Artificial Neural Networks for COVID-19 Forecasting in Mexico: An Empirical Study

Artificial Neural Networks (ANN) have encountered interesting applications in forecasting several phenomena, and they have recently been applied in understanding the evolution of the novel coronavirus COVID-19 epidemic. Alone or together with other mathematical, dynamical, and statistical methods, ANN help to predict or model the transmission behavior at a global or regional level, thus providing valuable information for decision-makers. In this research, four typical ANN have been used to analyze the historical evolution of COVID-19 infections in Mexico: Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LTSM) neural networks, and the hybrid approach LTSM-CNN. From the open-source data of the Resource Center at the John Hopkins University of Medicine, a comparison of the overall qualitative fitting behavior and the analysis of quantitative metrics were performed. Our investigation shows that LSTM-CNN achieves the best qualitative performance; however, the CNN model reports the best quantitative metrics achieving better results in terms of the Mean Squared Error and Mean Absolute Error. The latter indicates that the long-term learning of the hybrid LSTM-CNN method is not necessarily a critical aspect to forecast COVID-19 cases as the relevant information obtained from the features of data by the classical MLP or CNN.