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Stochastic modeling of influenza spread dynamics with recurrences

We present results of a study of a simple, stochastic, agent-based model of influenza A infection, simulating its dynamics over the course of one flu season. Building on an early work of Bartlett, we define a model with a limited number of parameters and rates that have clear epidemiological interpr...

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Detalles Bibliográficos
Autores principales: Whitman, John, Jayaprakash, Ciriyam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7173783/
https://www.ncbi.nlm.nih.gov/pubmed/32315318
http://dx.doi.org/10.1371/journal.pone.0231521
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author Whitman, John
Jayaprakash, Ciriyam
author_facet Whitman, John
Jayaprakash, Ciriyam
author_sort Whitman, John
collection PubMed
description We present results of a study of a simple, stochastic, agent-based model of influenza A infection, simulating its dynamics over the course of one flu season. Building on an early work of Bartlett, we define a model with a limited number of parameters and rates that have clear epidemiological interpretation and can be constrained by data. We demonstrate the occurrence of recurrent behavior in the infected number [more than one peak in a season], which is observed in data, in our simulations for populations consisting of cohorts with strong intra- and weak inter-cohort transmissibility. We examine the dependence of the results on epidemiological and population characteristics by investigating their dependence on a range of parameter values. Finally, we study infection with two strains of influenza, inspired by observations, and show a counter-intuitive result for the effect of inoculation against the strain that leads to the first wave of infection.
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spelling pubmed-71737832020-04-27 Stochastic modeling of influenza spread dynamics with recurrences Whitman, John Jayaprakash, Ciriyam PLoS One Research Article We present results of a study of a simple, stochastic, agent-based model of influenza A infection, simulating its dynamics over the course of one flu season. Building on an early work of Bartlett, we define a model with a limited number of parameters and rates that have clear epidemiological interpretation and can be constrained by data. We demonstrate the occurrence of recurrent behavior in the infected number [more than one peak in a season], which is observed in data, in our simulations for populations consisting of cohorts with strong intra- and weak inter-cohort transmissibility. We examine the dependence of the results on epidemiological and population characteristics by investigating their dependence on a range of parameter values. Finally, we study infection with two strains of influenza, inspired by observations, and show a counter-intuitive result for the effect of inoculation against the strain that leads to the first wave of infection. Public Library of Science 2020-04-21 /pmc/articles/PMC7173783/ /pubmed/32315318 http://dx.doi.org/10.1371/journal.pone.0231521 Text en © 2020 Whitman, Jayaprakash http://creativecommons.org/licenses/by/4.0/ 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 author and source are credited.
spellingShingle Research Article
Whitman, John
Jayaprakash, Ciriyam
Stochastic modeling of influenza spread dynamics with recurrences
title Stochastic modeling of influenza spread dynamics with recurrences
title_full Stochastic modeling of influenza spread dynamics with recurrences
title_fullStr Stochastic modeling of influenza spread dynamics with recurrences
title_full_unstemmed Stochastic modeling of influenza spread dynamics with recurrences
title_short Stochastic modeling of influenza spread dynamics with recurrences
title_sort stochastic modeling of influenza spread dynamics with recurrences
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7173783/
https://www.ncbi.nlm.nih.gov/pubmed/32315318
http://dx.doi.org/10.1371/journal.pone.0231521
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