Modeling and analysis of Covid-19 infections in Peru
Descripción del Articulo
Describe the COVID-19 pandemic in Peru, carry out mathematical statistical modeling, determine the critical time, the speed with which the pandemic developed and validate the estimated data; have characterized this research; whose objective has been to model and analyze COVID-19 infections in Peru,...
Autores: | , , , , , , , , |
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Formato: | artículo |
Fecha de Publicación: | 2023 |
Institución: | Universidad Ricardo Palma |
Repositorio: | Revistas - Universidad Ricardo Palma |
Lenguaje: | español |
OAI Identifier: | oai:oai.revistas.urp.edu.pe:article/6191 |
Enlace del recurso: | http://revistas.urp.edu.pe/index.php/Biotempo/article/view/6191 |
Nivel de acceso: | acceso abierto |
Materia: | contagions COVID-19 estimation logistic modeling Peru validation contagios estimación modelado logístico Perú validación |
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Revistas - Universidad Ricardo Palma |
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dc.title.none.fl_str_mv |
Modeling and analysis of Covid-19 infections in Peru Modelado y análisis de los contagios por Covid-19 en el Perú |
title |
Modeling and analysis of Covid-19 infections in Peru |
spellingShingle |
Modeling and analysis of Covid-19 infections in Peru Ortiz-Guizado, Julia Iraida contagions COVID-19 estimation logistic modeling Peru validation contagios COVID-19 estimación modelado logístico Perú validación |
title_short |
Modeling and analysis of Covid-19 infections in Peru |
title_full |
Modeling and analysis of Covid-19 infections in Peru |
title_fullStr |
Modeling and analysis of Covid-19 infections in Peru |
title_full_unstemmed |
Modeling and analysis of Covid-19 infections in Peru |
title_sort |
Modeling and analysis of Covid-19 infections in Peru |
dc.creator.none.fl_str_mv |
Ortiz-Guizado, Julia Iraida Marín-Machuca, Olegario Alvarado-Zambrano, Fredy Aníbal Candela-Díaz, José Eduardo Chinchay-Barragán, Carlos Enrique Alvarado-Zambrano, Ricardo Arnaldo Jáuregui-del-Águila, Luis Germán Marín-Sánchez, Ulert Rojas-Rueda, Maria del Pilar |
author |
Ortiz-Guizado, Julia Iraida |
author_facet |
Ortiz-Guizado, Julia Iraida Marín-Machuca, Olegario Alvarado-Zambrano, Fredy Aníbal Candela-Díaz, José Eduardo Chinchay-Barragán, Carlos Enrique Alvarado-Zambrano, Ricardo Arnaldo Jáuregui-del-Águila, Luis Germán Marín-Sánchez, Ulert Rojas-Rueda, Maria del Pilar |
author_role |
author |
author2 |
Marín-Machuca, Olegario Alvarado-Zambrano, Fredy Aníbal Candela-Díaz, José Eduardo Chinchay-Barragán, Carlos Enrique Alvarado-Zambrano, Ricardo Arnaldo Jáuregui-del-Águila, Luis Germán Marín-Sánchez, Ulert Rojas-Rueda, Maria del Pilar |
author2_role |
author author author author author author author author |
dc.subject.none.fl_str_mv |
contagions COVID-19 estimation logistic modeling Peru validation contagios COVID-19 estimación modelado logístico Perú validación |
topic |
contagions COVID-19 estimation logistic modeling Peru validation contagios COVID-19 estimación modelado logístico Perú validación |
description |
Describe the COVID-19 pandemic in Peru, carry out mathematical statistical modeling, determine the critical time, the speed with which the pandemic developed and validate the estimated data; have characterized this research; whose objective has been to model and analyze COVID-19 infections in Peru, and compare infected people and estimated infected people; assess the critical time in which the maximum speed of estimated infected people occurs and statistically validate the model. The data on COVID-19 infections until February 24, 2023 has been taken into account; determining that they describe a sigmoidal logistic dispersion; event that was mathematically modeled using the expression , which is a predictive logistic equation. With the predictive mathematical model, the number of people infected and their behavior of COVID-19 in Peru was estimated. Likewise, the speed of people