Advanced time complexity analysis for real-time COVID-19 prediction in Saudi Arabia using LightGBM and XGBoost

Abstract

This study addresses the critical need for predictive models that balance high accuracy with computational efficiency, a requirement essential for timely and effective public health responses to COVID-19 outbreaks. We evaluate the performance of two advanced machine learning models, LightGBM and XGBoost, in predicting COVID-19 case trends across five Saudi cities. Using time-series data, we analyze key metrics such as RMSE, MAE, MAPE, R², and computation time to assess each model's suitability for real-time applications. The findings highlight XGBoost's superior performance in computational speed, being up to three times faster than LightGBM in certain cases, making it ideal for rapid decision-making scenarios. Meanwhile, LightGBM demonstrates competitive accuracy and exceptional scalability, positioning it as a reliable tool for managing large datasets. These insights underscore the importance of time complexity as a critical factor in predictive modeling, enabling public health organizations to allocate resources efficiently, implement containment strategies promptly, and develop agile responses to future pandemics.