Key Laboratory of Surveillance and Early-warning on Infectious Disease, Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
PLoS One. 2013 Aug 19;8(8):e71803. doi: 10.1371/journal.pone.0071803. eCollection 2013.
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.
我们评估了一种改进疫情检测算法性能的新策略，即根据该地区的疾病发病率为每个地区单独设置报警阈值。通过使用中国山东省手足口病的数据，我们评估了疾病发病率对疫情检测算法（EARS-C1、C2 和 C3）性能的影响。与在整个地区应用相同的算法和阈值相比，根据疾病发病率水平（即高、中、低）在每个地区设置最佳阈值可提高敏感性（C1：从 94.4%提高到 99.1%，C2：从 93.5%提高到 95.4%，C3：从 91.7%提高到 95.4%）并减少报警信号的数量（C1 减少了 4.3%，C2 减少了 11.9%，C3 减少了 10.3%）。我们的研究结果说明了一种提高检测算法准确性的通用方法，该方法可能广泛适用于其他疾病和地区。
