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

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.

Predictive models for COVID-19 cases, deaths and recoveries in Algeria.

This study was conducted to predict the number of COVID-19 cases, deaths and recoveries using reported data by the Algerian Ministry of health from February 25, 2020 to January 10, 2021. Four models were compared including Gompertz model, logistic model, Bertalanffy model and inverse artificial neural network (ANNi). Results showed that all the models showed a good fit between the predicted and the real data (R2>0.97). In this study, we demonstrate that obtaining a good fit of real data is not directly related to a good prediction efficiency with future data. In predicting cases, the logistic model obtained the best precision with an error of 0.92% compared to the rest of the models studied. In deaths, the Gompertz model stood out with a minimum error of 1.14%. Finally, the ANNi model reached an error of 1.16% in the prediction of recovered cases in Algeria. .