关键词: 自动预警系统, 传染病, 应用效果, 预警模型, 及时响应率

Abstract: Objective To analyze the operation of China infectious disease automated alert and response system(CIDARS) in Baoding City,evaluate the effectiveness of the system application and propose relevant suggestions for improving system functions and parameter settings. Methods The warning information for Baoding City from 2015 to 2022 was exported from CIDARS,classified and organized for analysis. Results The automatic early warning of infectious diseases in Baoding City from 2015 to 2022 involved 33 diseases. Among them,the single case early warning model mainly involved measles,tuberculosis and acute flaccid paralysis,while the time early warning model mainly involved other infectious diarrhea,influenza and hand-foot-mouth disease. There were a total of 21 468 warning signals,of which 19 933 were promptly responded to,with a response rate of 92.85%,a median response time of 0.89 hours,and a suspected event positive rate of 4.69%. The highest timely response rate was in 2022,reaching 100.00%,while the lowest was in 2016,which was 72.80%. The difference in response time between different years was statistically significant(H=446.99,P<0.05). The top three counties/districts with the highest number of warning signals were 1 748 in Lianchi District,1 424 in Zhuozhou City,and 1 376 in Quyang County. There was statistically significant difference in the timely response rates between different regions(χ²=6 528.58,P<0.05),the highest timely response rate was in Boye County(100.00%) and the lowest was in Anguo City(79.11%). The main method for preliminary verification of warning signals was through monitoring data analysis,which accounted for 82.44% of the total warning verification. Conclusion The automatic early warning system for infectious diseases in Baoding City is operating steadily,but there are shortcomings such as low timely response rate,low positive rate of suspected events,and weak correlation with the number of reports,so it is necessary to continue to improve the system functions and parameter settings.

Key words: Automatic warning system, Infectious disease, Application effect, Early warning model, Timely response rate