Objective To study the influencing factors for hemorrhagic fever with renal syndrome (HFRS) in Huludao, Liaoning province, China and to construct an incidence prediction model using a Bayesian network. Methods Data about incidence, host, and meteorological conditions of HFRS were collected at surveillance sites in Huludao from January 2008 to October 2018. The tabu search algorithm was used to learn the structure of the Bayesian network, and maximum likelihood estimation was applied to estimate Bayesian network parameters. Results At the 0.01 level, factors associated with the incidence of HFRS in Huludao included: rodent density; the average wind speed and sunshine hours in the contemporaneous month; the average maximum temperature, relative humidity, and normalized difference vegetation index with a one-month lag; and the average temperature, average minimum temperature, average atmospheric pressure, and precipitation with a two-months lag. The correlation coefficients were 0.691, 0.689, 0.345, -0.635, -0.631, -0.674, -0.714, -0.746, 0.650, and -0.643, respectively. When using meteorological and host data to predict the incidence of HFRS, the accuracy of the Bayesian network model was 85.00% (17/20), the precision was 83.33% (10/12), and the area under the receiver operating characteristic curve was 0.919. Conclusion The Bayesian network-based incidence prediction model shows a relatively high prediction accuracy for HFRS in Huludao, providing a certain reference for HFRS prevention and control.

Objective To determine the prevalence of hantavirus in rodents based on surveillance data in residential areas of Huludao for the formulation of preventive and control strategies in humans. Methods Rodent cages were used to capture small mammals. Lung samples of the subjects were then taken for detection of Hantavirus antigens using indirect immunofluorescence assay (IFA). Genotyping was conducted using RT-PCR. Results In 2005 and 2006, 254 Rattus norvegicus, 17 Mus musculus and 5 Apodemus agrarius were captured in nine residential areas and two wild fields in Huludao. The virus-carrying rates were 4.72% in R. norvegicus and 5.88% in M. musculus, respectively. Nine strains of Hantavirus were amplified using the RNA derived from the positive lung tissues of R. norvegicus. According to genotyping results, all were identified as Seoul virus (SEOV). A strain of SEOV was also isolated. Hantavirus was not detected from M. musculus and A. agrarius. Conclusion R. norvegicus was the primary host of Hantavirus in residential areas of Huludao and all rodent-carrying Hantavirus strains were identified as SEOV.

Objective To determine the epidemiological characteristics, long-term trend and regional typing of hemorrhagic fever with renal syndrome (HFRS) in Huludao city, which would provide the basis for development of specific control strategies. Methods Epidemiological data on HFRS in the city were retrieved from the National Noticeable Infectious Disease Reporting System for the analysis. Indirect immunofluorescence assay (IFA) was performed to identify the Hanta viral antigen in rodent lung samples for the calculation of virus-carrying rates. The hemagglutination inhibition test was adopted to serotype the serum samples collected from patients at the recovery stage. Results Remaining at a high level since 1998, the HFRS incidence sharply declined from 2006. An onset peak in spring was noticeable, while the number of cases from February to June accounted for 61.37% of the overall patients. The young and middle-age male farmers were at a high risk of contracting the disease. The rat density and virus carrying rates were high, and the SEO serotype was identified from the recovering patients’ sera samples. Conclusion Noticeable decrease of the HFRS incidence in Huludao city was attributable to large-scale vaccination. The SEO serotype was prevalent in this region, while transformation into mixed-type was not yet observed. It is essential to intensify the rodent prevention and control measures and strengthen the observation of the long-term effect of vaccination among inoculated populations. Supplementary immunization may be conducted when necessary.

【Abstract】 Objective To study the relationships of meteorological factors, animal host and hemorrhagic fever with renal syndrome （HFRS） incidence, and construct mathematical model for the forecast of HFRS. Methods Firstly, air pressure, air temperature, relative humidity, precipitation, sunshine duration and sunshine percentage were selected from all meteorological factors of Huludao city. Secondly, Pearson, Kendall and Spearman correlation analyses were used to describe the relationships among meteorological factors, animal host situation including rodent density and viral carriage of rodents and HFRS incidence. Thirdly, Bayesian discrimination analysis (BDA) was adopted to forecast HFRS incidence on the premise of meteorological factors and animal host formation as explanatory variables. Results There was the close relation between rodent density and annual HRFS incidence(r＝0.738, P＝0.000), and the rodent density was also influenced by sunshine duration, sunshine percentage and precipitation.  A positive correlation was found between rodent density and sunshine time(r＝0.494, P＝0.016), and the correlation between rodent density and precipitation was negative(r＝-0.350, P＝0.101). The step wise BDA and all variables discrimination analysis had all good effect on the forecasting of HFRS based on meteorological factors and animal host data. The accuracy rate of fitting and leave?one?out (LOO) cross-validation of stepwise BDA all reached 82.6%(19/23) , however, that of  fitting of all variables BDA was 90.9%(20/22) and 81.8%(18/22) for LOO cross-validation. For next year incidence prediction, the accuracy rates of fitting and LOO cross-validation step-wise were all 86.4%(19/22) for step-wise BDA, while for all variables BDA, its accuracy rate of fitting was 100%(21/21) and that of LOO cross-validation was 57.1%(12/21). Conclusion HFRS incidence was related to animal epidemic situation which was influenced by meteorological factors. Stepwise BDA offered useful information in the discrimination and forecasting of HFRS incidence, which had a good application in the future.

Objective To study the superiority and application prospect of support vector machine(SVM) on the forecast of the incidence of hemorrhagic fever with renal syndrome(HFRS).Methods Firstly,the routine meteorological data of Huludao city including average air pressure,average temperature,relative humidity,precipitation and sunshine time and the epidemiologic information of animal disease including rodent density and rodents borne virus from 1984 to 2006 were used as predictable variables.All the variables were limited to the range from 0 to 1.The whole data atlas were separated into training atlas and test atlas.The test atlas were made up of 1/3 individuals(trunc) randomly sampled from data atlas,and other samples were composed of training atlas.Secondly,SVM was applied to the HFRS incidence prediction and the SVM model was constructed by software R2.60.Finally,the performance of SVM,back-propagation(BP) and radial basis function(RBF) Neural Networks were compared by computing the sum square error(SSE).The above procedures were repeated for 10 replications.Results The mean and standard diviation of SSE of SVM for training atlas was(0.031±0.009),while those of BP and RBF neural network were(0.074±0.030) and(0.082±0.018),respectively.For the test atlas,the mean and standard diviation of SSE of SVM was(0.067±0.021),while those of BP and RBF neural network were(0.073±0.022) and(0.089±0.036),respectively.Conclusion As a new pattern recognition method developed on the basis of statistics theory in recent years,SVM had higher forecast precision and stronger generalization ability to solve the small sample size and the indentification of nonlinear and high-dimension model,SVM was reliable for the prediction of HFRS incidence,which could serve as a reference method for the HFRS prediction.