| dc.description.abstract | 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 8th. 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 27th to May 8th. The results show a good fit between the
observed data and those obtained by the Gompertz, Logistic and Artificial Neural Networks models with
an R2 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 9th to 16th 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 16th. 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. | |
