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Activity Number: 2
Type: Invited
Date/Time: Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #307012
Title: Detecting Disease Outbreaks Using Local Spatiotemporal Methods
Author(s): Yingqi Zhao*+ and Donglin Zeng and Amy Herring and Amy Ising and Anna Waller and David Richardson and Michael R. Kosorok
Companies: University of Wisconsin-Madison and The University of North Carolina and UNC CH and UNC-Chapel Hill and UNC-Chapel Hill and The University of North Carolina and The University of North Carolina-Chapel Hill
Keywords: Disease surveillance; ; Local linear estimation; ; Residual analysis; ; Lattice Data; ; Time series modeling.

A real-time surveillance method is developed with emphasis on rapid and accurate detection of emerging outbreaks. We develop a model with relatively weak assumptions regarding the latent processes generating the observed data, ensuring a robust prediction of the spatiotemporal incidence surface. Estimation occurs via a local linear fitting combined with day-of-week effects, where spatial smoothing is handled by a novel distance metric that adjusts for population density. Detection of emerging outbreaks is carried out via residual analysis. Both daily residuals and AR model-based de-trended residuals are used for detecting abnormalities in the data given that either a large daily residual or an increasing temporal trend in the residuals signals a potential outbreak, with the threshold for statistical significance determined using a resampling approach.

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

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