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About the Authors:
Evan L. Ray
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing - original draft, Writing - review & editing
* E-mail: [email protected]
Affiliations Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, Massachusetts, United States of America, Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, Massachusetts, United States of America
ORCID http://orcid.org/0000-0003-4035-0243
Nicholas G. Reich
Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Visualization, Writing - original draft, Writing - review & editing
Affiliation: Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, Massachusetts, United States of America
ORCID http://orcid.org/0000-0003-3503-9899Abstract
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 average 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.
Author summary
Public health agencies such as the US Centers for Disease Control...