This page lists papers and archived postings developed for a project entitled Influenza Outbreak Prediction: Applying Data Assimilation Methodologies to Make Skillful Forecasts of an Inherently Chaotic, Nonlinear System, which is funded through the NIH (NIGMS)/NSF (DMS) joint initiative to support research at the interface of the biological and mathematical sciences. The title is a bit of a mouthful (as is the funding program name); basically, we are using data assimilation methods, as commonly employed in numerical weather prediction, in conjunction with real-time observations of influenza incidence to train and optimize model simulations of influenza transmission dynamics on the fly and then use those optimized models to generate real-time forecasts of influenza outcomes. Additional funding comes from the Biomedical Advanced Research and Development Authority of the Department of Health and Human Services, as well as the Models of Infectious Disease Agent Study (MIDAS) program of the the NIH.