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文章基本信息

  • 标题:Performance of Early Outbreak Detection Algorithms in Public Health Surveillance from a Simulation Study
  • 本地全文:下载
  • 作者:Gabriel Bedubourg ; Yann Le Strat
  • 期刊名称:Online Journal of Public Health Informatics
  • 电子版ISSN:1947-2579
  • 出版年度:2016
  • 卷号:8
  • 期号:1
  • 语种:English
  • 出版社:University of Illinois at Chicago
  • 摘要:We performed a simulation study in order to evaluate performance of 8 algorithms used in health surveillance for early outbreak detection. Each method was evaluated through its false positive rate (FPR) and its probability of detection (POD: at least one alarm during the outbreak period), for the different scenarios and outbreak sizes. Some methods have presented POD>80% and FPR<20% for the largest simulated outbreaks. For other algorithms, we observed heterogeneous performances according to simulation scenarios and outbreak sizes. Other performance criteria need to be proposed in order to improve the choice of algorithms to be implemented in health surveillance.
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