Mumps presents a serious threat to public health in China. We conducted a descriptive analysis to identify the epidemiological characteristics of mumps in Shandong Province. Spatial autocorrelation and space-time scan analyses were utilized to detect spatial-temporal clusters. From 2005 to 2014, 115745 mumps cases were reported in Shandong, with an average male-to-female ratio of 1.94. Mumps occurred mostly in spring (32.17% of all reported cases) and in children aged 5 to 9 (40.79% of all reported cases). The Moran’s I test was significant and local indicators of spatial autocorrelation (LISA) analysis revealed significant spatial clusters with high incidence. The results showed that the mid-west of Shandong Province and some coastal regions (Qingdao City and Weihai City) were high-risk areas, particularly in the center of the Jining City and the junction of Dongying City, Binzhou City and Zibo City. The results could assist local and national public health agencies in formulating better public health strategic planning and resource allocation.

Background: The early warning model of infectious diseases plays a key role in prevention and control. This study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect. Methods: Data on notified HFRS cases in Weifang city, Shandong Province were collected from the official website and Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases. Results: Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA (1, 0.11, 2)(1, 0, 1)₁₂: Akaike information criterion (AIC):-631.31; SARIMA (1, 0, 2)(1, 1, 1)₁₂: AIC: − 227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE):0.058; SARIMA: RMSE: 0.090). Conclusions: The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

Microbial communities play crucial roles in the global carbon cycle, particularly in peatland ecosystems under climate change. The peatlands of the Sanjiang Plain could be highly vulnerable to global warming because they are mainly located at the southern limit of northern peatlands. In this study, the alpha diversity and composition of bacterial communities in three different minerotrophic fens along a chronosequence were investigated. We captured a rich microbial community that included many rare operational taxonomic units (OTUs) but was dominated by a few bacterial classes that have frequently been detected in other peatland ecosystems. Notably, a large diversity of methanotrophs affiliated with Alpha- and Gammaproteobacteria was also detected. Bacterial alpha diversity and composition varied as a function of peat depth and its associated physical-chemical properties, such as total carbon, total nitrogen, pH and bulk density. We also found that bacterial community turnover (beta diversity) to be significantly correlated with soil age, whereas bacterial alpha diversity was not.

This study aimed to predict the incidence of mumps using a seasonal autoregressive integrated moving average (SARIMA) model, and provide theoretical evidence for early warning prevention and control in Zibo City, Shandong Province, China. Monthly mumps data from Zibo City gathered between 2005 and 2013 were used as a training set to construct a SARIMA model, and the monthly mumps in 2014 were defined as a test set for the model. From 2005 to 2014, a total of 8722 cases of mumps were reported in Zibo City; the male-to-female ratio of cases was 1.85:1, the age group of 1–20 years old accounted for 94.05% of all reported cases, and students made up the largest proportion (65.89%). The main serious endemic areas of mumps were located in Huantai County, Linzi District, and Boshan District of Zibo City. There were two epidemic peaks from April to July and from October to January in next year. The fitted model SARIMA (0, 1, 1) (0, 1, 1)₁₂ was established (AIC = 157.528), which has high validity and reasonability. The SARIMA model fitted dynamic changes of mumps in Zibo City well. It can be used for short-term forecasting and early warning of mumps.