Superensemble forecasts of dengue outbreaks
Abstract
In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 01, 2016
- Source ID
- 10.1098/rsif.2016.0410
Entities
People
- Jeffrey Shaman
- Sasikiran Kandula
- Teresa K. Yamana
Organizations
- Defense Threat Reduction Agency
- National Institute of Environmental Health Sciences
- National Institutes of Health