Abstract
Currently, the COVID-19 infection has caused a global pandemic, affecting the population of many countries on all continents. In this paper, we propose a model based on a convolutional neural network (CNN)—long short-term memory (LSTM) with the ability to predict the probable number of new confirmed COVID-19 cases for the next few days. In the testing phase, the CNN-LSTM model automatically and sequentially inserts the value of confirmed cases for two consecutive days, predicting the value of cases that will occur the next day (third day). This process is done repeatedly to form the time series of a complete forecast by country. In our experiment, a time series dataset was built to train and test the proposed deep learning inference model from available datasets with real data on the number of confirmed cases, deaths, and recovered cases caused for the transmission of COVID-19 from five countries. The precision results obtained by the CNN-LSTM model are acceptable, reporting values of minimum root-mean-squared error (RMSE) of 1401.33, mean absolute error (MAE) of 1027.42, mean absolute percentage error (MAPE) of 0.39, and coefficient of determination (R2) of 0.99, according to the scenarios analyzed. Finally, the CNN-LSTM model provides a promising method for predicting the time series of confirmed COVID-19 cases, as evidenced by the satisfactory performance of the number of new confirmed cases for the five scenarios analyzed.
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Ramirez-Alcocer, U.M., Tello-Leal, E., Hernandez-Resendiz, J.D., Macias-Hernandez, B.A. (2022). A Deep Learning Model for Early Prediction of COVID-19 Spread. In: Pandit, M., Gaur, M.K., Rana, P.S., Tiwari, A. (eds) Artificial Intelligence and Sustainable Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-1653-3_41
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