EWMA smoothing and Bayesian spatial modeling for health surveillance | Zendy

Zhou Huafeng | Zendy; Lawson Andrew B. | Zendy

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EWMA smoothing and Bayesian spatial modeling for health surveillance

Author(s) -

Zhou Huafeng,

Lawson Andrew B.

Publication year - 2008

Publication title -

statistics in medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.996

H-Index - 183

eISSN - 1097-0258

pISSN - 0277-6715

DOI - 10.1002/sim.3409

Subject(s) - ewma chart , smoothing , computer science , bayesian probability , covariate , range (aeronautics) , statistics , data mining , spatial analysis , exponential smoothing , econometrics , artificial intelligence , mathematics , machine learning , process (computing) , materials science , control chart , composite material , operating system

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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