A large-scale immuno-epidemiological simulation of influenza A epidemics
Identifieur interne : 003794 ( Ncbi/Curation ); précédent : 003793; suivant : 003795A 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.
Descripteurs français
- KwdFr :
- Adolescent, Adulte, Adulte d'âge moyen, Enfant, Enfant d'âge préscolaire, Grippe humaine (épidémiologie), Humains, Jeune adulte, Modèles théoriques, Pennsylvanie (épidémiologie), Sous-type H1N1 du virus de la grippe A (immunologie), Sous-type H1N1 du virus de la grippe A (isolement et purification), Sujet âgé, Épidémies.
- MESH :
- immunologie : Sous-type H1N1 du virus de la grippe A.
- isolement et purification : Sous-type H1N1 du virus de la grippe A.
- épidémiologie : Grippe humaine, Pennsylvanie.
- Adolescent, Adulte, Adulte d'âge moyen, Enfant, Enfant d'âge préscolaire, Humains, Jeune adulte, Modèles théoriques, Sujet âgé, Épidémies.
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : Pennsylvania.
- epidemiology : Influenza, Human.
- immunology : Influenza A Virus, H1N1 Subtype.
- isolation & purification : Influenza A Virus, H1N1 Subtype.
- Adolescent, Adult, Aged, Child, Child, Preschool, Epidemics, Humans, Middle Aged, Models, Theoretical, Young Adult.
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
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PMC:4194421Le document en format XML
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S" first="Sarah" last="Lukens">Sarah Lukens</name><affiliation wicri:level="2"><nlm:aff id="Aff14">Department of Mathematics, University of Pittsburgh, Pittsburgh, PA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>Department of Mathematics, University of Pittsburgh, Pittsburgh</wicri:cityArea></affiliation><affiliation wicri:level="2"><nlm:aff id="Aff22">Department of Biological Sciences, University of Notre Dame, South Bend, IN USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Indiana</region></placeName><wicri:cityArea>Department of Biological Sciences, University of Notre Dame, South Bend</wicri:cityArea></affiliation></author><author><name sortKey="Depasse, Jay" sort="Depasse, Jay" uniqKey="Depasse J" first="Jay" last="Depasse">Jay Depasse</name><affiliation wicri:level="2"><nlm:aff id="Aff15">Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh</wicri:cityArea></affiliation></author><author><name sortKey="Rosenfeld, Roni" sort="Rosenfeld, Roni" uniqKey="Rosenfeld R" first="Roni" last="Rosenfeld">Roni Rosenfeld</name><affiliation wicri:level="2"><nlm:aff id="Aff16">School of Computer Science, Carnegie Mellon University, Pittsburgh, PA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>School of Computer Science, Carnegie Mellon University, Pittsburgh</wicri:cityArea></affiliation></author><author><name sortKey="Ghedin, Elodie" sort="Ghedin, Elodie" uniqKey="Ghedin E" first="Elodie" last="Ghedin">Elodie Ghedin</name><affiliation wicri:level="2"><nlm:aff id="Aff17">Center for Vaccine Research, Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>Center for Vaccine Research, Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh</wicri:cityArea></affiliation></author><author><name sortKey="Mochan, Ericka" sort="Mochan, Ericka" uniqKey="Mochan E" first="Ericka" last="Mochan">Ericka Mochan</name><affiliation wicri:level="2"><nlm:aff id="Aff20">Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh</wicri:cityArea></affiliation></author><author><name sortKey="Brown, Shawn T" sort="Brown, Shawn T" uniqKey="Brown S" first="Shawn T" last="Brown">Shawn T. Brown</name><affiliation wicri:level="2"><nlm:aff id="Aff15">Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh</wicri:cityArea></affiliation></author><author><name sortKey="Grefenstette, John" sort="Grefenstette, John" uniqKey="Grefenstette J" first="John" last="Grefenstette">John Grefenstette</name><affiliation wicri:level="2"><nlm:aff id="Aff19">Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh</wicri:cityArea></affiliation></author><author><name sortKey="Burke, Donald S" sort="Burke, Donald S" uniqKey="Burke D" first="Donald S" last="Burke">Donald S. Burke</name><affiliation wicri:level="2"><nlm:aff