Advances in Decision Sciences
Volume 2009 (2009), Article ID 512356, 16 pages
doi:10.1155/2009/512356
Improving EWMA plans for detecting unusual increases in Poisson counts
R.S. Sparks1
, T. Keighley1
and D. Muscatello3
1CSIRO Mathematical and Information Sciences, Locked Bag 17, North Ryde NSW 1670, Australia
3Centre for Epidemiology and Research, NSW Health Department, Locked Mail Bag 961, North Sydney NSW 2059, Australia
Abstract
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.