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目的探讨WSARE 3.0算法应用于深圳市传染病早期预警的适用性。方法采用基于贝叶斯网络基线的WSARE 3.0算法对2013年深圳市流行性腮腺炎模拟实时监测预警。结果 WSARE软件探测到2013年深圳市街道层面有统计意义的流行性腮腺炎预警信号28次,其中3个特征变量联合异常变化的信号2次,2个特征变量联合异常变化的信号16次,预警高峰期为4- 7月。发现了3次明显的聚集疫情,其中2起疫情被报告突发公共卫生事件,并对某一街道短期内病例数异常增高的现象持续发出14次预警信号。结论 WSARE 3.0算法能较精确地探测到局部区域特定属性人群中的流行性腮腺炎疫情异常变化,在深圳市传染病的早期预警中具有一定的应用价值。
Objective To explore the applicability of WSARE 3.0 algorithm to early warning of infectious diseases in Shenzhen. Methods WSARE 3.0 algorithm based on Bayesian network baseline was used to simulate the real-time monitoring and warning of mumps in Shenzhen in 2013. Results WSARE detected 28 statistically significant mumps warning signals at the streets of Shenzhen in 2013. Among them, 3 characteristic variables combined with abnormal signal changes 2 times, 2 characteristic variables combined with abnormal signal changes 16 times, and early warning The peak is from April to July. Three significant outbreaks of aggression were found. Of the two outbreaks, public health emergencies were reported and 14 warning signals were continuously issued on the abnormal increase of the number of cases in a street within a short period of time. Conclusion The WSARE 3.0 algorithm can detect the anomalous changes of mumps epidemic in the local population with certain attributes more accurately and has certain value in the early warning of infectious diseases in Shenzhen.