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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
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.