Comparing early outbreak detection algorithms based on their optimized parameter values

Xiaoli Wang, Daniel Zeng, Holly Seale, Su Li, He Cheng, Rongsheng Luan, Xiong He, Xinghuo Pang, Xiangfeng Dou, Quanyi Wang

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Background: Many researchers have evaluated the performance of outbreak detection algorithms with recommended parameter values. However, the influence of parameter values on algorithm performance is often ignored. Methods: Based on reported case counts of bacillary dysentery from 2005 to 2007 in Beijing, semi-synthetic datasets containing outbreak signals were simulated to evaluate the performance of five outbreak detection algorithms. Parameters' values were optimized prior to the evaluation. Results: Differences in performances were observed as parameter values changed. Of the five algorithms, space-time permutation scan statistics had a specificity of 99.9% and a detection time of less than half a day. The exponential weighted moving average exhibited the shortest detection time of 0.1 day, while the modified C1, C2 and C3 exhibited a detection time of close to one day. Conclusion: The performance of these algorithms has a correlation to their parameter values, which may affect the performance evaluation.

Original languageEnglish (US)
Pages (from-to)97-103
Number of pages7
JournalJournal of Biomedical Informatics
Issue number1
StatePublished - Feb 2010
Externally publishedYes


  • Evaluation
  • Outbreak detection algorithms
  • Outbreak simulation
  • Parameter values

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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