  Timely and accurate forecasts of seasonal influenza would assist public
health decision-makers in planning intervention strategies, efficiently
allocating resources, and possibly saving lives. For these reasons, influenza
forecasts are consequential. Producing timely and accurate influenza forecasts,
however, have proven challenging due to noisy and limited data, an incomplete
understanding of the disease transmission process, and the mismatch between the
disease transmission process and the data-generating process. In this paper, we
introduce a dynamic Bayesian (DB) flu forecasting model that exploits model
discrepancy through a hierarchical model. The DB model allows forecasts of
partially observed flu seasons to borrow discrepancy information from
previously observed flu seasons. We compare the DB model to all models that
competed in the CDC's 2015--2016 flu forecasting challenge. The DB model
outperformed all models, indicating the DB model is a leading influenza
forecasting model.
