Jornet-Sanz, M.; Corberán-Vallet, A.; Santonja, F.; Villanueva Micó, RJ. (2017). A Bayesian stochastic SIRS model with a vaccination strategy for the analysis of respiratory syncytial virus. SORT. Statistics and Operations Research Transactions. 41(1):159-175. https://doi.org/10.2436/20.8080.02.56
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/149724
Title:
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A Bayesian stochastic SIRS model with a vaccination strategy for the analysis of respiratory syncytial virus
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Author:
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Jornet-Sanz, Marc
Corberán-Vallet, Ana
Santonja, F.
Villanueva Micó, Rafael Jacinto
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UPV Unit:
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Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
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Issued date:
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Abstract:
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[EN] Our objective in this paper is to model the dynamics of respiratory syncytial virus in the region of Valencia (Spain) and analyse the effect of vaccination strategies from a health-economic point of view. Compartmental ...[+]
[EN] Our objective in this paper is to model the dynamics of respiratory syncytial virus in the region of Valencia (Spain) and analyse the effect of vaccination strategies from a health-economic point of view. Compartmental mathematical models based on differential equations are commonly used in epidemiology to both understand the underlying mechanisms that influence disease transmission and analyse the impact of vaccination programs. However, a recently proposed Bayesian stochastic susceptible-infected-recovered-susceptible model in discrete-time provided an improved and more natural description of disease dynamics. In this work, we propose an extension of that stochastic model that allows us to simulate and assess the effect of a vaccination strategy that consists on vaccinating a proportion of newborns.
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Subjects:
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Infectious diseases
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Respiratory syncytial virus (RSV)
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Discrete-time epidemic model
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Stochastic compartmental model
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Bayesian analysis
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Intervention strategies
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Copyrigths:
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Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
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Source:
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SORT. Statistics and Operations Research Transactions. (issn:
1696-2281
)
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DOI:
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10.2436/20.8080.02.56
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Publisher:
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Institut d'Estadística de Catalunya (Idescat)
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Publisher version:
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https://doi.org/10.2436/20.8080.02.56
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Project ID:
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info:eu-repo/grantAgreement/MINECO//MTM2014-56233-P/ES/PREDICCION Y OPTIMIZACION BAJO INCERTIDUMBRE: MODELOS ESTOCASTICOS DINAMICOS Y APLICACIONES/
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Thanks:
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This work has been supported by Grant Number MTM2014-56233-P from the Spanish Ministry of Economy and Competitiveness.
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Type:
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Artículo
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