A large-scale immuno-epidemiological simulation of influenza A epidemics
Identifieur interne : 001D83 ( Pmc/Checkpoint ); précédent : 001D82; suivant : 001D84A large-scale immuno-epidemiological simulation of influenza A epidemics
Auteurs : Sarah Lukens [États-Unis] ; Jay Depasse [États-Unis] ; Roni Rosenfeld [États-Unis] ; Elodie Ghedin [États-Unis] ; Ericka Mochan [États-Unis] ; Shawn T. Brown [États-Unis] ; John Grefenstette [États-Unis] ; Donald S. Burke [États-Unis] ; David Swigon [États-Unis] ; Gilles Clermont [États-Unis]Source :
- BMC Public Health [ 1471-2458 ] ; 2014.
Abstract
Agent based models (ABM) are useful to explore population-level scenarios of disease spread and containment, but typically characterize infected individuals using simplified models of infection and symptoms dynamics. Adding more realistic models of individual infections and symptoms may help to create more realistic population level epidemic dynamics.
Using an equation-based, host-level mathematical model of influenza A virus infection, we develop a function that expresses the dependence of infectivity and symptoms of an infected individual on initial viral load, age, and viral strain phenotype. We incorporate this response function in a population-scale agent-based model of influenza A epidemic to create a hybrid multiscale modeling framework that reflects both population dynamics and individualized host response to infection.
At the host level, we estimate parameter ranges using experimental data of H1N1 viral titers and symptoms measured in humans. By linearization of symptoms responses of the host-level model we obtain a map of the parameters of the model that characterizes clinical phenotypes of influenza infection and immune response variability over the population. At the population-level model, we analyze the effect of individualizing viral response in agent-based model by simulating epidemics across Allegheny County, Pennsylvania under both age-specific and age-independent severity assumptions.
We present a framework for multi-scale simulations of influenza epidemics that enables the study of population-level effects of individual differences in infections and symptoms, with minimal additional computational cost compared to the existing population-level simulations.
The online version of this article (doi:10.1186/1471-2458-14-1019) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/1471-2458-14-1019
PubMed: 25266818
PubMed Central: 4194421
Affiliations:
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Agent based models (ABM) are useful to explore population-level scenarios of disease spread and containment, but typically characterize infected individuals using simplified models of infection and symptoms dynamics. Adding more realistic models of individual infections and symptoms may help to create more realistic population level epidemic dynamics.</p>
</sec>
<sec><title>Methods</title>
<p>Using an equation-based, host-level mathematical model of influenza A virus infection, we develop a function that expresses the dependence of infectivity and symptoms of an infected individual on initial viral load, age, and viral strain phenotype. We incorporate this response function in a population-scale agent-based model of influenza A epidemic to create a hybrid multiscale modeling framework that reflects both population dynamics and individualized host response to infection.</p>
</sec>
<sec><title>Results</title>
<p>At the host level, we estimate parameter ranges using experimental data of H1N1 viral titers and symptoms measured in humans. By linearization of symptoms responses of the host-level model we obtain a map of the parameters of the model that characterizes clinical phenotypes of influenza infection and immune response variability over the population. At the population-level model, we analyze the effect of individualizing viral response in agent-based model by simulating epidemics across Allegheny County, Pennsylvania under both age-specific and age-independent severity assumptions.</p>
</sec>
<sec><title>Conclusions</title>
<p>We present a framework for multi-scale simulations of influenza epidemics that enables the study of population-level effects of individual differences in infections and symptoms, with minimal additional computational cost compared to the existing population-level simulations.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/1471-2458-14-1019) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Public Health</journal-id>
<journal-id journal-id-type="iso-abbrev">BMC Public Health</journal-id>
<journal-title-group><journal-title>BMC Public Health</journal-title>
</journal-title-group>
<issn pub-type="epub">1471-2458</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">25266818</article-id>
<article-id pub-id-type="pmc">4194421</article-id>
<article-id pub-id-type="publisher-id">7130</article-id>
<article-id pub-id-type="doi">10.1186/1471-2458-14-1019</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>A large-scale immuno-epidemiological simulation of influenza A epidemics</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Lukens</surname>
<given-names>Sarah</given-names>
</name>
<address><email>slukens@nd.edu</email>
</address>
<xref ref-type="aff" rid="Aff14"></xref>
<xref ref-type="aff" rid="Aff22"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>DePasse</surname>
<given-names>Jay</given-names>
</name>
<address><email>depasse@psc.edu</email>
</address>
<xref ref-type="aff" rid="Aff15"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Rosenfeld</surname>
<given-names>Roni</given-names>
</name>
<address><email>roni.rosenfeld@cs.cmu.edu</email>
</address>
<xref ref-type="aff" rid="Aff16"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ghedin</surname>
<given-names>Elodie</given-names>
</name>
<address><email>elodie.ghedin@nyu.edu</email>
</address>
<xref ref-type="aff" rid="Aff17"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Mochan</surname>
<given-names>Ericka</given-names>
</name>
<address><email>edm29@pitt.edu</email>
</address>
<xref ref-type="aff" rid="Aff20"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Brown</surname>
<given-names>Shawn T</given-names>
</name>
<address><email>stbrown@unicron.psc.edu</email>
</address>
