Epidemic Characteristics and Spatio-Temporal Patterns of HFRS in Qingdao City,China,2010-2022

Objective This study investigated the epidemic characteristics and spatio-temporal dynamics of hemorrhagic fever with renal syndrome(HFRS)in Qingdao City,China.Methods Information was collected on HFRS cases in Qingdao City from 2010 to 2022.Descriptive epidemiologic,seasonal decomposition,spatial autocorrelation,and spatio-temporal cluster analyses were performed.Results A total of 2,220 patients with HFRS were reported over the study period,with an average annual incidence of 1.89/100,000 and a case fatality rate of 2.52%.The male:female ratio was 2.8:1.75.3%of patients were aged between 16 and 60 years old,75.3%of patients were farmers,and 11.6%had both“three red”and“three pain”symptoms.The HFRS epidemic showed two-peak seasonality:the primary fall-winter peak and the minor spring peak.The HFRS epidemic presented highly spatially heterogeneous,street/township-level hot spots that were mostly distributed in Huangdao,Pingdu,and Jiaozhou.The spatio-temporal cluster analysis revealed three cluster areas in Qingdao City that were located in the south of Huangdao District during the fall-winter peak.Conclusion The distribution of HFRS in Qingdao exhibited periodic,seasonal,and regional characteristics,with high spatial clustering heterogeneity.The typical symptoms of“three red”and“three pain”in patients with HFRS were not obvious.

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.