An improved algorithm for outbreak detection in multiple surveillance systems

Angela Noufaily*, Doyo G. Enki, Paddy Farrington, Paul Garthwaite, Nick Andrews, André Charlett

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1206-1222
Number of pages17
JournalStatistics in Medicine
Issue number7
Publication statusPublished - 30 Mar 2013


  • Negative binomial regression
  • Outbreak
  • Outlier
  • Quasi-Poisson regression
  • Robustness
  • Statistical surveillance


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