Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models

Torrealba-Rodriguez, O.; Conde-Gutiérrez, R.A.; Hernández-Javier, A.L.

This work presents the modeling and prediction of cases of COVID-19 infection in Mexico through mathematical and computational models using only the confirmed cases provided by the daily technical report COVID-19 MEXICO until May 8(th). The mathematical models: Gompertz and Logistic, as well as the computational model: Artificial Neural Network were applied to carry out the modeling of the number of cases of COVID-19 infection from February 27(th) to May 8(th). The results show a good fit between the observed data and those obtained by the Gompertz, Logistic and Artificial Neural Networks models with an R(2) of 0.9998, 0.9996, 0.9999, respectively. The same mathematical models and inverse Artificial Neural Network were applied to predict the number of cases of COVID-19 infection from May 9(th) to 16(th) in order to analyze tendencies and extrapolate the projection until the end of the epidemic. The Gompertz model predicts a total of 47,576 cases, the Logistic model a total of 42,131 cases, and the inverse artificial neural network model a total of 44,245 as of May 16(th). Finally, to predict the total number of COVID-19 infected until the end of the epidemic, the Gompertz, Logistic and inverse Artificial Neural Network model were used, predicting 469,917 59,470 and 70,714 cases, respectively.