Comparing Time Series Predictions of COVID-19 Deaths Using SARIMAX, Neural Network, and XGBoost

Abstract

Accurate and timely prediction of the development of the Covid-19 pandemic is critical to enabling adaptive responses and supporting better decision-making in pandemic management. This research aims to evaluate the effectiveness of three prediction models—SARIMAX, Neural Network, and XGBoost—in forecasting Covid-19-related deaths worldwide. Historical data from July 1 to August 15, 2020, was utilized to develop and test these models, with performance assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The findings reveal that the XGBoost model outperformed the other two, achieving the lowest MAE value of 347.87, demonstrating its superior accuracy for short-term trend forecasting. These results highlight the strengths and limitations of various forecasting methods, offering valuable insights for future research and practical applications in epidemic prediction. The implications of this research underscore the potential of advanced predictive models in enabling faster identification and response to health threats, thus contributing to more effective epidemic control and public health preparedness.