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,...

Descripción completa

Detalles Bibliográficos
Autores: 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
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
id REVURP_496fe78438b628f4c24bde1b1a84252a
oai_identifier_str oai:oai.revistas.urp.edu.pe:article/6191
network_acronym_str REVURP
network_name_str Revistas - Universidad Ricardo Palma
repository_id_str
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
instname_str Universidad Ricardo Palma
instacron_str URP
institution URP
reponame_str Revistas - Universidad Ricardo Palma
collection Revistas - Universidad Ricardo Palma
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 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
Nota importante:
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).