Influenza Forecasting

Influenza forecasting aims to accurately predict the timing and severity of influenza outbreaks to improve public health preparedness and response. Current research emphasizes integrating diverse data sources (e.g., hospital admissions, outpatient visits, lab-confirmed cases) using advanced machine learning techniques such as ensemble methods (combining multiple models), gradient boosting, and random forests, as well as incorporating epidemiological knowledge into neural networks. These efforts focus on improving forecast accuracy, calibration (the reliability of uncertainty estimates), and the ability to generate probabilistic predictions, ultimately leading to better resource allocation and more effective interventions.