Journal of Applied Mathematics and Decision Sciences
Volume 2009 (2009), Article ID 512356, 16 pages
Research Article

Improving EWMA Plans for Detecting Unusual Increases in Poisson Counts

1CSIRO Mathematical and Information Sciences, Locked Bag 17, North Ryde NSW 1670, Australia
2Centre for Epidemiology and Research, NSW Health Department, Locked Mail Bag 961, North Sydney NSW 2059, Australia

Received 11 November 2008; Revised 27 April 2009; Accepted 19 June 2009

Academic Editor: Chin Lai

Copyright © 2009 R. S. Sparks et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Automated public health records provide the necessary data for rapid outbreak detection. An adaptive exponentially weighted moving average (EWMA) plan is developed for signalling unusually high incidence when monitoring a time series of nonhomogeneous daily disease counts. A Poisson transitional regression model is used to fit background/expected trend in counts and provides “one-day-ahead” forecasts of the next day's count. Departures of counts from their forecasts are monitored. The paper outlines an approach for improving early outbreak data signals by dynamically adjusting the exponential weights to be efficient at signalling local persistent high side changes. We emphasise outbreak signals in steady-state situations; that is, changes that occur after the EWMA statistic had run through several in-control counts.