Prediction of deaths from COVID-19 with the modified logistic model, in Peru

Olegario Marín Machuca (), Jessica Blanca Vargas Ayala (), Luis Adolfo Pérez Ton (), Carlos Enrique Chinchay Barragán (), María del Pilar Rojas Rueda (), Max Alejandro Huaranja Montaño () and Fernando Antonio Sernaqué Auccahuasi ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 1, 381-392

Abstract: COVID-19 is a public health millions of deaths since the end problem that has had an international impact that has led to of 2019, and the Peruvian population was no stranger to this situation. Therefore, the following investigation was conducted to correlate mortality from COVID-19, estimate the critical time (days) for the maximum rate of estimated deceased people, and validate the reliability of the models. Data on people who died from COVID-19 up to February 27, 2023, were considered, with which the pandemic dispersion was carried out, arriving to determine that they describe a sigmoidal logistic dispersion, an event that was mathematically modeled using the predictive logistic equation N=M⁄((1+A×e^(-k×t))). Using this predictive mathematical model, the number and rate of deaths among people with COVID-19 in Peru were determined. In addition, the critical time (t_c) was estimated, whose value was t_c=396 days for the maximum rate 〖((dN ̂)⁄dt)〗_máx=484.7450 people/day, and the date on which the maximum rate of people who died from COVID-19 was April 15, 2021. The Pearson correlation coefficient between the time elapsed (t) and the number of deceased people (N) in Peru, based on 32 cases, turned out to be r=-0.89085; determining that the relationship is real, that there is a non-significant difference, 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 deceased people (N), and that 79.4% of the variance in N is explained by t; for people who died from COVID-19 in Peru.

Keywords: COVID-19; Deceased in Peru; Estimate; Logistics modeling; Validation. (search for similar items in EconPapers)
Date: 2025
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