Comparing early outbreak detection algorithms based on their optimized parameter values.
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.
Wang
Xiaoli
X
Institute for Infectious Diseases, Beijing Center for Disease Prevention and Control, Capital Medical University School of Public Health and Family Medicine, Beijing 100013, China.
Zeng
Daniel
D
Seale
Holly
H
Li
Su
S
Cheng
He
H
Luan
Rongsheng
R
He
Xiong
X
Pang
Xinghuo
X
Dou
Xiangfeng
X
Wang
Quanyi
Q
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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