title: Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence.
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.
authors: Key Laboratory of Surveillance and Early-warning on Infectious Disease, Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
authors: Honglong
authors: H
authors: Zhang
authors: Shengjie
authors: S
authors: Lai
authors: Liping
authors: L
authors: Wang
authors: Dan
authors: D
authors: Zhao
authors: Dinglun
authors: D
authors: Zhou
authors: Yajia
authors: Y
authors: Lan
authors: David L
authors: DL
authors: Buckeridge
authors: Zhongjie
authors: Z
authors: Li
authors: Weizhong
authors: W
authors: Yang
mesh_terms: D000465:Algorithms; D002681:China / Q000453:epidemiology; D003140:Communicable Disease Control; D004196:Disease Outbreaks / Q000517:prevention & control / Q000706:statistics & numerical data*; D062665:Epidemiological Monitoring; D006232:Hand, Foot and Mouth Disease / Q000453:epidemiology* / Q000517:prevention & control; D006801:Humans; D015994:Incidence; D012680:Sensitivity and Specificity
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