunskaya</surname>
<given-names>Ami</given-names>
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<phone>+11909-621-8409</phone>
<fax>+11909-607-1247</fax>
<email>aer04747@pomona.edu</email>
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<xref ref-type="aff" rid="Aff2">2</xref>
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<name>
<surname>Segal</surname>
<given-names>Rebecca</given-names>
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<phone>+11804-828-4756</phone>
<email>rasegal@vcu.edu</email>
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<xref ref-type="aff" rid="Aff3">3</xref>
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<name>
<surname>Shtylla</surname>
<given-names>Blerta</given-names>
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<phone>+119096218712</phone>
<email>shtyllab@pomona.edu</email>
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<xref ref-type="aff" rid="Aff4">4</xref>
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<name>
<surname>Edholm</surname>
<given-names>Christina J.</given-names>
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<address>
<email>cedholm@utk.edu</email>
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<xref ref-type="aff" rid="Aff5">5</xref>
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<name>
<surname>Emerenini</surname>
<given-names>Blessing O.</given-names>
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<name>
<surname>Murillo</surname>
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<name>
<surname>Saucedo</surname>
<given-names>Omar</given-names>
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<name>
<surname>Shakiba</surname>
<given-names>Nika</given-names>
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<name>
<surname>Wang</surname>
<given-names>Xueying</given-names>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Allen</surname>
<given-names>Linda J. S.</given-names>
</name>
<xref ref-type="aff" rid="Aff11">11</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Peace</surname>
<given-names>Angela</given-names>
</name>
<xref ref-type="aff" rid="Aff11">11</xref>
</contrib>
<aff id="Aff5">
<label>5</label>
<institution-wrap>
<institution-id institution-id-type="GRID">grid.411461.7</institution-id>
<institution-id institution-id-type="ISNI">0000 0001 2315 1184</institution-id>
<institution>Department of Mathematics,</institution>
<institution>University of Tennessee,</institution>
</institution-wrap>
Knoxville, TN USA</aff>
<aff id="Aff6">
<label>6</label>
<institution-wrap>
<institution-id institution-id-type="GRID">grid.68312.3e</institution-id>
<institution-id institution-id-type="ISNI">0000 0004 1936 9422</institution-id>
<institution>Department of Physics,</institution>
<institution>Ryerson University,</institution>
</institution-wrap>
Toronto, ON Canada</aff>
<aff id="Aff7">
<label>7</label>
<institution-wrap>
<institution-id institution-id-type="GRID">grid.265892.2</institution-id>
<institution-id institution-id-type="ISNI">0000000106344187</institution-id>
<institution>Department of Biostatistics,</institution>
<institution>University of Alabama at Birmingham,</institution>
</institution-wrap>
Birmingham, AL USA</aff>
<aff id="Aff8">
<label>8</label>
<institution-wrap>
<institution-id institution-id-type="GRID">grid.261331.4</institution-id>
<institution-id institution-id-type="ISNI">0000 0001 2285 7943</institution-id>
<institution>Mathematical Biosciences Institute,</institution>
<institution>The Ohio State University,</institution>
</institution-wrap>
Columbus, OH USA</aff>
<aff id="Aff9">
<label>9</label>
<institution-wrap>
<institution-id institution-id-type="GRID">grid.17063.33</institution-id>
<institution-id institution-id-type="ISNI">0000 0001 2157 2938</institution-id>
<institution>Institute of Biomaterials and Biomedical Engineering (IBBME),</institution>
<institution>University of Toronto,</institution>
</institution-wrap>
Toronto, ON Canada</aff>
<aff id="Aff10">
<label>10</label>
<institution-wrap>
<institution-id institution-id-type="GRID">grid.30064.31</institution-id>
<institution-id institution-id-type="ISNI">0000 0001 2157 6568</institution-id>
<institution>Department of Mathematics and Statistics,</institution>
<institution>Washington State University,</institution>
</institution-wrap>
Pullman, WA USA</aff>
<aff id="Aff11">
<label>11</label>
<institution-wrap>
<institution-id institution-id-type="GRID">grid.264784.b</institution-id>
<institution-id institution-id-type="ISNI">0000 0001 2186 7496</institution-id>
<institution>Department of Mathematics and Statistics,</institution>
<institution>Texas Tech University,</institution>
</institution-wrap>
Lubbock, TX USA</aff>
</contrib-group>
<pub-date pub-type="epub">
<day>25</day>
<month>10</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="collection">
<year>2018</year>
</pub-date>
<volume>14</volume>
<fpage>1</fpage>
<lpage>29</lpage>
<permissions>
<copyright-statement>© The Author(s) and the Association for Women in Mathematics 2018</copyright-statement>
<license>
<license-p>This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.</license-p>
</license>
</permissions>
<abstract id="Abs1">
<p id="Par1">The importance of host transmissibility in disease emergence has been demonstrated in historical and recent pandemics that involve infectious individuals, known as superspreaders, who are capable of transmitting the infection to a large number of susceptible individuals. To investigate the impact of superspreaders on epidemic dynamics, we formulate deterministic and stochastic models that incorporate differences in superspreaders versus nonsuperspreaders. In particular, continuous-time Markov chain models are used to investigate epidemic features associated with the presence of superspreaders in a population. We parameterize the models for two case studies, Middle East respiratory syndrome (MERS) and Ebola. Through mathematical analysis and numerical simulations, we find that the probability of outbreaks increases and time to outbreaks decreases as the prevalence of superspreaders increases in the population. In particular, as disease outbreaks occur more rapidly and more frequently when initiated by superspreaders, our results emphasize the need for expeditious public health interventions.</p>
</abstract>
<kwd-group xml:lang="en">
<title>Keywords</title>
<kwd>Superspreader</kwd>
<kwd>Host heterogeneity</kwd>
<kwd>Middle East respiratory syndrome</kwd>
<kwd>Ebola</kwd>
<kwd>Deterministic model</kwd>
<kwd>Stochastic model</kwd>
</kwd-group>
<custom-meta-group>
<custom-meta>
<meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) and the Association for Women in Mathematics 2018</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations>
<list></list>
<tree></tree>
</affiliations>
</record>

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