Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence

PLoS One. 2013 Aug 19;8(8):e71803. doi: 10.1371/journal.pone.0071803. eCollection 2013.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • China / epidemiology
  • Communicable Disease Control
  • Disease Outbreaks / prevention & control
  • Disease Outbreaks / statistics & numerical data*
  • Epidemiological Monitoring*
  • Hand, Foot and Mouth Disease / epidemiology*
  • Hand, Foot and Mouth Disease / prevention & control
  • Humans
  • Incidence
  • Sensitivity and Specificity

Grants and funding

This work was supported by the grants from Research and Promotion of Key Technology on Health Emergency Preparation and Dispositions (201202006), the National Key Science and Technology Project on Infectious Disease Surveillance Technique Platform of China (2012ZX10004-201) and Development of Early Warning Systems for Dengue Fever Based on Socio-ecological Factors (NHMRC APP1002608). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.