The focus of this ongoing work is to develop accurate and reliable ensemble forecasts of influenza and other viral respiratory disease outcomes. We use a variety of mathematical, statistical and machine learning methods for this work.
In 2014, using our first forecasting system, Jeffrey Shaman, Wan Yang, Alicia Karspeck, and Marc Lipsitch won the inaugural ‘CDC Predict the Flu Challenge’. The prize announcement can be found here:
In recent years, we have studied more fundamental questions related to the prediction of nonlinear systems and, in particular, the data needed to facilitate such predictions. The objectives are to understand and quantify the trade-off between model complexity and data richness, the levels of model complexity that could feasibly produce accurate and reliable forecasts, and the abundance, frequency and quality of data needed to make those forecasts possible.
Funding for this work was first provided by the NIH (NIGMS)/NSF (DMS) joint initiative to support research at the interface of the biological and mathematical sciences.
Over the years, additional support has come from BARDA, DTRA, NIAID, CDC and CSTE.