ABSTRACT
Objective To understand the value of poisson distribution in monitoring and early warning of hand-foot-mouth disease( HFMD). Methods Alert data based on infectious disease reports from 2009 to 2011 were used to establish a database. Moving average method was employed to set the number of expected cases each week. The probability of outbreak was calculated by poisson's distribution. If the probability was smaller than a standard,the signal of early warning was sent out. Epidemiological investigation was used to verify it. Results Using this method,44 abnormal signals of HFMD were probed,and 33 signals were verified. The positive rate,sensitivity,specificity and Youden 's index were 75. 00%,94. 29%,35. 29% and 29. 58% respectively. Compared to the moving average control chart and the automatic early-warning information system of infectious diseases,it was more efficient. Conclusions The poisson distribution with the moving average method can be used as an effective method in early warning system of HFMD.
ABSTRACT
Objective To explore and analyze the spatial distribution characteristics of hand - foot - mouth disease in Hangzhou city. Methods Based on the incidence of hand - foot - mouth disease, the whole and local spatial autocorrelation analysis was used to test the spatial heterogeneity by the software GeoDa095i. Results A total of 5534 cases of hand-foot-mouth disease were reported in Hangzhou city in 2011 with the reported incidence of 63. 61/100000. The wholel Moran’s I index was 0. 33(P<0. 05)indicating that the disease had high spatial cluster occurrence. Local spatial autocorrelation analysis showed that several districts including Gongshu,Jianggan,Xihu and Xiaoshan had high cluster occurrence(LISA=0. 4135,0. 4600,0. 1785 and 0. 3508 respectively,all P<0. 05)while Chun′an county and Tonglu county had the low cluster occurrence(LISA=0. 6923 and 0. 6762 respectively,both P<0. 05). Conclusion The spatial distribution of hand - foot - mouth disease in Hangzhou city is not random distribution and has aggregation phenomenon.