Probabilistic solution of random SI-type epidemiological models using the Random Variable Transformation technique

[EN] This paper presents a full probabilistic description of the solution of random SI-type epidemiological models which are based on nonlinear differential equations. This description consists of determining: the first probability density function of the solution in terms of the density functions o...

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Detalles Bibliográficos
Autores: M.-C. Casabán|||0000-0002-5708-5709, Cortés, J.-C.|||0000-0002-6528-2155, Romero, José-Vicente|||0000-0003-3366-6557, Roselló, María-Dolores|||0000-0002-5724-7683
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/64197
Acceso en línea:https://riunet.upv.es/handle/10251/64197
Access Level:acceso abierto
Palabra clave:Random SI-type epidemiological models
First-order nonlinear random differential equations
Random Variable Transformation technique
First probability density function
MATEMATICA APLICADA
Descripción
Sumario:[EN] This paper presents a full probabilistic description of the solution of random SI-type epidemiological models which are based on nonlinear differential equations. This description consists of determining: the first probability density function of the solution in terms of the density functions of the diffusion coefficient and the initial condition, which are assumed to be independent random variables; the expectation and variance functions of the solution as well as confidence intervals and, finally, the distribution of time until a given proportion of susceptibles remains in the population. The obtained formulas are general since they are valid regardless the probability distributions assigned to the random inputs. We also present a pair of illustrative examples including in one of them the application of the theoretical results to model the diffusion of a technology using real data.