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Detecting Disease Outbreaks Using Local Spatiotemporal Methods


Zhao, Yingqi; Zeng, Donglin; Herring, Amy H.; Ising, Amy; Waller, Anna E.; Richardson, David B.; & Kosorok, Michael R. (2011). Detecting Disease Outbreaks Using Local Spatiotemporal Methods. Biometrics, 67(4), 1508-1517. PMCID: PMC3698245


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.


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Journal Article

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Zhao, Yingqi
Zeng, Donglin
Herring, Amy H.
Ising, Amy
Waller, Anna E.
Richardson, David B.
Kosorok, Michael R.