<xref ref-type="aff" rid="Aff15"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Grefenstette</surname>
<given-names>John</given-names>
</name>
<address><email>gref@pitt.edu</email>
</address>
<xref ref-type="aff" rid="Aff19"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Burke</surname>
<given-names>Donald S</given-names>
</name>
<address><email>donburke@pitt.edu</email>
</address>
<xref ref-type="aff" rid="Aff21"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Swigon</surname>
<given-names>David</given-names>
</name>
<address><email>swigon@pitt.edu</email>
</address>
<xref ref-type="aff" rid="Aff14"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Clermont</surname>
<given-names>Gilles</given-names>
</name>
<address><email>clermontg@ccm.upmc.edu</email>
</address>
<xref ref-type="aff" rid="Aff18"></xref>
</contrib>
<aff id="Aff14"><label></label>
Department of Mathematics, University of Pittsburgh, Pittsburgh, PA USA</aff>
<aff id="Aff15"><label></label>
Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA USA</aff>
<aff id="Aff16"><label></label>
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA USA</aff>
<aff id="Aff17"><label></label>
Center for Vaccine Research, Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA USA</aff>
<aff id="Aff18"><label></label>
Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA USA</aff>
<aff id="Aff19"><label></label>
Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA USA</aff>
<aff id="Aff20"><label></label>
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA USA</aff>
<aff id="Aff21"><label></label>
Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA USA</aff>
<aff id="Aff22"><label></label>
Department of Biological Sciences, University of Notre Dame, South Bend, IN USA</aff>
</contrib-group>
<pub-date pub-type="epub"><day>29</day>
<month>9</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>29</day>
<month>9</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="collection"><year>2014</year>
</pub-date>
<volume>14</volume>
<elocation-id>1019</elocation-id>
<history><date date-type="received"><day>28</day>
<month>7</month>
<year>2014</year>
</date>
<date date-type="accepted"><day>18</day>
<month>9</month>
<year>2014</year>
</date>
</history>
<permissions><copyright-statement>© Lukens et al.; licensee BioMed Central Ltd. 2014</copyright-statement>
<license license-type="open-access"><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/4.0">http://creativecommons.org/licenses/by/4.0</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1"><sec><title>Background</title>
<p>Agent based models (ABM) are useful to explore population-level scenarios of disease spread and containment, but typically characterize infected individuals using simplified models of infection and symptoms dynamics. Adding more realistic models of individual infections and symptoms may help to create more realistic population level epidemic dynamics.</p>
</sec>
<sec><title>Methods</title>
<p>Using an equation-based, host-level mathematical model of influenza A virus infection, we develop a function that expresses the dependence of infectivity and symptoms of an infected individual on initial viral load, age, and viral strain phenotype. We incorporate this response function in a population-scale agent-based model of influenza A epidemic to create a hybrid multiscale modeling framework that reflects both population dynamics and individualized host response to infection.</p>
</sec>
<sec><title>Results</title>
<p>At the host level, we estimate parameter ranges using experimental data of H1N1 viral titers and symptoms measured in humans. By linearization of symptoms responses of the host-level model we obtain a map of the parameters of the model that characterizes clinical phenotypes of influenza infection and immune response variability over the population. At the population-level model, we analyze the effect of individualizing viral response in agent-based model by simulating epidemics across Allegheny County, Pennsylvania under both age-specific and age-independent severity assumptions.</p>
</sec>
<sec><title>Conclusions</title>
<p>We present a framework for multi-scale simulations of influenza epidemics that enables the study of population-level effects of individual differences in infections and symptoms, with minimal additional computational cost compared to the existing population-level simulations.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/1471-2458-14-1019) contains supplementary material, which is available to authorized users.</p>
</sec>
</abstract>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2014</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Indiana</li>
<li>Pennsylvanie</li>
</region>
</list>
<tree><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Lukens, Sarah" sort="Lukens, Sarah" uniqKey="Lukens S" first="Sarah" last="Lukens">Sarah Lukens</name>
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<name sortKey="Brown, Shawn T" sort="Brown, Shawn T" uniqKey="Brown S" first="Shawn T" last="Brown">Shawn T. Brown</name>
<name sortKey="Burke, Donald S" sort="Burke, Donald S" uniqKey="Burke D" first="Donald S" last="Burke">Donald S. Burke</name>
<name sortKey="Clermont, Gilles" sort="Clermont, Gilles" uniqKey="Clermont G" first="Gilles" last="Clermont">Gilles Clermont</name>
<name sortKey="Depasse, Jay" sort="Depasse, Jay" uniqKey="Depasse J" first="Jay" last="Depasse">Jay Depasse</name>
<name sortKey="Ghedin, Elodie" sort="Ghedin, Elodie" uniqKey="Ghedin E" first="Elodie" last="Ghedin">Elodie Ghedin</name>
<name sortKey="Grefenstette, John" sort="Grefenstette, John" uniqKey="Grefenstette J" first="John" last="Grefenstette">John Grefenstette</name>
<name sortKey="Lukens, Sarah" sort="Lukens, Sarah" uniqKey="Lukens S" first="Sarah" last="Lukens">Sarah Lukens</name>
<name sortKey="Mochan, Ericka" sort="Mochan, Ericka" uniqKey="Mochan E" first="Ericka" last="Mochan">Ericka Mochan</name>
<name sortKey="Rosenfeld, Roni" sort="Rosenfeld, Roni" uniqKey="Rosenfeld R" first="Roni" last="Rosenfeld">Roni Rosenfeld</name>
<name sortKey="Swigon, David" sort="Swigon, David" uniqKey="Swigon D" first="David" last="Swigon">David Swigon</name>
</country>
</tree>
</affiliations>
</record>
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