Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization

COVID-19 virus has encountered people in the world with numerous problems. Given the negative impacts of COVID-19 on all aspects of people's lives, especially health and economy, accurately forecasting the number of cases infected with this virus can help governments to make accurate decisions on the interventions that must be taken. In this study, we propose three hybrid approaches for forecasting COVID-19 time series methods based on combining three deep learning models such as multi-head attention, long short-term memory (LSTM), and convolutional neural network (CNN) with the Bayesian optimization algorithm. All models are designed based on the multiple-output forecasting strategy, which allows the forecasting of the multiple time points. The Bayesian optimization method automatically selects the best hyperparameters for each model and enhances forecasting performance. Using the publicly available epidemical data acquired from Johns Hopkins University's Coronavirus Resource Center, we conducted our experiments and evaluated the proposed models against the benchmark model. The results of experiments exhibit the superiority of the deep learning models over the benchmark model both for short-term forecasting and long-horizon forecasting. In particular, the mean SMAPE of the best deep learning model is 0.25 for the short-term forecasting (10 days ahead). Also, for long-horizon forecasting, the best deep learning model obtains the mean SMAPE of 2.59.