Predictive modelling of COVID-19 New Confirmed Cases in Algeria using Artificial Neural Network

Abstract

This study investigates the potential of a simple artificial neural network for the prediction of COVID-19 New Confirmed Cases in Algeria (CNCC). Four different ANN models were built (GRNN, RBFNN, ELM, and MLP). The performance of the predictive models is evaluated based on four numerical parameters, namely root mean squared error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and Pearson correlation coefficient (R). Taylor diagram was also used to examine the similarities and differences between the observed and predicted values obtained from the proposed models. The results showed the potential of the multi-layer perceptron neural network (MLPNN) which exhibited a high level of accuracy in comparison to the other models.

Extreme Learning Machine Algorithms for Prediction of Positive Rate in Covid-19: A Comparative Study

“…[9] aimed to predict the prevalence of Covid-19 in Nigeria using a linear regression model. Djeddou et al [10] used four different artificial neural network models to model newly confirmed cases in Algeria. Achterberg et.…”