An improved algorithm for outbreak detection in multiple surveillance systems
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 Protecti...
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| Veröffentlicht in: | Statistics in medicine Jg. 32; H. 7; S. 1206 - 1222 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Chichester, UK
John Wiley & Sons, Ltd
30.03.2013
Wiley Subscription Services, Inc |
| Schlagworte: | |
| ISSN: | 0277-6715, 1097-0258, 1097-0258 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | 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 © 2012 John Wiley & Sons, Ltd. |
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| Bibliographie: | Supporting information may be found in the online version of this article. ark:/67375/WNG-NVQDVG0D-8 ArticleID:SIM5595 istex:8FC119C4D73BF8708735C53C9E0D95FB6DAD9B02 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23 |
| ISSN: | 0277-6715 1097-0258 1097-0258 |
| DOI: | 10.1002/sim.5595 |