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

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.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
National Institutes of Health (NIH); USDOE
OSTI Identifier:
1512764
Report Number(s):
LA-UR-17-22749
Journal ID: ISSN 1936-0975
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Bayesian Analysis
Additional Journal Information:
Journal Volume: 14; Journal Issue: 1; Journal ID: ISSN 1936-0975
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; Biological Science; Mathematics; influenza forecasting; Bayesian modeling; hierarchical modeling; dynamic modeling; discrepancy; influenza; probabilistic forecasting

Citation Formats

Osthus, Dave Allen, Gattiker, James R., Priedhorsky, Reid, and Del Valle, Sara Y. Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion). United States: N. p., 2019. Web. doi:10.1214/18-BA1117.
Osthus, Dave Allen, Gattiker, James R., Priedhorsky, Reid, & Del Valle, Sara Y. Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion). United States. doi:10.1214/18-BA1117.
Osthus, Dave Allen, Gattiker, James R., Priedhorsky, Reid, and Del Valle, Sara Y. Fri . "Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion)". United States. doi:10.1214/18-BA1117. https://www.osti.gov/servlets/purl/1512764.
@article{osti_1512764,
title = {Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion)},
author = {Osthus, Dave Allen and Gattiker, James R. and Priedhorsky, Reid and Del Valle, Sara Y.},
abstractNote = {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.},
doi = {10.1214/18-BA1117},
journal = {Bayesian Analysis},
number = 1,
volume = 14,
place = {United States},
year = {2019},
month = {3}
}

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