Epidemiological characteristics and time series analysis of scarlet fever in Wujin district,Changzhou city from 2012 to 2022
Objective To understand the epidemiological characteristics and incidence trends of scarlet fever in Wujin district of Changzhou city,Jiangsu province from 2012 to 2022,so as to provide basis for the prevention and control of scarlet fever.Methods The descriptive epidemiological methods were used to analyze the characteristics of scarlet fever in Wujin district from 2012 to 2022.The seasonal autoregressive integrated moving average(SARIMA)model was established based on the monthly reported cases with scarlet fever from 2012 to 2021 for forecasting the incidence of scarlet fever in 2022.Results There were 557 cases with scarlet fever reported in Wujin district from 2012 to 2022,with the average annual reported incidence rate of 3.55/100 000.The annual reported incidence rate of 5.07/100 000 was the highest in 2014,and then the incidence rate generally showed downward trend(x2=14.256,P＜0.001).The peaks of incidence were from March to June and from November to January of the next year.The ratio of the male to the female was 1.64∶1,and the children aged 4-8 years were the main and accounted for 76.66％of the total number of cases.The SARIMA(1,0,0)(1,0,1)12 was the optimal model which passed test,and the parameter estimation was with statistically significant,and the residual of Ljung-Box test was white noise sequence(P=0.799).The monthly number of the cases suffered from scarlet fever in 2022 were predicted with this model,and the actual number of the cases fell within the 95％CI of the predicted number of the cases.Conclusions Since 2014,the reported incidence rates of scarlet fever in Wujin district shows decreasing trend.The SARIMA model can be used for short-term prediction of the incidence of scarlet fever,and providing reference for epidemic prevention and control.The prevention and control measures of scarlet fever should be strengthened in kindergartens and primary schools.
Scarlet feverEpidemiological characteristicsSARIMA model
万方数据
