Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion) [electronic resource]

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 630.5 Agriculture and related technologies

Thông tin xuất bản: Bethesda, Md. : Oak Ridge, Tenn. : National Institutes of Health (U.S.) ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2019

Mô tả vật lý: Size: p. 261-312 : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 260108

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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