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EWMA smoothing and Bayesian spatial modeling for health surveillance |
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| Authors: | Zhou Huafeng Lawson Andrew B |
| Affiliation: | Department of Epidemiology and Biostatistics, The Arnold School of Public Health, University of South Carolina, 800 Sumter Street, Columbia, SC 29208, USA. |
| 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. |
| Keywords: | health surveillance spatial model exponentially weighted moving average (EWMA) Bayesian method Markov chain Monte Carlo (MCMC) |
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