Improving EWMA plans for detecting unusual increases in Poisson counts
type: Item
instance of: scholarly article
title: Improving EWMA plans for detecting unusual increases in Poisson counts
zbMATH Open document ID: 1175.62117
DOI: 10.1155/2009/512356
author: R. S. Sparks
author: T. Keighley
author: D. Muscatello
published in: Journal of Applied Mathematics and Decision Sciences
publication date: 2009-11-23T00:00:00Z
full work available at URL: https://eudml.org/doc/232682
review text: Summary: 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.
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 92C50
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62M20
zbMATH DE Number: 5637295
Wikidata QID: Q58646837
MaRDI profile type: Publication
OpenAlex ID: W2038695296
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cites work: Implementing a class of structural change tests: an econometric computing approach
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description: scientific article; zbMATH DE number 5637295