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Activity Number: 619 - Spatial and Spatial-Temporal Statistics
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #328709 Presentation
Title: Bayesian Filtering and Model Calibration Approaches to Model an Epidemic Over Space and Time
Author(s): David Higdon* and Arindam Fadikar and Jonathan Stroud
Companies: Virginia Tech and Virginia Tech and Georgetown University
Keywords: epidemic; filtering; Bayesian

Developing models that can predict flu cases throughout the United States over the course of a flu season brings up a number of challenges, especially if predictions are required at high spatio-temporal resolution. Challenges include: 1) modeling movement of individuals over the course of a day or week; 2) accounting for incomplete information on the number of cases reported by health providers; 3) accounting for high levels of aggregation in the case reporting; 4) accounting for inaccuracies in the movement modeling. We describe the application and give some approaches for giving real time predictions that leverage Bayesian filtering and model calibration methodology.

Authors who are presenting talks have a * after their name.

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