Computational modeling of fractional COVID-19 model by Haar wavelet collocation Methods with real data.
Math Biosci Eng
; 20(6): 11281-11312, 2023 Apr 26.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2327329
ABSTRACT
This study explores the use of numerical simulations to model the spread of the Omicron variant of the SARS-CoV-2 virus using fractional-order COVID-19 models and Haar wavelet collocation methods. The fractional order COVID-19 model considers various factors that affect the virus's transmission, and the Haar wavelet collocation method offers a precise and efficient solution to the fractional derivatives used in the model. The simulation results yield crucial insights into the Omicron variant's spread, providing valuable information to public health policies and strategies designed to mitigate its impact. This study marks a significant advancement in comprehending the COVID-19 pandemic's dynamics and the emergence of its variants. The COVID-19 epidemic model is reworked utilizing fractional derivatives in the Caputo sense, and the model's existence and uniqueness are established by considering fixed point theory results. Sensitivity analysis is conducted on the model to identify the parameter with the highest sensitivity. For numerical treatment and simulations, we apply the Haar wavelet collocation method. Parameter estimation for the recorded COVID-19 cases in India from 13 July 2021 to 25 August 2021 has been presented.
Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
COVID-19
Tipo de estudio:
Estudio observacional
Tópicos:
Variantes
Límite:
Humanos
Idioma:
Inglés
Revista:
Math Biosci Eng
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
Mbe.2023500
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