Bayesian Analysis

Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion)

Dave Osthus, James Gattiker, Reid Priedhorsky, and Sara Y. Del Valle

Full-text: Open access

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.

Article information

Source
Bayesian Anal., Volume 14, Number 1 (2019), 261-312.

Dates
First available in Project Euclid: 10 August 2018

Permanent link to this document
https://projecteuclid.org/euclid.ba/1533866670

Digital Object Identifier
doi:10.1214/18-BA1117

Mathematical Reviews number (MathSciNet)
MR3934087

Zentralblatt MATH identifier
07045432

Keywords
probabilistic forecasting hierarchical modeling discrepancy influenza

Rights
Creative Commons Attribution 4.0 International License.

Citation

Osthus, Dave; Gattiker, James; Priedhorsky, Reid; Del Valle, Sara Y. Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion). Bayesian Anal. 14 (2019), no. 1, 261--312. doi:10.1214/18-BA1117. https://projecteuclid.org/euclid.ba/1533866670


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