id="Aff21">Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh</wicri:cityArea></affiliation></author><author><name sortKey="Swigon, David" sort="Swigon, David" uniqKey="Swigon D" first="David" last="Swigon">David Swigon</name><affiliation wicri:level="2"><nlm:aff id="Aff14">Department of Mathematics, University of Pittsburgh, Pittsburgh, PA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>Department of Mathematics, University of Pittsburgh, Pittsburgh</wicri:cityArea></affiliation></author><author><name sortKey="Clermont, Gilles" sort="Clermont, Gilles" uniqKey="Clermont G" first="Gilles" last="Clermont">Gilles Clermont</name><affiliation wicri:level="2"><nlm:aff id="Aff18">Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh</wicri:cityArea></affiliation></author></analytic><series><title level="j">BMC Public Health</title><idno type="eISSN">1471-2458</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Aged</term><term>Child</term><term>Child, Preschool</term><term>Epidemics</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype (immunology)</term><term>Influenza A Virus, H1N1 Subtype (isolation & purification)</term><term>Influenza, Human (epidemiology)</term><term>Middle Aged</term><term>Models, Theoretical</term><term>Pennsylvania (epidemiology)</term><term>Young Adult</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Enfant</term><term>Enfant d'âge préscolaire</term><term>Grippe humaine (épidémiologie)</term><term>Humains</term><term>Jeune adulte</term><term>Modèles théoriques</term><term>Pennsylvanie (épidémiologie)</term><term>Sous-type H1N1 du virus de la grippe A (immunologie)</term><term>Sous-type H1N1 du virus de la grippe A (isolement et purification)</term><term>Sujet âgé</term><term>Épidémies</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Pennsylvania</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="immunologie" xml:lang="fr"><term>Sous-type H1N1 du virus de la grippe A</term></keywords><keywords scheme="MESH" qualifier="immunology" xml:lang="en"><term>Influenza A Virus, H1N1 Subtype</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Influenza A Virus, H1N1 Subtype</term></keywords><keywords scheme="MESH" qualifier="isolement et purification" xml:lang="fr"><term>Sous-type H1N1 du virus de la grippe A</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Pennsylvanie</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Aged</term><term>Child</term><term>Child, Preschool</term><term>Epidemics</term><term>Humans</term><term>Middle Aged</term><term>Models, Theoretical</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Enfant</term><term>Enfant d'âge préscolaire</term><term>Humains</term><term>Jeune adulte</term><term>Modèles théoriques</term><term>Sujet âgé</term><term>Épidémies</term></keywords></textClass></profileDesc></teiHeader><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><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Murillo, Ln" uniqKey="Murillo L">LN Murillo</name></author><author><name sortKey="Murillo, Ms" uniqKey="Murillo M">MS 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uniqKey="Grefenstette J">JJ Grefenstette</name></author><author><name sortKey="Brown, St" uniqKey="Brown S">ST Brown</name></author><author><name sortKey="Rosenfeld, R" uniqKey="Rosenfeld R">R Rosenfeld</name></author><author><name sortKey="Depasse, J" uniqKey="Depasse J">J DePasse</name></author><author><name sortKey="Stone, Nt" uniqKey="Stone N">NT Stone</name></author><author><name sortKey="Cooley, Pc" uniqKey="Cooley P">PC Cooley</name></author><author><name sortKey="Wheaton, Wd" uniqKey="Wheaton W">WD Wheaton</name></author><author><name sortKey="Fyshe, A" uniqKey="Fyshe A">A Fyshe</name></author><author><name sortKey="Galloway, Dd" uniqKey="Galloway D">DD Galloway</name></author><author><name sortKey="Sriram, A" uniqKey="Sriram A">A Sriram</name></author><author><name sortKey="Guclu, H" uniqKey="Guclu H">H Guclu</name></author><author><name sortKey="Abraham, T" uniqKey="Abraham T">T Abraham</name></author><author><name sortKey="Burke, Ds" uniqKey="Burke D">DS 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