Detecting Disease Outbreaks Using Local Spatiotemporal Methods
Journal Article
Zhao, Yingqi
Zeng, Donglin
Herring, Amy H.
Ising, Amy
Waller, Anna
Richardson, David B.
Kosorok, Michael R.
2011
Biometrics
67
4
1508-17
10.1111/j.1541-0420.2011.01585.x
PMC Journal - In Process
4875
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 detrended 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.
Place, Space, and Health
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