  Accurate and reliable predictions of infectious disease dynamics can be
valuable to public health organizations that plan interventions to decrease or
prevent disease transmission. A great variety of models have been developed for
this task, using different model structures, covariates, and targets for
prediction. Experience has shown that the performance of these models varies;
some tend to do better or worse in different seasons or at different points
within a season. Ensemble methods combine multiple models to obtain a single
prediction that leverages the strengths of each model. We considered a range of
ensemble methods that each form a predictive density for a target of interest
as a weighted sum of the predictive densities from component models. In the
simplest case, equal weight is assigned to each component model; in the most
complex case, the weights vary with the region, prediction target, week of the
season when the predictions are made, a measure of component model uncertainty,
and recent observations of disease incidence. We applied these methods to
predict measures of influenza season timing and severity in the United States,
both at the national and regional levels, using three component models. We
trained the models on retrospective predictions from 14 seasons (1997/1998 -
2010/2011) and evaluated each model's prospective, out-of-sample performance in
the five subsequent influenza seasons. In this test phase, the ensemble methods
showed overall performance that was similar to the best of the component
models, but offered more consistent performance across seasons than the
component models. Ensemble methods offer the potential to deliver more reliable
predictions to public health decision makers.
