Machine Learning-Based Prediction of Seasonal Influenza Trends in Saudi Arabia: A Tool for Regional Public Health Planning - Shokeralla, Alshaikh A.; El Guma, Fathelrhman; Abdalla, Ali H.; Hagsddig, Amal E.Y.; Musa, Rahma AbuBakr; Eltaweel, M.A.M.; Elawad, Abdelaziz H.; Elshamy, Ibrahim (International Journal of Statistics in Medical Research, 2025)
Influenza has continued to be a worldwide social health problem, especially in high-density population areas with minimal early-warning mechanisms. The present study evaluates the predictive ability of two machine learning models Support Vector Regression (SVR) and Random Forest (RF) to predict weekly influenza cases in Saudi Arabia, spanning from 2017 to 2022, on 313 weekly influenza records in the WHO Global Influenza Surveillance and Response System (GISRS). The performance of these models was measured with R², MAE, MSE, and RMSE. Although SVR had a better training accuracy (R² = 0.96), RF had a better generalization (R² = 0.818) and more consistent predictions at the peaks of the seasons. These observations show that RF is appropriate to real-time influenza surveillance and can provide a replicable and versatile framework to assist data-driven epidemic preparedness in Saudi Arabia and other similar contexts throughout MENA and Asia-Pacific.