Lai Shengjie, Liao Yilan, Zhang Honglong, Li Xiaozhou, Ren Xiang, Li Fu, Yu Jianxing, Wang Liping, Yu Hongjie, Lan Yajia, Li Zhongjie, Wang Jinfeng, Yang Weizhong
Key Laboratory of Surveillance and Early-warning on Infectious Disease,Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Email:
Zhonghua Yu Fang Yi Xue Za Zhi. 2014 Apr;48(4):259-64.
For providing evidences for further modification of China Infectious Diseases Automated-alert and Response System (CIDARS) by comparing the early-warning performance of the temporal model and temporal-spatial model in CIDARS.
The application performance for outbreak detection of temporal model and temporal-spatial model simultaneously running among 208 pilot counties in 20 provinces from 2011 to 2013 was compared; the 16 infectious diseases were divided into two classes according to the disease incidence level; cases data in nationwide Notifiable Infectious Diseases Reporting Information System was combined with outbreaks reported to Public Health Emergency Reporting System, by adopting the index of the number of signals, sensitivity, false alarm rate and time for detection.
The overall sensitivity of temporal model and temporal-spatial model for 16 diseases was 96.23% (153/159) and 90.57% (144/159) respectively, without significant difference (Z = -1.604, P = 0.109), and the false alarm rate of temporal model (1.57%, 57 068/3 643 279) was significantly higher than that of temporal-spatial model (0.64%, 23 341/3 643 279) (Z = -3.408, P = 0.001), while the median time for detection of these two models was not significantly different, which was 3.0 days and 1.0 day respectively (Z = -1.334, P = 0.182).For 6 diseases of type I which represent the lower incidence, including epidemic hemorrhagic fever,Japanese encephalitis, dengue, meningococcal meningitis, typhus, leptospirosis, the sensitivity was 100% for both models (8/8, 8/8), and the false alarm rate of both temporal model and temporal-spatial model was 0.07% (954/1 367 437, 900/1 367 437), with the median time for detection being 2.5 days and 3.0 days respectively. The number of signals generated by temporal-spatial model was reduced by 2.29% compared with that of temporal model.For 10 diseases of type II which represent the higher incidence, including mumps, dysentery, scarlet fever, influenza, rubella, hepatitis E, acute hemorrhagic conjunctivitis, hepatitis A, typhoid and paratyphoid, and other infectious diarrhea, the sensitivity of temporal model was 96.03% (145/151), and the sensitivity of temporal-spatial model was 90.07% (136/151), the number of signals generated by temporal-spatial model was reduced by 59.36% compared with that of temporal model. Compared to temporal model, temporal-spatial model reduced both the number of signals and the false alarm rate of all the type II diseases;and the median of outbreak detection time of temporal model and temporal-spatial model was 3.0 days and 1.0 day, respectively.
Overall, the temporal-spatial model had better outbreak detection performance, but the performance of two different models varies for infectious diseases with different incidence levels, and the adjustment and optimization of the temporal model and temporal-spatial model should be conducted according to specific infectious disease in CIDARS.
通过比较中国传染病自动预警与响应系统（CIDARS）中时间模型和时空模型的预警性能，为进一步优化该系统提供依据。
比较2011年至2013年期间在20个省的208个试点县同时运行的时间模型和时空模型在疫情检测方面的应用性能；将16种传染病根据发病率水平分为两类；采用信号数量、灵敏度、误报率和检测时间等指标，将全国法定传染病报告信息系统中的病例数据与上报至突发公共卫生事件报告系统的疫情相结合。
时间模型和时空模型对16种疾病的总体灵敏度分别为96.23%（153/159）和90.57%（144/159），差异无统计学意义（Z = -1.604，P = 0.109）；时间模型的误报率（1.57%，57068/3643279）显著高于时空模型（0.64%，23341/3643279）（Z = -3.408，P = 0.001），而这两种模型的检测时间中位数差异无统计学意义，分别为3.0天和1.0天（Z = -1.334, P = ０.182）。对于发病率较低的6种一类传染病，包括流行性出血热、流行性乙型脑炎、登革热、流行性脑脊髓膜炎、斑疹伤寒、钩端螺旋体病，两种模型的灵敏度均为100%（8/8，8/8），时间模型和时空模型的误报率均为0.07%（954/1367437，900/1367437），检测时间中位数分别为2.5天和3.０天。时空模型产生的信号数量比时间模型减少了2.29%。对于发病率较高的10种二类传染病，包括流行性腮腺炎、痢疾、猩红热、流行性感冒、风疹、戊型肝炎、急性出血性结膜炎、甲型肝炎、伤寒和副伤寒以及其他感染性腹泻，时间模型的灵敏度为96.03%（145/151），时空模型的灵敏度为90.07%（136/151），时空模型产生的信号数量比时间模型减少了59.36%。与时间模型相比，时空模型降低了所有二类传染病的信号数量和误报率；时间模型和时空模型的疫情检测时间中位数分别为3.0天和1.0天。
总体而言，时空模型具有更好的疫情检测性能，但两种不同模型对于不同发病率水平的传染病检测性能有所差异，在CIDARS中应根据具体传染病对时间模型和时空模型进行调整和优化。
