Time series analysis and prediction of the trends of COVID-19 epidemic in Singapore based on machine learning - Wenbin Yang; Xin Chang (Computer Methods and Programs in Biomedicine Update)
The COVID-19 pandemic has posed a significant threat to global health, with ongoing rises in new cases and deaths in Singapore, profoundly affecting public health, social activities, and the economy. This study compares the performance of LSTM, GRU, and a composite prediction model (LSTM-GRU) using a dataset of new and cumulative COVID-19 cases in Singapore, provided by the World Health Organization. The analysis uses weekly cumulative data from 2020 to January 21, 2024, to forecast new cases for the upcoming weeks. Model performance is evaluated using RMSE, MAE, MAPE, and R2. The results show that the LSTM model outperforms others, particularly in capturing significant data fluctuations. This research provides insights into the trends of the pandemic in Singapore and offers a basis for further epidemiological control efforts in the region.