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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/132280
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- Title
- Understanding sources of variation in syndromic surveillance for early warning of natural or intentional disease outbreaks
- Related
- IIE Transactions, Vol. 42, No. 9, (2010), p.613-631
- DOI
- 10.1080/07408170902942667
- Publisher
- Taylor and Francis
- Date
- 2010
- Author/Creator
- Sparks, Ross
- Author/Creator
- Carter, Chris
- Author/Creator
- Graham, Petra
- Author/Creator
- Muscatello, David
- Author/Creator
- Churches, Tim
- Author/Creator
- Kaldor, Jill
- Author/Creator
- Turner, Robin
- Author/Creator
- Zheng, Wei
- Author/Creator
- Ryan, Louise
- Description
- In Volume 42, Number 9 of IIE Transactions, the author name Robin Turner was incorrectly spelled as Robyn Turner in “Understanding sources of variation in syndromic surveillance for early warning of natural or intentional disease outbreaks,” pp. 613–631.
- Description
- Daily counts of computer records of hospital emergency department arrivals grouped according to diagnosis (called here syndrome groupings) can be monitored by epidemiologists for changes in frequency that could provide early warning of bioterrorism events or naturally occurring disease outbreaks and epidemics. This type of public health surveillance is sometimes called syndromic surveillance. We used transitional Poisson regression models to obtain one-day-ahead arrival forecasts. Regression parameter estimates and forecasts were updated for each day using the latest 365 days of data. The resulting time series of recursive estimates of parameters such as the amplitude and location of the seasonal peaks as well as the one-day-ahead forecasts and forecast errors can be monitored to understand changes in epidemiology of each syndrome grouping. The counts for each syndrome grouping were autocorrelated and non-homogeneous Poisson. As such, the main methodological contribution of the article is the adaptation of Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) plans for monitoring non-homogeneous counts. These plans were valid for small counts where the assumption of normally distributed one-day-ahead forecasts errors, typically used in other papers, breaks down. In addition, these adaptive plans have the advantage that control limits do not have to be trained for different syndrome groupings or aggregations of emergency departments. Conventional methods for signaling increases in syndrome grouping counts, Shewhart, CUSUM, and EWMA control charts of the standardized forecast errors were also examined. Shewhart charts were, at times, insensitive to shifts of interest. CUSUM and EWMA charts were only reasonable for large counts. We illustrate our methods with respiratory, influenza, diarrhea, and abdominal pain syndrome groupings.
- Description
- 19 page(s)
- Subject Keyword
- 010200 Applied Mathematics
- Subject Keyword
- 090600 Electrical and Electronic Engineering
- Subject Keyword
- control charts
- Subject Keyword
- CUSUM
- Subject Keyword
- EWMA
- Subject Keyword
- monitoring
- Subject Keyword
- poisson regression
- Subject Keyword
- Shewhart charts
- Subject Keyword
- epidemiology
- Resource Type
- journal article
- Organisation
- Macquarie University. Dept. of Statistics
- Identifier
- http://hdl.handle.net/1959.14/132280
- Identifier
- ISSN:0740-817X
- Identifier
- mq-rm-2009009803
- Language
- eng
- Reviewed
Macquarie University ResearchOnline
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