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Modeling the Evolution of SARS-CoV-2 Using a Fractional-Order SIR Approach

Modelando la evolución del SARS-COV-2 usando una aproximación fraccionaria

dc.creatorQuintero, Anderson S.
dc.creatorGutiérrez-Carvajal , Ricardo E.
dc.date2021-07-12
dc.date.accessioned2021-08-19T16:21:48Z
dc.date.available2021-08-19T16:21:48Z
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1866
dc.identifier10.22430/22565337.1866
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/12078
dc.descriptionTo show the potential of non-commensurable fractional-order dynamical systems in modeling epidemiological phenomena, we will adjust the parameters of a fractional generalization of the SIR model to describe the population distributions generated by SARS-CoV-2 in France and Colombia. Despite the completely different contexts of both countries, we will see how the system presented here manages to adequately model them thanks to the flexibility provided by the fractional-order differential equations. The data for Colombia were obtained from the records published by the Colombian Ministry of Information Technology and Communications from March 24 to July 10, 2020. Those for France were taken from the information published by the Ministry of Solidarity and Health from May 1 to September 6, 2020. As for the methodology implemented in this study, we conducted an exploratory analysis focused on solving the fractional SIR model by means of the fractional transformation method. In addition, the model parameters were adjusted using a sophisticated optimization method known as the Bound Optimization BY Quadratic Approximation (BOBYQA) algorithm. According to the results, the maximum error percentage for the evolution of the susceptible, infected, and recovered populations in France was 0.05%, 19%, and 6%, respectively, while that for the evolution of the susceptible, infected, and recovered populations in Colombia was 0.003%, 19%, and 38%, respectively. This was considered for data in which the disease began to spread and human intervention did not imply a substantial change in the community.en-US
dc.descriptionCon el objetivo de exponer el potencial de los sistemas dinámicos de orden fraccionario, inconmensurables para la modelación de fenómenos epidemiológicos, en este artículo se ajustarán los parámetros de una generalización fraccionaria del modelo SIR (susceptibles, infectados y recuperados) para describir las distribuciones poblacionales generadas por el SARS-CoV-2 en Francia y Colombia, dos países cuyos contextos son totalmente diferentes. Asimismo, se mostrará cómo el sistema presentado logra describir adecuadamente los dos contextos debido a la flexibilidad proporcionada por las ecuaciones diferenciales de orden fraccionario. Los datos, para Colombia, fueron obtenidos del registro hecho por el Ministerio de Tecnologías de la Información y las Comunicaciones, considerándose las fechas del 24 de marzo del 2020 hasta el 10 de julio del mismo año. Por su parte, para Francia, los datos fueron tomados del monitoreo hecho por el Ministerio de Solidaridad y Salud, en un periodo comprendido desde el 1 de mayo de 2020 hasta el 6 de septiembre del mismo año. La metodología seguida es un análisis exploratorio centrado en la solución del modelo SIR fraccionario a partir del método de la transformación fraccionaria, ajustado mediante un plan sofisticado de optimización llamado algoritmo BOBYQA. Los resultados presentados muestran que el porcentaje de error máximo para la evolución de la población susceptible, infectada y recuperada en Francia es de 0.05 %, 19 % y 6 %, respectivamente. Mientras tanto, en Colombia se tiene un valor correspondiente de 0.003 %, 19 %, 38 %, esto para datos en los que se inició la dispersión de la enfermedad, donde la intervención humana no tuvo un cambio contundente en la comunidad.  es-ES
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dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1866/2081
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1866/2086
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1866/2087
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1866/2088
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dc.rightsCopyright (c) 2021 TecnoLógicasen-US
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0en-US
dc.sourceTecnoLógicas; Vol. 24 No. 51 (2021); e1866en-US
dc.sourceTecnoLógicas; Vol. 24 Núm. 51 (2021); e1866es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectSARS-CoV-2 modelingen-US
dc.subjectfractional calculusen-US
dc.subjectSIR model (Susceptible-Infected-Recovered)en-US
dc.subjectbiological system modelingen-US
dc.subjectModelamiento del SARS-CoV-2es-ES
dc.subjectcálculo fraccionarioes-ES
dc.subjectmodelo SIR (susceptible, infectada, recuperada)es-ES
dc.subjectmodelamiento de sistemas biológicoses-ES
dc.titleModeling the Evolution of SARS-CoV-2 Using a Fractional-Order SIR Approachen-US
dc.titleModelando la evolución del SARS-COV-2 usando una aproximación fraccionariaes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


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