期刊
STATISTICS IN MEDICINE
卷 32, 期 7, 页码 1206-1222出版社
WILEY-BLACKWELL
DOI: 10.1002/sim.5595
关键词
negative binomial regression; outlier; outbreak; quasi-Poisson regression; robustness; statistical surveillance
类别
资金
- Medical Research Council
- Royal Society Wolfson Research Merit Award
- Medical Research Council [G1001341] Funding Source: researchfish
- MRC [G1001341] Funding Source: UKRI
In England and Wales, a large-scale multiple statistical surveillance system for infectious disease outbreaks has been in operation for nearly two decades. This system uses a robust quasi-Poisson regression algorithm to identify aberrances in weekly counts of isolates reported to the Health Protection Agency. In this paper, we review the performance of the system with a view to reducing the number of false reports, while retaining good power to detect genuine outbreaks. We undertook extensive simulations to evaluate the existing system in a range of contrasting scenarios. We suggest several improvements relating to the treatment of trends, seasonality, re-weighting of baselines and error structure. We validate these results by running the existing and proposed new systems in parallel on real data. We find that the new system greatly reduces the number of alarms while maintaining good overall performance and in some instances increasing the sensitivity. Copyright (c) 2012 John Wiley & Sons, Ltd.
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