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Analysis of a nonstandard computer method to simulate a nonlinear stochastic epidemiological model of coronavirus-like diseases.
Macías-Díaz, J E; Raza, Ali; Ahmed, Nauman; Rafiq, Muhammad.
Afiliación
  • Macías-Díaz JE; Department of Mathematics, School of Digital Technologies, Tallinn University, Narva Rd. 25, 10120 Tallinn, Estonia; Departamento de Matemáticas y Física, Universidad Autónoma de Aguascalientes, Avenida Universidad 940, Ciudad Universitaria, Aguascalientes 20131, Mexico. Electronic address: jemacias
  • Raza A; Department of Mathematics, National College of Business Administration and Economics Lahore, Pakistan. Electronic address: alimustasamcheema@gmail.com.
  • Ahmed N; Department of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan. Electronic address: nauman.ahmed@math.uol.edu.pk.
  • Rafiq M; Department of Mathematics, Faculty of Sciences, University of Central Punjab, Lahore, Pakistan. Electronic address: m.rafiq@ucp.edu.pk.
Comput Methods Programs Biomed ; 204: 106054, 2021 Jun.
Article en En | MEDLINE | ID: mdl-33813059
BACKGROUND AND OBJECTIVE: We propose a nonstandard computational model to approximate the solutions of a stochastic system describing the propagation of an infectious disease. The mathematical model considers the existence of various sub-populations, including humans who are susceptible to the disease, asymptomatic humans, infected humans and recovered or quarantined individuals. Various mechanisms of propagation are considered in order to describe the propagation phenomenon accurately. METHODS: We propose a stochastic extension of the deterministic model, considering a random component which follows a Brownian motion. In view of the difficulties to solve the system exactly, we propose a computational model to approximate its solutions following a nonstandard approach. RESULTS: The nonstandard discretization is fully analyzed for positivity, boundedness and stability. It is worth pointing out that these properties are realized in the discrete scenario and that they are thoroughly established herein using rigorous mathematical arguments. We provide some illustrative computational simulations to exhibit the main computational features of this approach. CONCLUSIONS: The results show that the nonstandard technique is capable of preserving the distinctive characteristics of the epidemiologically relevant solutions of the model, while other (classical) approaches are not able to do it. For the sake of convenience, a computational code of the nonstandard discrete model may be provided to the readers at their requests.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Coronavirus Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Comput Methods Programs Biomed Asunto de la revista: INFORMATICA MEDICA Año: 2021 Tipo del documento: Article Pais de publicación: Irlanda
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Coronavirus Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Comput Methods Programs Biomed Asunto de la revista: INFORMATICA MEDICA Año: 2021 Tipo del documento: Article Pais de publicación: Irlanda