Predicting the status of COVID-19 active cases using a neural network time series

The design of intelligent systems for analyzing information and predicting the epidemiological trends of the disease is rapidly expanding because of the coronavirus disease (COVID-19) pandemic. The COVID-19 datasets provided by Johns Hopkins University were included in the analysis. This dataset contains some missing data that is imputed using the multi-objective particle swarm optimization method. A time series model based on nonlinear autoregressive exogenou (NARX) neural network is proposed to predict the recovered and death COVID-19 cases. This model is trained and evaluated for two modes: predicting the situation of the affected areas for the next day and the next month. After training the model based on the data from January 22 to February 27, 2020, the performance of the proposed model was evaluated in predicting the situation of the areas in the coming two weeks. The error rate was less than 5%. The prediction of the proposed model for April 9, 2020, was compared with the actual data for that day. The absolute percentage error (AE) worldwide was 12%. The lowest mean absolute error (MAE) of the model was for South America and Australia with 3 and 3.3, respectively. In this paper, we have shown that geographical areas with mortality and recovery of COVID-19 cases can be predicted using a neural network-based model