Forecast COVID-19 Epidemics by Strengthening Deep Learning Models with Time Series Analysis

The COVID-19 pandemic has profoundly impacted economic and social structures, directly affecting individuals’ lives. Deep learning models offer the potential to forecast future long-term trends and capture the temporal dependencies present in time series data. In this study, we propose leveraging the autocorrelation function (ACF) and the partial autocorrelation function (PACF) series as additional components to enhance the forecasting accuracy of our models. Our proposed method is applied to forecast COVID-19 time series data in twelve countries using the deep learning techniques of Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). When comparing the rankings average of mean absolute error and R-squared, the proposed models demonstrated superior performance in time series forecasting compared to the standard LSTM and GRU model. Specifically, the ACF-PACF-GRU model achieved the best median values for mean absolute percentage error (1.67 per cent for confirmed cases and 2.17 per cent for death cases) and root mean square error (1.92 for confirmed cases and 2.17 for death cases). Therefore, the proposed ACF-PACF-GRU model showed the highest performance in forecasting both confirmed and death cases. This research introduces a novel method for constructing effective time series models aimed at forecasting disease burdens, thereby aiding in epidemic control and the implementation of preventive measures.

@article{Tangseefa2025,
  title     = {Forecast COVID-19 Epidemics by Strengthening Deep Learning Models with Time Series Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {7},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Warapree Tangseefa and Tepanata Pumpaibool and Paisit Khanarsa and Krung Sinapiromsaran},
  doi       = {10.14569/IJACSA.2025.0160791},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160791}
}