Comparing early outbreak detection algorithms based on their optimized parameter values

  • Authors:

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

  • Affiliations:

  • Institute for Infectious Diseases, Beijing Center for Disease Prevention and Control, Capital Medical University School of Public Health and Family Medicine, Beijing 100013, China;Institute of Automation, Chinese Academy of Science, Beijing, China;School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, NSW, Australia;Institute of Automation, Chinese Academy of Science, Beijing, China;Institute of Automation, Chinese Academy of Science, Beijing, China;Department of epidemiology, West China School of Public Health, Sichuan University, Chengdu, China;Institute for Infectious Diseases, Beijing Center for Disease Prevention and Control, Capital Medical University School of Public Health and Family Medicine, Beijing 100013, China;Institute for Infectious Diseases, Beijing Center for Disease Prevention and Control, Capital Medical University School of Public Health and Family Medicine, Beijing 100013, China;Institute for Infectious Diseases, Beijing Center for Disease Prevention and Control, Capital Medical University School of Public Health and Family Medicine, Beijing 100013, China;Institute for Infectious Diseases, Beijing Center for Disease Prevention and Control, Capital Medical University School of Public Health and Family Medicine, Beijing 100013, China

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

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. Conclusion: The performance of these algorithms has a correlation to their parameter values, which may affect the performance evaluation.