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Objective: This research study proposes the use of the polynomial regression approach to model data behavior for characterizing behavior patterns for COVID-19 case, death, and recovery curves in Colombia. Methodology: Data were obtained from the reports provided by the Colombian Ministry of Health. First, the authors assessed the polynomial regression procedure through multiple linear regressions. Then, a tool was designed and implemented for applying this procedure on the collected dataset. Finally, the corresponding results were assessed. Results: For the three studies considered (cases, deaths, and recoveries), 20 polynomials were obtained together with the root mean square error (RMSE) and the determination level (R2) associated with each study. Further, a set of predictions was generated based on the linear regressions obtained for each study. Conclusions: Because polynomials with future values are volatile, polynomial equations have proven more useful when assessing COVID-19 curves up to the data capture date. Therefore, they can be used to determine the impact of government measures over a given period of time. In addition, the machine learning libraries provided in Python language significantly supported the estimation of polynomial regressions. ; Objetivo: Caracterizar el comportamiento de las curvas de casos, muertes y personas recuperadas por el COVID-19 en Colombia, en esta investigación se propuso como aporte el uso del enfoque de regresión polinomial para el modelar el comportamiento de los datos. Metodología: Se obtuvieron los datos a partir de los reportes proporcionados por el Ministerio de Salud de Colombia. En primera instancia, se estudió el procedimiento empleado para la obtención de la regresión polinomial mediante la regresión lineal múltiple, en segunda instancia, se diseñó e implementó una herramienta para la aplicación del procedimiento estudiado sobre el conjunto de datos recolectado, y finalmente, se realizó el análisis de los resultados. Resultados: Se obtuvo para los tres estudios considerados (casos, muertes y personas recuperadas) 20 polinomios en conjunto con el error medio cuadrático (RMSE) y el nivel de determinación (R2) asociados a cada uno. Así mismo, se obtuvo un conjunto de predicciones basadas en las regresiones lineales obtenidas para cada estudio. Conclusiones: La volatilidad de los polinomios con valores futuros, las ecuaciones polinómicas son más útiles para evaluar el comportamiento de la curva del COVID-19 hasta el día de captura de los datos, it means, pueden ser usadas para determinar el impacto de las medidas gubernamentales en un periodo determinado de tiempo. Igualmente, las librerías de machine learning provistas por el lenguaje Python, demostraron ser de gran apoyo a la estimación de la regresión polinomial.

Introduction: With the growing demand for applications in the market, companies have increasingly focused on meeting the technological needs of the average user, often overlooking those with functional diversity. Consequently, accessibility has emerged as a critical challenge across various contexts, particularly within the educational sphere, where the diverse range of individuals accessing web content is particularly pronounced. Objective: This paper presents a study centered on the accessibility inspection of six university websites located in the city of Cartagena. The primary aim is to ascertain the relationship between the integration of accessibility guidelines and web positioning factors as determined by Webometrics. Methodology: The methodology comprises five distinct phases: website selection, tool selection for inspection, execution of accessibility inspection, analysis of obtained results, and formulation of conclusions and recommendations. Results: The study revealed a correlation exceeding 0.8 between the local ranking of universities according to Webometrics and the ranking of accessibility issues identified through automated inspection tools. Conclusions: It is recommended that educational institutions adopt automatic web evaluation tools to enhance the accessibility of their institutional websites. This regular review and rectification of issues represent best practices for improving both local and international visibility and usability.