Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models

This study investigates the predictive utility of Google search queries for forecasting influenza-like illness (ILI) in compulsory education schools in Macau. The increasing availability of online data offers a novel approach to health surveillance, potentially improving the timeliness and accuracy of ILI predictions in educational settings. We employed three machine learning models: extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and ridge regression (Ridge), to forecast the ILI-caused absence rate in kindergartens, primary schools, and middle schools one and two weeks in advance in Macau. The covariates for these models include Google search queries and historical ILI data. This is Macau's first study to apply machine learning methodologies and Internet big data to the surveillance of ILI in educational institutions. Our approach offers a feasible and computationally efficient method for forecasting ILI in Macau's compulsory education schools. This methodology could be adapted for use in other regions with limited influenza data resources, providing a valuable tool for public health planning and response.