[Characteristics and stability of surveillance data on respiratory syndrome, during the Shanghai World Expo in Pudong New District].

OBJECTIVE: To reveal the characteristics and stability of the system through the analyzing the surveillance data of respiratory-feverous syndrome via the syndromic surveillance system which was established during the Shanghai World Expo in Pudong New District and provide references for the development and operation optimization on this Mass Gatherings Surveillance Systems. METHODS: Data used was from the surveillance data of respiratory-feverous syndrome collected from Pudong New District Syndromic Surveillance System, through May 1 to October 31, 2010. On the basis of description of data characteristics, correlation analyses were conducted, when compared to the surveillance data of respiratory-feverous syndrome and Pudong influenza-like illness (ILI) used as reference. Comparison of variances on the surveillance data and the report lag time of the earlier and later surveillance periods were also carried out to evaluate the quality and stability of data. RESULTS: Reports on the respiratory-feverous syndrome showed a peak in late September with day-of-week effects and holiday effects. Correlation between respiratory-feverous syndrome and ILI was the strongest in the same day (r = 0.596, P < 0.05). In the earlier surveillance period from 2010-05-01 to 2010-07-31, the correlation between respiratory-feverous syndrome and ILI was not obvious (r = -0.058, P > 0.05); however, the two-time series showed consistent trend with the correlation coefficient as 0.798 (P < 0.05), in the later period from 2010-08-01 to 2010-10-31. In addition, variability of the surveillance data on respiratory-feverous syndrome was less in the later period than in the earlier one, with quality of the report on related data better in the later period. Analyses on the correlations of reference sequence, variability and quality of report indicated that the stability of the later surveillance period was better than the earlier one. CONCLUSION: Only with the operation of syndromic surveillance system for a certain period of time, could data in the system maintain stability. Surveillance data showed both day-of-week effects and holiday effects, suggesting that there was a need to choose early warning models with short baseline data.

[Comparison on the performance of both temporal and temporal-spatial models for outbreak detection through China Infectious Diseases Automated-alert and Response System (CIDARS)].

OBJECTIVE: To analyze the pilot results of both temporal and temporal-spatial models in outbreaks detection in China Infectious Diseases Automated-alert and Response System (CIDARS) to further improve the system. METHODS: The amount of signal, sensitivity, false alarm rate and time to detection regarding these two models of CIDARS, were analyzed from December 6, 2009 to December 5, 2010 in 221 pilot counties of 20 provinces. RESULTS: The sensitivity of these two models was equal (both 98.15%). However, when comparing to the temporal model, the temporal-spatial model had a 59.86% reduction on the signals (15 702) while the false alarm rate of the temporal-spatial model (0.73%) was lower than the temporal model (1.79%), and the time to detection of the temporal-spatial model (0 day) was also 1 day shorter than the temporal model. CONCLUSION: Comparing to the temporal model, the temporal-spatial model of CIDARS seemed to be better performed on outbreak detection.

[Preliminary application on China Infectious Diseases Automated-alert and Response System (CIDARS), between 2008 and 2010].

OBJECTIVE: To analyze the results of application on China Infectious Diseases Automated-alert and Response System (CIDARS) and for further improving the system. METHODS: Amount of signal, proportion of signal responded, time to signal response, manner of signal verification and the outcome of each signal in CIDARS were descriptively analyzed from July 1, 2008 to June 30, 2010. RESULTS: A total of 533 829 signals were generated nationwide on 28 kinds of infectious diseases in the system. 97.13% of the signals had been responded and the median time to response was 1.1 hours. Among them, 2472 signals were generated by the fixed-value detection method which involved 9 kinds of diseases after the preliminary verification, field investigation and laboratory tests. 2202 signals were excluded, and finally 246 cholera cases, 15 plague cases and 9 H5N1 cases as well as 39 outbreaks of cholera were confirmed. 531 357 signals were generated by the other method - the 'moving percentile method' which involved 19 kinds of diseases. The average amount of signal per county per week was 1.65, with 6603 signals (1.24%) preliminarily verified as suspected outbreaks and 1594 outbreaks were finally confirmed by further field investigation. For diseases in CIDARS, the proportion of signals related to suspected outbreaks to all triggered signals showed a positive correlation with the proportion of cases related to outbreaks of all the reported cases (r = 0.963, P < 0.01). CONCLUSION: The signals of CIDARS were responded timely, and the signal could act as a clue for potential outbreaks, which helped enhancing the ability on outbreaks detection for local public health departments.

[The effectiveness of China Infectious Disease Automated-alert and Response System (CIDARS) in the local regions].

OBJECTIVE: To understand the effectiveness of China Infectious Disease Automated-alert and Response System (CIDARS) for outbreak detection at the regional level. METHODS: Two counties in Hunan province (Yuelu and Shuangfeng county) and two counties in Yunnan province (Xishan and Gejiu county) were chosen as the study areas. Data from CIDARS were analyzed on the following items: reported cases, warning signals, the time interval of signal response feedback, way of signal verification, outcome of signal verification and field investigation, from July 1, 2008 to June 30, 2010. RESULTS: In total, 12 346 cases from 28 kinds of diseases were reported, and 2096 signals of 19 diseases were generated by the system, with an average of 4.94 signals per county per week. The median of time interval on signal verification feedback was 0.70 hours (P(25)-P(75): 0.06 - 1.29 h) and the main way of signal preliminary verification was through the review of surveillance data (account for 63.07%). Among all the signals, 34 of them (1.62%) were considered to be related to suspected events via the preliminary verification at the local level. Big differences were found to have existed on the proportion of signals related to the suspected events of the total signals among the four counties, with Shuangfeng county as 4.71%, Yuelu county as 1.88%, Gejiu county as 0.95% and Xishan county as 0.58%. After an indepth study on the fields of suspected events, 12 outbreaks were finally confirmed, including 5 on rubella, 4 on mumps, 2 on influenza and 1 on typhoid fever. CONCLUSION: CIDARS could be used to assist the local public health institutions on early detection of possible outbreaks at the early stage. However, the effectiveness was different depending on the regions and diseases.