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A large-scale immuno-epidemiological simulation of influenza A epidemics

BACKGROUND: 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...

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Autores principales: Lukens, Sarah, DePasse, Jay, Rosenfeld, Roni, Ghedin, Elodie, Mochan, Ericka, Brown, Shawn T, Grefenstette, John, Burke, Donald S, Swigon, David, Clermont, Gilles
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194421/
https://www.ncbi.nlm.nih.gov/pubmed/25266818
http://dx.doi.org/10.1186/1471-2458-14-1019
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author Lukens, Sarah
DePasse, Jay
Rosenfeld, Roni
Ghedin, Elodie
Mochan, Ericka
Brown, Shawn T
Grefenstette, John
Burke, Donald S
Swigon, David
Clermont, Gilles
author_facet Lukens, Sarah
DePasse, Jay
Rosenfeld, Roni
Ghedin, Elodie
Mochan, Ericka
Brown, Shawn T
Grefenstette, John
Burke, Donald S
Swigon, David
Clermont, Gilles
author_sort Lukens, Sarah
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2458-14-1019) contains supplementary material, which is available to authorized users.
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spelling pubmed-41944212014-10-14 A large-scale immuno-epidemiological simulation of influenza A epidemics Lukens, Sarah DePasse, Jay Rosenfeld, Roni Ghedin, Elodie Mochan, Ericka Brown, Shawn T Grefenstette, John Burke, Donald S Swigon, David Clermont, Gilles BMC Public Health Research Article BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2458-14-1019) contains supplementary material, which is available to authorized users. BioMed Central 2014-09-29 /pmc/articles/PMC4194421/ /pubmed/25266818 http://dx.doi.org/10.1186/1471-2458-14-1019 Text en © Lukens et al.; licensee BioMed Central Ltd. 2014 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 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Lukens, Sarah
DePasse, Jay
Rosenfeld, Roni
Ghedin, Elodie
Mochan, Ericka
Brown, Shawn T
Grefenstette, John
Burke, Donald S
Swigon, David
Clermont, Gilles
A large-scale immuno-epidemiological simulation of influenza A epidemics
title A large-scale immuno-epidemiological simulation of influenza A epidemics
title_full A large-scale immuno-epidemiological simulation of influenza A epidemics
title_fullStr A large-scale immuno-epidemiological simulation of influenza A epidemics
title_full_unstemmed A large-scale immuno-epidemiological simulation of influenza A epidemics
title_short A large-scale immuno-epidemiological simulation of influenza A epidemics
title_sort large-scale immuno-epidemiological simulation of influenza a epidemics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194421/
https://www.ncbi.nlm.nih.gov/pubmed/25266818
http://dx.doi.org/10.1186/1471-2458-14-1019
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