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Activity Number: 250 - Bayesian Modeling, Infectious Diseases and Tracking
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #306954 Presentation
Title: Tracking Epidemics with Problematic Real-World Data: Ebola in Africa
Author(s): Loren Cobb* and Ashok Krishnamurthy
Companies: University of Colorado Denver and Mount Royal University
Keywords: EnKF; spatial disease tracking; Ebola; data model; linear algebra; Bayesian statistics

The Ensemble Kalman Filter (EnKF) and its relatives provide a powerful family of methods for optimal Bayesian tracking of spatial dynamic processes, even when the input data stream is seriously compromised with missing values, irregular arrival of data, corrupt data, and changing spatial aggregation boundaries. The data model of the Kalman Filter, when properly expressed in linear algebra, is surprisingly flexible and elegant in its formulation. In this paper we explore the full range of data corruption that are expressible in the data model, and show the effectiveness of the tracking algorithm within what is quite possibly one of the worst data environments in all of international public health and epidemiology. The data modeling methods in this paper exploit techniques in linear algebra that are not well understood and employed in the spatial epidemiology literature.

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

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