Prevalence of hemorrhagic fever with renal syndrome in Yiyuan County, China, 2005–2014

Xue

et al. 2018

Sci Rep

Zhejiang Province is one of the six provinces in China that has the highest incidence of haemorrhagic fever with renal syndrome (HFRS). Data on HFRS cases in Zhejiang Province from January 2007 to July 2017 were obtained from the China Information Network System of Disease Prevention and Control. Joinpoint regression analysis was used to observe the trend of the incidence rate of HFRS. The monthly incidence rate was predicted by autoregressive integrated moving average(ARIMA) models. Spatial autocorrelation analysis was performed to detect geographic clusters. A multivariate time series model was employed to analyze heterogeneous transmission of HFRS. There were a total of 4,836 HFRS cases, with 15 fatal cases reported in Zhejiang Province, China in the last decade. Results show that the mean absolute percentage error (MAPE) of the modelling performance and the forecasting performance of the ARIMA model were 27.53% and 16.29%, respectively. Male farmers and middle-aged patients account for the majority of the patient population. There were 54 high-high clusters and 1 high-low cluster identified at the county level. The random effect variance of the autoregressive component is 0.33; the spatio-temporal component is 1.30; and the endemic component is 2.45. According to the results, there was obvious spatial heterogeneity in the endemic component and spatio-temporal component but little spatial heterogeneity in the autoregressive component. A significant decreasing trend in the incidence rate was identified, and obvious clusters were discovered. Spatial heterogeneity in the factors driving HFRS transmission was discovered, which suggested that a targeted preventive effort should be considered in different districts based on their own main factors that contribute to the epidemics.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Spatial-temporal characteristics and the epidemiology of haemorrhagic fever with renal syndrome from 2007 to 2016 in Zhejiang Province, China

Xue

et al. 2018

Sci Rep

“…The ARIMA model is one of the most popular univariate time series models, which is widely used in infectious disease modeling [29,30,31]. The ARIMA model is based on the ARMA models, linearly expressing the current human Echinococcosis cases with its previous cases (autoregressive) and the residual series (moving average).…”

Section: Methodsmentioning

confidence: 99%

Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics

Zhang

Zheng

et al. 2017

IJERPH

Echinococcosis, which can seriously harm human health and animal husbandry production, has become an endemic in the Xinjiang Uygur Autonomous Region of China. In order to explore an effective human Echinococcosis forecasting model in Xinjiang, three grey models, namely, the traditional grey GM(1,1) model, the Grey-Periodic Extensional Combinatorial Model (PECGM(1,1)), and the Modified Grey Model using Fourier Series (FGM(1,1)), in addition to a multiplicative seasonal ARIMA(1,0,1)(1,1,0)4 model, are applied in this study for short-term predictions. The accuracy of the different grey models is also investigated. The simulation results show that the FGM(1,1) model has a higher performance ability, not only for model fitting, but also for forecasting. Furthermore, considering the stability and the modeling precision in the long run, a dynamic epidemic prediction model based on the transmission mechanism of Echinococcosis is also established for long-term predictions. Results demonstrate that the dynamic epidemic prediction model is capable of identifying the future tendency. The number of human Echinococcosis cases will increase steadily over the next 25 years, reaching a peak of about 1250 cases, before eventually witnessing a slow decline, until it finally ends.

“…Adjustments to policies and practices should be performed in a timely manner. However, although many studies have analyzed the HFRS epidemic situation in Shandong Province[4,6,7], we do not have a comprehensive and clear understanding of the current epidemic foci of HFRS. For this reason, we conducted a systematic analysis of surveillance information on HFRS in Shandong Province from 2012 to 2015 to assess the severity of the situation and determine the new epidemic foci in Shandong Province.…”

Section: Introductionmentioning

confidence: 99%

Epidemic foci of hemorrhagic fever with renal syndrome in Shandong Province, China, based on patients, rodents and molecular epidemiology characteristics, 2012-2015

Zheng

et al. 2019

Preprint

Background Hemorrhagic fever with renal syndrome (HFRS), an infectious disease caused by hantaviruses, is endemic in China and remains a serious public health problem. Historically, Shandong Province has had the largest HFRS burden in China. However, we do not have a comprehensive and clear understanding of the current epidemic foci of HFRS in Shandong Province. Methodology/Principal Findings The incidence and mortality rates were calculated, and a phylogenetic analysis was performed after laboratory testing of the virus in rodents. Spatial epidemiology analysis was applied to investigate the epidemic foci, including their sources. A total of 6,206 HFRS cases and 59 related deaths were reported in Shandong Province. The virus carriage rates of the rodents Rattus norvegicus, Apodemus agrarius and Mus musculus were 10.24%, 6.31% and 0.27%, respectively. The phylogenetic analysis indicated that two novel viruses isolated from R. norvegicus in Anqiu City and Qingzhou City were dissimilar to the other isolated strains, but closely related to strains previously isolated in northeastern China. Three epidemic foci were defined, two of which were derived from the Jining and Linyi epidemic foci, respectively, while the other was the residue of the Jining epidemic focus. Conclusions/Significance The southeastern and central Shandong Province are current key HFRS epidemic foci dominated by A. agrarius and R. norvegicus, respectively. Our study could help local departments to strengthen prevention and control measures in key areas to reduce the hazards of HFRS.