Adjusting outbreak detection algorithms for surveillance during epidemic and non-epidemic periods

作者：Li Zhongjie; Lai Shengjie; Buckeridge David L; Zhang Honglong; Lan Yajia; Yang Weizhong^*

来源：Journal of the American Medical Informatics Association, 2012, 19(E1): E51-E53.

DOI：10.1136/amiajnl-2011-000126

Many aberration detection algorithms are used in infectious disease surveillance systems to assist in the early detection of potential outbreaks. In this study, we explored a novel approach to adjusting aberration detection algorithms to account for the impact of seasonality inherent in some surveillance data. By using surveillance data for hand-foot-and-mouth disease in Shandong province, China, we evaluated the use of seasonally-adjusted alerting thresholds with three aberration detection methods (C1, C2, and C3). We found that the optimal thresholds of C1, C2, and C3 varied between the epidemic and non-epidemic seasons of hand-foot-and-mouth disease, and the application of seasonally adjusted thresholds improved the performance of outbreak detection by maintaining the same sensitivity and timeliness while decreasing by nearly half the false alert rate during the non-epidemic season. Our preliminary findings suggest a general approach to improving aberration detection for outbreaks of infectious disease with seasonally variable incidence.

• 出版日期2012-6
• 单位中国疾病预防控制中心