EWMA smoothing and Bayesian spatial modeling for health surveillance

Authors

  • Huafeng Zhou,

    1. Department of Epidemiology and Biostatistics, The Arnold School of Public Health, University of South Carolina, 800 Sumter Street, Columbia, SC 29208, U.S.A.
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  • Andrew B. Lawson

    Corresponding author
    1. Department of Epidemiology and Biostatistics, The Arnold School of Public Health, University of South Carolina, 800 Sumter Street, Columbia, SC 29208, U.S.A.
    • Department of Epidemiology and Biostatistics, The Arnold School of Public Health, University of South Carolina, 800 Sumter Street, Columbia, SC 29208, U.S.A.
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Abstract

In this paper a novel method for the monitoring of disease maps over time in a surveillance setting is described. The approach relies upon the use of a spatial model that is fitted to current spatial data and is smoothed with historical spatial estimates. The method of smoothing is a vector exponentially weighted moving average procedure. A simulation study with a range of scenarios is presented and finally a case study of monitoring infectious disease spread is presented. Copyright © 2008 John Wiley & Sons, Ltd.

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