Increasingly, models that borrow from the methods developed to forecast weather and climate have been applied to infectious disease forecasting. Rather than condition on a single model, "ensemble forecasting" integrates the projections of many candidate models (see Shaman 2013 for influenza, and Lindström 2015 for agricultural disease). The ensemble projection reflects a weighted average of the individual models, with weights based on their ability to retrospectively predict a common, previously observed dataset, and . While formal prediction is still a long way from being of practical utility, dynamic models have been a critical tool in assessing the potential impact of candidate interventions. In the next section, we illustrate several case studies where dynamic models have influenced planning and evaluation of infectious disease control.