Flusion: Integrating multiple data sources for accurate influenza predictions

• Published in: Epidemics Vol. 50; p. 100810
• Main Authors: Ray, Evan L., Wang, Yijin, Wolfinger, Russell D., Reich, Nicholas G.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.03.2025; Elsevier
• Subjects: Bayes Theorem; Centers for Disease Control and Prevention, U.S; Forecasting; Forecasting - methods; Gradient boosting; Hospitalization - statistics & numerical data; Humans; Infectious Disease; Influenza, Human - epidemiology; Information Sources; Internal Medicine; Machine Learning; Models, Statistical; Population Surveillance - methods; Seasons; Transfer learning; United States - epidemiology; United States; Infectious disease; Transfer learning; Forecasting; Gradient boosting

Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the motivation that accurate probabilistic forecasts could improve situational awareness and yield more effective public health actions. Starting with the 202...