Modeling and analysis of Covid-19 infections in Peru

• 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

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.