ABSTRACT
Objective TostudytheinfluenceofmeteorologicalconditionschangingontheactivityintensityofinfluenzaA (H1 N1 )and to establish the prediction model of H1 N1 positive rate associated with meteorological factors.Methods TheinfluenzaA(H1N1)datafromAprilof2009toJanuaryof2011insentinelhospitalanddailymeteorologicaldatain the same period were collected,which were analyzed by Spearman correlation analysis.The prediction model was establishedusingChi-squaredautomaticinteractiondetector(CHAID).Results Weeklyaverageatmospheric pressure(r =0.50),highest atmospheric pressure (r =0.51 ),lowest atmospheric pressure (r =0.50),average temperature (r=-0.40),highest atmospheric temperature(r=-0.41),lowest atmospheric temperature(r=-0.39), precipitation(r=-0.23 )and average wind speed (r=-0.22 )were positively correlated with the activity intensity of H1N1(all P<0.05).Factors that affected H1N1 positive rate were lowest atmospheric pressure,average wind speed and precipitation(P<0.05 ).The prediction model of H1 N1 positive rate showed that the correct rate of prediction was 66.67%.Conclusion Lowestatmosphericpressure,averagewindspeedandprecipitationarecloselyassociatedwith the activity intensity of influenza A (H1 N1 ).CHAID method can be used to predict the H1 N1 epidemics.
ABSTRACT
<p><b>OBJECTIVE</b>To analyze and evaluate the application of China Infectious Diseases Automated-alert and Response System(CIDARS)in Zhejiang province.</p><p><b>METHODS</b>Data through the monitoring program in 2012 was analyzed descriptively and compared with the incidence data in the same period as well information related to public health emergency events.</p><p><b>RESULTS</b>A total of 14 292 signals were generated on 28 kinds of infectious diseases in the system, in Zhejiang province. 100% of the signals had been responded and the median time to response was 0.81 hours. 123 signals (0.86%)were preliminarily verified as suspected outbreaks and 33 outbreaks were finally confirmed by further field investigation, with a positive ratio of 0.23% . Information related to regional distribution showed significant differences which reflecting a positive correlation between the numbers of diseases and the time of early-warning(r = 0. 97, P < 0.01). Distribution of information related to different types of diseases was also significantly different, showing a positive correlation between the prevalent strength of the disease and the amount of information in a specific area(r = 0.80, P < 0.01).</p><p><b>CONCLUSION</b>CIDARS had a good performance which 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 for different regions and diseases.</p>
Subject(s)
Humans , China , Epidemiology , Communicable Disease Control , Methods , Communicable Diseases , Epidemiology , Disease Outbreaks , Incidence , Population Surveillance , Methods , Public HealthABSTRACT
Objective To evaluate the performance of China Infectious Disease Automatedalert and Response System(CIDARS). Methods A retrospective analysis was conducted on data related to the warning signals, the outcome of signal verification, the field investigation of CIDARS,and the emergent events reported through Public Health Emergency Events Surveillance System from July 1,2008 to June 30, 2010 in Zhejiang province. The performance of CIDARS was qualitatively evaluated by indicators on its sensitivity and rote of false alarm. Results In total, 26 446 signals were generated by the system which involving 17 diseases, with an average of 2.83 signals per country per week. Among all the signals, 99.95% of them were responded. 0.90% of the signals were judged as suspected events via the preliminary verification, and 30 outbreaks were finally confirmed by field investigation. The sensitivity of the system was 69.77% with the false alarm rate as 1.39%. Conclusion The system seemed to have worked on the outbreak early warning of infectious diseases and could directly reflect the anomaly event emerged from the infectious disease reporting system.However, more efforts should be paid to the following areas as how to decrease the false positive signals, select suitable thresholds and increase the quality of data in order to enhance the accuracy of the system.