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 (R>0.

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 8. 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 to May 8. 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 of 0.