infected with COVID-19 in Peru was evaluated. The critical time (tc) was estimated for which the speed of infected people was maximum, values that are tc=740 days and the maximum speed =6 934.9307 people/day, respectively and the date that there was the maximum speed of infections due to COVID-19 was February 28, 2022. The Pearson correlation coefficient for the time elapsed (t) and the number of infected people (N) in Peru, due to COVID-19, based on 37 cases, was r=-0.79; determining that the relationship between time and the number of infections is real, that the predictive model has a high estimate of the correlated data, that there is a “very strong correlation” between the time elapsed (t) and the number of infected people (N) and that 63% of the variance in N is explained by t. It is concluded that the logistic model can be rigorously applied to pandemic and epidemiological phenomena with high resolution and with a high degree of estimation and, it has been determined that the correlation coefficient has a "very strong negative association" between the number of infections due to COVID-19 and elapsed time in days. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-19 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://revistas.urp.edu.pe/index.php/Biotempo/article/view/6191 10.31381/biotempo.v20i2.6191 |
url |
http://revistas.urp.edu.pe/index.php/Biotempo/article/view/6191 |
identifier_str_mv |
10.31381/biotempo.v20i2.6191 |
dc.language.none.fl_str_mv |
spa |
language |
spa |
dc.relation.none.fl_str_mv |
http://revistas.urp.edu.pe/index.php/Biotempo/article/view/6191/9636 http://revistas.urp.edu.pe/index.php/Biotempo/article/view/6191/9981 |
dc.rights.none.fl_str_mv |
Derechos de autor 2023 Biotempo info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Derechos de autor 2023 Biotempo |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf text/html |
dc.publisher.none.fl_str_mv |
Facultad de Ciencias Biológicas, Universidad Ricardo Palma |
publisher.none.fl_str_mv |
Facultad de Ciencias Biológicas, Universidad Ricardo Palma |
dc.source.none.fl_str_mv |
Biotempo; Vol. 20 Núm. 2 (2023): Biotempo; 237-245 2519-5697 1992-2159 10.31381/biotempo.v20i2 reponame:Revistas - Universidad Ricardo Palma instname:Universidad Ricardo Palma instacron:URP |
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Universidad Ricardo Palma |
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URP |
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Revistas - Universidad Ricardo Palma |
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Revistas - Universidad Ricardo Palma |
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1790259308173197312 |
spelling |
Modeling and analysis of Covid-19 infections in PeruModelado y análisis de los contagios por Covid-19 en el PerúOrtiz-Guizado, Julia Iraida Marín-Machuca, Olegario Alvarado-Zambrano, Fredy Aníbal Candela-Díaz, José Eduardo Chinchay-Barragán, Carlos Enrique Alvarado-Zambrano, Ricardo Arnaldo Jáuregui-del-Águila, Luis Germán Marín-Sánchez, Ulert Rojas-Rueda, Maria del Pilar contagionsCOVID-19estimationlogistic modelingPeruvalidationcontagiosCOVID-19estimaciónmodelado logísticoPerúvalidaciónDescribe the COVID-19 pandemic in Peru, carry out mathematical statistical modeling, determine the critical time, the speed with which the pandemic developed and validate the estimated data; have characterized this research; whose objective has been to model and analyze COVID-19 infections in Peru, and compare infected people and estimated infected people; assess the critical time in which the maximum speed of estimated infected people occurs and statistically validate the model. The data on COVID-19 infections until February 24, 2023 has been taken into account; determining that they describe a sigmoidal logistic dispersion; event that was mathematically modeled using the expression , which is a predictive logistic equation. With the predictive mathematical model, the number of people infected and their behavior of COVID-19 in Peru