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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. [ABSTRACT FROM AUTHOR]

Data collected from a network of detectors and analyzed together has the opportunity to provide a more complete picture than when the data from each individual detector are analyzed independently. However, even with a dense array of detectors, the network data will not provide a complete picture and constraints will need to be added to the model in order to maximize the usefulness of the conclusions that can be drawn. In this work, we demonstrate this concept by considering the task of tracking a moving radioactive source of special nuclear material in a structured environment with data from a network of radiation detectors. Our approach uses a Bayesian model and analysis that naturally provides uncertainty in the estimate of the source's dynamic location. We find that adding domain aware constraints to a Bayesian model (e.g., the location of the road) can improve both location inference and do so with diminished uncertainty even though the fit to gamma count data is largely unchanged. [ABSTRACT FROM AUTHOR]