Optimizing broad learning system hyper-parameters through particle swarm optimization for predicting COVID-19 in 184 Countries

Abstract

The Coronavirus Disease 2019 (COVID-19) began to outbreak since December 2019 and widely spread over the world. How to accurately predict the spread of COVID-19 is one of the essential issues for controlling the pandemic. This study establishes a general model that can predict the trend of COVID-19 in a country based on historical COVID-19 data in 184 countries. First, Savitzky-Golay (S-G) filter is utilized to detect multiple waves of COVID-19 in a country. Then, a PSO-SIR (particle swarm optimization susceptible-infected-recovery) model is provided for data augmentation. Finally, a novel PSO-BLS (particle swarm optimization broad learning system) is proposed for predicting the trend of COVID-19. Experimental results show that compared with the deep learning models (ANN, CNN, LSTM, and GRU), the PSO-BLS algorithm has higher accuracy and stability in predicting the number of active infected cases and removed cases.