The present work is focused on modeling and predicting the cumulative number of deaths from COVID-19 in México by comparing an artificial neural network (ANN) with a Gompertz model applying multiple optimization algorithms for the estimation of coefficients and parameters, respectively. For the modeling process, the data published by the daily technical report COVID-19 in Mexico from March 19th to September 30th were used. The data published in the month of October were included to carry out the prediction. The results show a satisfactory comparison between the real data and those obtained by both models with a R
2
> 0.999. The Levenberg–Marquardt and BFGS quasi-Newton optimization algorithm were favorable for fitting the coefficients during learning in the ANN model due to their fast and precision, respectively. On the other hand, the Nelder–Mead simplex algorithm fitted the parameters of the Gompertz model faster by minimizing the sum of squares. Therefore, the ANN model better fits the real data using ten coefficients. However, the Gompertz model using three parameters converges in less computational time. In the prediction, the inverse ANN model was solved by a genetic algorithm obtaining the best precision with a maximum error of 2.22% per day, as opposed to the 5.48% of the Gompertz model with respect to the real data reported from November 1st to 15th. Finally, according to the coefficients and parameters obtained from both models with recent data, a total of 109,724 cumulative deaths for the inverse ANN model and 100,482 cumulative deaths for the Gompertz model were predicted for the end of 2020.
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
2
>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.
.
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