Version 1
: Received: 13 April 2021 / Approved: 15 April 2021 / Online: 15 April 2021 (07:44:35 CEST)
How to cite:
Perez Abreu, R.; Estrada, S.; de-la-Torre-Gutiérrez, H. A Two-Stage Polynomial and Nonlinear Growth Approach for Modeling the COVID-19 Outbreak in Mexico. Preprints2021, 2021040398. https://doi.org/10.20944/preprints202104.0398.v1
Perez Abreu, R.; Estrada, S.; de-la-Torre-Gutiérrez, H. A Two-Stage Polynomial and Nonlinear Growth Approach for Modeling the COVID-19 Outbreak in Mexico. Preprints 2021, 2021040398. https://doi.org/10.20944/preprints202104.0398.v1
Perez Abreu, R.; Estrada, S.; de-la-Torre-Gutiérrez, H. A Two-Stage Polynomial and Nonlinear Growth Approach for Modeling the COVID-19 Outbreak in Mexico. Preprints2021, 2021040398. https://doi.org/10.20944/preprints202104.0398.v1
APA Style
Perez Abreu, R., Estrada, S., & de-la-Torre-Gutiérrez, H. (2021). A Two-Stage Polynomial and Nonlinear Growth Approach for Modeling the COVID-19 Outbreak in Mexico. Preprints. https://doi.org/10.20944/preprints202104.0398.v1
Chicago/Turabian Style
Perez Abreu, R., Samantha Estrada and Héctor de-la-Torre-Gutiérrez. 2021 "A Two-Stage Polynomial and Nonlinear Growth Approach for Modeling the COVID-19 Outbreak in Mexico" Preprints. https://doi.org/10.20944/preprints202104.0398.v1
Abstract
Since December 2019, the coronavirus disease (COVID-19) has rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, the authors use statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national level in Mexico. Two types of models are proposed: 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 is used to estimate the middle and the end of the outbreak. Model selection will be performed using Vuong’s test. The proposed models show overall fit similar to predictive models (e.g. time series, and machine learning); however, the interpretation of parameters is less complex for decision-makers and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue.
Keywords
COVID-19; epidemic modeling; time series forecasting; nonlinear growth models; Prais-Winsten estimation
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
