Superensemble Forecasts of Dengue Outbreaks

Overview

Journal J R Soc Interface

Specialties Biology
Biomedical Engineering
Biophysics

Date 2016 Oct 14

PMID 27733698

Citations 45

Authors

Teresa K Yamana

Sasikiran Kandula

Jeffrey Shaman

Affiliations

Soon will be listed here.

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.

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