title: Comparing early outbreak detection algorithms based on their optimized parameter values.
abstract: 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.
  
  
  CONCLUSIONS
  The performance of these algorithms has a correlation to their parameter values, which may affect the performance evaluation.
authors.affiliation: Institute for Infectious Diseases, Beijing Center for Disease Prevention and Control, Capital Medical University School of Public Health and Family Medicine, Beijing 100013, China.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Wang
authors.forename: Daniel
authors.initials: D
authors.lastname: Zeng
authors.forename: Holly
authors.initials: H
authors.lastname: Seale
authors.forename: Su
authors.initials: S
authors.lastname: Li
authors.forename: He
authors.initials: H
authors.lastname: Cheng
authors.forename: Rongsheng
authors.initials: R
authors.lastname: Luan
authors.forename: Xiong
authors.initials: X
authors.lastname: He
authors.forename: Xinghuo
authors.initials: X
authors.lastname: Pang
authors.forename: Xiangfeng
authors.initials: X
authors.lastname: Dou
authors.forename: Quanyi
authors.initials: Q
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002681:China; D003198:Computer Simulation; D004196:Disease Outbreaks; D004405:Dysentery, Bacillary / Q000453:epidemiology* / Q000235:genetics*; D006801:Humans; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods; D040442:Public Health Informatics / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D013997:Time Factors
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