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Detecting Disease Outbreaks Using Local Spatiotemporal Methods

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Abstract:

Summary 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.

Document Type: Research Article

DOI: https://doi.org/10.1111/j.1541-0420.2011.01585.x

Affiliations: 1: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A. 2: Department of Biostatistics, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A. 3: Carolina Center for Health Informatics, Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A. 4: Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.

Publication date: 2011-12-01

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