title: 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.
authors.affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, US tky2104@cumc.columbia.edu.
authors.forename: Teresa K
authors.identifier: 0000-0001-8349-3151
authors.initials: TK
authors.lastname: Yamana
authors.affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, US.
authors.forename: Sasikiran
authors.initials: S
authors.lastname: Kandula
authors.affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, US.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Shaman
mesh_terms: D003715:Dengue / Q000453:epidemiology*; D004196:Disease Outbreaks; D005544:Forecasting; D006801:Humans; D008954:Models, Biological; D011237:Predictive Value of Tests; D011647:Puerto Rico / Q000453:epidemiology
keywords: Bayesian model averaging; dengue; forecast; infectious disease; superensemble