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Closed-form probability distribution of number of infections at a given time in a stochastic SIS epidemic model

We study the effects of external fluctuations in the transmission rate of certain diseases and how these affect the distribution of the number of infected individuals over time. To do this, we introduce random noise in the transmission rate in a deterministic SIS model and study how the number of in...

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Autor principal: Otunuga, Olusegun Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819802/
https://www.ncbi.nlm.nih.gov/pubmed/31687591
http://dx.doi.org/10.1016/j.heliyon.2019.e02499
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author Otunuga, Olusegun Michael
author_facet Otunuga, Olusegun Michael
author_sort Otunuga, Olusegun Michael
collection PubMed
description We study the effects of external fluctuations in the transmission rate of certain diseases and how these affect the distribution of the number of infected individuals over time. To do this, we introduce random noise in the transmission rate in a deterministic SIS model and study how the number of infections changes over time. The objective of this work is to derive and analyze the closed form probability distribution of the number of infections at a given time in the resulting stochastic SIS epidemic model. Using the Fokker-Planck equation, we reduce the differential equation governing the number of infections to a generalized Laguerre differential equation. The properties of the distribution, together with the effect of noise intensity, are analyzed. The distribution is demonstrated using parameter values relevant to the transmission dynamics of influenza in the United States.
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spelling pubmed-68198022019-11-04 Closed-form probability distribution of number of infections at a given time in a stochastic SIS epidemic model Otunuga, Olusegun Michael Heliyon Article We study the effects of external fluctuations in the transmission rate of certain diseases and how these affect the distribution of the number of infected individuals over time. To do this, we introduce random noise in the transmission rate in a deterministic SIS model and study how the number of infections changes over time. The objective of this work is to derive and analyze the closed form probability distribution of the number of infections at a given time in the resulting stochastic SIS epidemic model. Using the Fokker-Planck equation, we reduce the differential equation governing the number of infections to a generalized Laguerre differential equation. The properties of the distribution, together with the effect of noise intensity, are analyzed. The distribution is demonstrated using parameter values relevant to the transmission dynamics of influenza in the United States. Elsevier 2019-09-23 /pmc/articles/PMC6819802/ /pubmed/31687591 http://dx.doi.org/10.1016/j.heliyon.2019.e02499 Text en Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Otunuga, Olusegun Michael
Closed-form probability distribution of number of infections at a given time in a stochastic SIS epidemic model
title Closed-form probability distribution of number of infections at a given time in a stochastic SIS epidemic model
title_full Closed-form probability distribution of number of infections at a given time in a stochastic SIS epidemic model
title_fullStr Closed-form probability distribution of number of infections at a given time in a stochastic SIS epidemic model
title_full_unstemmed Closed-form probability distribution of number of infections at a given time in a stochastic SIS epidemic model
title_short Closed-form probability distribution of number of infections at a given time in a stochastic SIS epidemic model
title_sort closed-form probability distribution of number of infections at a given time in a stochastic sis epidemic model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819802/
https://www.ncbi.nlm.nih.gov/pubmed/31687591
http://dx.doi.org/10.1016/j.heliyon.2019.e02499
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