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A Two-Step Polynomial and Nonlinear Growth Approach for Modeling COVID-19 Cases in Mexico
Rafael Alberto Pérez Abreu Carrión
Samantha Estrada
HECTOR DE LA TORRE GUTIERREZ
christophe chesneau
András Nábrádi
SÁNDOR J. KOVÁCS
Acceso Abierto
Atribución
https://doi.org/10.3390/math9182180
Since December 2019, the novel coronavirus (SARS-CoV-2) and its associated illness COVID-19 have rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, we used statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national levels in Mexico. In this paper, we propose two types of models. First, a polynomial model of the growth for the first part of the outbreak until the inflection point of the pandemic curve and then a second nonlinear growth model used to estimate the middle and the end of the outbreak. Model selection was performed using Vuong’s test. The proposed models showed overall fit similar to predictive models (e.g., time series and machine learning); however, the interpretation of parameters is simpler for decisionmakers, and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue.
Mathematics
07-09-2021
Artículo
https://www.mdpi.com/
Inglés
Pérez Abreu C., R.; Estrada, S.; de-la-Torre-Gutiérrez, H. A Two-Step Polynomial and Nonlinear Growth Approach for Modeling COVID-19 Cases in Mexico. Mathematics 2021, 9, 2180. https:// doi.org/10.3390/math9182180
VIRUS RESPIRATORIOS
Versión publicada
publishedVersion - Versión publicada
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