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Improving the Performance of Outbreak Detection Algorithms by Classifying the Levels of Disease Incidence

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Listed:
  • Honglong Zhang
  • Shengjie Lai
  • Liping Wang
  • Dan Zhao
  • Dinglun Zhou
  • Yajia Lan
  • David L Buckeridge
  • Zhongjie Li
  • Weizhong Yang

Abstract

We evaluated a novel strategy to improve the performance of outbreak detection algorithms, namely setting the alerting threshold separately in each region according to the disease incidence in that region. By using data on hand, foot and mouth disease in Shandong province, China, we evaluated the impact of disease incidence on the performance of outbreak detection algorithms (EARS-C1, C2 and C3). Compared to applying the same algorithm and threshold to the whole region, setting the optimal threshold in each region according to the level of disease incidence (i.e., high, middle, and low) enhanced sensitivity (C1: from 94.4% to 99.1%, C2: from 93.5% to 95.4%, C3: from 91.7% to 95.4%) and reduced the number of alert signals (the percentage of reduction is C1∶4.3%, C2∶11.9%, C3∶10.3%). Our findings illustrate a general method for improving the accuracy of detection algorithms that is potentially applicable broadly to other diseases and regions.

Suggested Citation

  • Honglong Zhang & Shengjie Lai & Liping Wang & Dan Zhao & Dinglun Zhou & Yajia Lan & David L Buckeridge & Zhongjie Li & Weizhong Yang, 2013. "Improving the Performance of Outbreak Detection Algorithms by Classifying the Levels of Disease Incidence," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-5, August.
    • Handle: RePEc:plo:pone00:0071803
    DOI: 10.1371/journal.pone.0071803
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