was estimated. Likewise, the speed of people infected with COVID-19 in Peru was evaluated. The critical time (tc) was estimated for which the speed of infected people was maximum, values that are tc=740 days and the maximum speed =6 934.9307 people/day, respectively and the date that there was the maximum speed of infections due to COVID-19 was February 28, 2022. The Pearson correlation coefficient for the time elapsed (t) and the number of infected people (N) in Peru, due to COVID-19, based on 37 cases, was r=-0.79; determining that the relationship between time and the number of infections is real, that the predictive model has a high estimate of the correlated data, that there is a “very strong correlation” between the time elapsed (t) and the number of infected people (N) and that 63% of the variance in N is explained by t. It is concluded that the logistic model can be rigorously applied to pandemic and epidemiological phenomena with high resolution and with a high degree of estimation and, it has been determined that the correlation coefficient has a "very strong negative association" between the number of infections due to COVID-19 and elapsed time in days.Describir la pandemia de la COVID-19 en el Perú, realizar un modelamiento estadístico matemático, determinar el tiempo crítico, la velocidad con que se desarrolló la pandemia y validar de los datos estimados; han caracterizado esta investigación; cuyo objetivo ha sido modelar y analizar los contagios por COVID-19 en el Perú, y comparar las personas contagiadas y las personas estimadas contagiadas; valorar el tiempo crítico en la que se produce la velocidad máxima de personas estimadas contagiadas y validar estadísticamente el modelo. Se ha tomado en cuenta los datos de contagios por la COVID-19 hasta el veinticuatro de febrero del 2023; llegando a determinar que describen una dispersión logística sigmoidal; suceso que fue modelado matemáticamente mediante la expresión , que es una ecuación logística predictora. Con el modelo matemático predictivo se estimó el número de personas contagiadas y su comportamiento de la COVID-19 en el Perú. De igual forma se evaluó la velocidad de las personas contagiadas con la COVID-19 en el Perú. Se estimó el tiempo crítico ( ) para la cual la velocidad de personas contagiadas fue máxima, valores que son y la velocidad máxima , respectivamente y la fecha que hubo la máxima velocidad de contagios por la COVID-19, fue el 28 de febrero del 2022. El coeficiente de correlación de Pearson para el tiempo transcurrido ( ) y el número de personas contagiadas ( ) en el Perú, por la COVID-19, basado en 37 casos, fue de ; determinando que la relación entre el tiempo y el número de contagios, es real, que el modelo predictivo tiene alta estimación de los datos correlacionados, que existe una “correlacion muy fuerte” entre el tiempo transcurrido ( ) y el número de personas contagiadas ( ) y que el 63 % de la variancia en es explicada por . Se concluye que el modelo logístico se puede aplicar con rigurosidad a fenómenos pandémicos y epidemiológicos con alta resolución y con alto grado de estimación y, se ha determinado que el coeficiente de correlación tiene una “asociación negativa muy fuerte” entre el número de contagios por la COVID-19 y el tiempo transcurrido en días.Facultad de Ciencias Biológicas, Universidad Ricardo Palma2023-12-19info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlhttp://revistas.urp.edu.pe/index.php/Biotempo/article/view/619110.31381/biotempo.v20i2.6191Biotempo; Vol. 20 Núm. 2 (2023): Biotempo; 237-2452519-56971992-215910.31381/biotempo.v20i2reponame:Revistas - Universidad Ricardo Palmainstname:Universidad Ricardo Palmainstacron:URPspahttp://revistas.urp.edu.pe/index.php/Biotempo/article/view/6191/9636http://revistas.urp.edu.pe/index.php/Biotempo/article/view/6191/9981Derechos de autor 2023 Biotempoinfo:eu-repo/semantics/openAccessoai:oai.revistas.urp.edu.pe:article/61912024-01-29T23:42:46Z |
score |
13.894945 |
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).