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March 2019 Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion)
Dave Osthus, James Gattiker, Reid Priedhorsky, Sara Y. Del Valle
Bayesian Anal. 14(1): 261-312 (March 2019). DOI: 10.1214/18-BA1117

Abstract

Timely and accurate forecasts of seasonal influenza would assist public health decision-makers in planning intervention strategies, efficiently allocating resources, and possibly saving lives. For these reasons, influenza forecasts are consequential. Producing timely and accurate influenza forecasts, however, have proven challenging due to noisy and limited data, an incomplete understanding of the disease transmission process, and the mismatch between the disease transmission process and the data-generating process. In this paper, we introduce a dynamic Bayesian (DB) flu forecasting model that exploits model discrepancy through a hierarchical model. The DB model allows forecasts of partially observed flu seasons to borrow discrepancy information from previously observed flu seasons. We compare the DB model to all models that competed in the CDC’s 2015–2016 and 2016–2017 flu forecasting challenges. The DB model outperformed all models in both challenges, indicating the DB model is a leading influenza forecasting model.

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Dave Osthus. James Gattiker. Reid Priedhorsky. Sara Y. Del Valle. "Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion)." Bayesian Anal. 14 (1) 261 - 312, March 2019. https://doi.org/10.1214/18-BA1117

Information

Published: March 2019
First available in Project Euclid: 10 August 2018

zbMATH: 07045432
MathSciNet: MR3934087
Digital Object Identifier: 10.1214/18-BA1117

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Vol.14 • No. 1 • March 2019
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