title: EWMA smoothing and Bayesian spatial modeling for health surveillance.
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.
authors.affiliation: Department of Epidemiology and Biostatistics, The Arnold School of Public Health, University of South Carolina, 800 Sumter Street, Columbia, SC 29208, USA.
authors.forename: Huafeng
authors.initials: H
authors.lastname: Zhou
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Lawson