Short term prediction of COVID-19 cases by using various types of neural network model

Abstract

Coronavirus disease 2019 (Covid-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The increasing number of positive cases caused by this virus in an area or country is suspected to form a certain pattern. The pattern of growth is thought to follow certain statistical distributions or model. In this research, the three types of neural network model are used to predict the number of Covid-19 cases in Indonesia. The types are Feed Forward Neural Network (FFNN), Cascade Forward Neural Network (CFNN), General Regression Neural Network (GRNN) and Recurrent Neural Network (RNN). The pattern of adding cases which always increases continuously makes the data pattern not easy to predict in the long term. In this study, repeated short-term predictions were carried out. In-sample predictions are repeated after new data are obtained, and so are out-sample predictions. The results show that the out-sample predictions of the three types of neural network are always under the actual value for each repetition. This condition is of course very worrying because the cases have a high possibility to increase more sharply than expected. However, the CFNN as the only type which giving a positive and negative variations of Mean Percentage Error (MPE), is the best model with the smallest error.