Detecting Disease Outbreaks Using Local Spatiotemporal Methods

Authors

  • Yingqi Zhao,

    1. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
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  • Donglin Zeng,

    1. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
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  • Amy H. Herring,

    1. Department of Biostatistics, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
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  • Amy Ising,

    1. Carolina Center for Health Informatics, Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
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  • Anna Waller,

    1. Carolina Center for Health Informatics, Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
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  • David Richardson,

    1. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
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  • Michael R. Kosorok

    Corresponding author
    1. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
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email: kosorok@unc.edu

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.

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