Epidemiological Characteristics of Varicella Outbreaks—China,2006–2022

Introduction:Varicella outbreaks significantly disrupt schools and other child-centered institutions.This study aimed to identify patterns and epidemiological features of varicella outbreaks in China from 2006 to 2022.Methods:Data were extracted from outbreak reports submitted to the Public Health Emergency Reporting Management Information System within the specified timeframe.Analytical methods included Spearman correlation tests and the Mann-Kendall trend tests,conducted using R software to analyze and summarize reported data.Additionally,statistical analyses of trends and epidemiological characteristics were performed using SPSS software.Results:Between 2006 and 2022,a total of 11,990 varicella outbreaks were reported in China,resulting in 354,082 cases.The attack rates showed a decreasing trend over the years(Z=−4.49,P<0.05).These outbreaks occurred in two peaks annually.The eastern region accounted for the highest number of outbreaks(31.53%),followed by the southwestern(24.22%)and southern(17.93%)regions.Varicella outbreaks were most common in elementary schools.Most of the outbreaks(60.43%)were classified as Grade IV(general)severity,with 86.41%of the outbreaks having 10–49 cases.The median and inter-quartile ranges(IQR)of the duration of outbreaks,response time,and case counts were 21(10,39)days,4(0,12)days,and 23(16,35)cases,respectively.These variables showed a positive correlation(P<0.001).Conclusions:Varicella outbreaks exhibited fluctuating trends,initially decreasing until 2012,followed by an increase,reaching the highest peak in 2018–2019.Continual monitoring of varicella epidemiology is necessary to assess the burden of the disease and formulate evidence-based strategies and policies for its prevention and control.

An Autoregressive Integrated Moving Average Model for Predicting Varicella Outbreaks — China, 2019

Introduction:Varicella,a prevalent respiratory infection among children,has become an escalating public health issue in China.The potential to considerably mitigate and control these outbreaks lies in surveillance-based early warning systems.This research employed an autoregressive integrated moving average(ARIMA)model with the objective of predicting future varicella outbreaks in the country.Methods:An ARIMA model was developed and fine-tuned using historical data on the monthly instances of varicella outbreaks reported in China from 2005 to 2018.To determine statistically significant models,parameter and Ljung-Box tests were employed.The coefficients of determination(R2)and the normalized Bayesian Information Criterion(BIC)were compared to selecting an optimal model.This chosen model was subsequently utilized to forecast varicella outbreak cases for the year 2019.Results:Four models passed parameter(all P<0.05)and Ljung-Box tests(all P>0.05).ARIMA(1,1,1)×(0,1,1)12 was determined to be the optimal model based on its coefficient of determination R2(0.271)and standardized BIC(14.970).Fitted values made by the ARIMA(1,1,1)×(0,1,1)12 model closely followed the values observed in 2019,the average relative error between the actual value and the predicted value is 15.2%.Conclusion:The ARIMA model can be employed to predict impending trends in varicella outbreaks.This serves to offer a scientific benchmark for strategies concerning varicella prevention and control.