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 establish a kinetic model for rat-to-rat and rat-to-human transmission of hemorrhagic fever with renal syndrome (HFRS), to simulate HFRS epidemics in Shenyang, China, and to provide a basis for the prevention and control of HFRS. Methods The incidence rates of HFRS in humans, virus-carrying rates among rodents, and density of rodents in Shenyang from 1984 to 2017 were collected. To simulate HFRS epidemics, a susceptible-infected model and a susceptible-infected-recovered model were established for rat-to-rat and rat-to-human transmission, respectively. Results From 1984 to 2017, the mean annual incidence rate of HFRS was 3.88/100 000 in Shenyang. From 1984 to 2013, the mean annual density of rodents and virus-carrying rate among rodents were 6.93% and 4.79%, respectively, in Shenyang. Compared with the actual incidence rate of HFRS, the incidence rate predicted by differential equations had a mean absolute error of 0.28. The expansion of vaccination coverage could lead to a lower incidence rate. If the vaccination coverage was expanded from 40.00% to 50.00%, the mean incidence of HFRS in Shenyang from 2005 to 2017 would decrease from 1.97/100 000 to 1.91/100 000. Conclusion Differential equations can be used to simulate the transmission dynamics of HFRS in Shenyang. Vaccination is an effective way to prevent HFRS. Vaccination coverage should be expanded to protect people susceptible to HFRS.

Objective To explore the spatial-temporal characteristics of hemorrhagic fever with renal syndrome (HFRS) in mainland China. Methods Demographic information and the annual number of incident HFRS cases in mainland China from 2004 to 2013 were collected. Retrospective time-space analysis (based on discrete poisson model) was conducted to detect the spatial and spatio-temporal clusters of HFRS cases in SaTScan (Version 9.1.1) after geographical information system database constructed via ArcGIS software (Version 9.3). Gravity centers of HFRS cases were calculated and depicted with bubble charts in Excel 2010. Results The number of HFRS cases decreased from 2004 to 2009 and increased with a gravity center northeast to that of population. The gravity center of HFRS cases in 2013 moved to southwest for 307.87 km compared to that of 2004 with the directional angle as 241.69 degree. The distance in longitude was 119.52 km and 282.61 km in latitude. The most likely cluster was in Heilongjiang, Jilin and Liaoning during 2004 to 2008 with the relative risk 9.22. The secondary cluster included four geographical areas, Shaanxi area (2008-2012), Shandong, Tianjin and Hebei area (2004-2005), Zhejiang province (2004-2007) and Jiangxi (2011-2013), the relative risks varied from 1.32 to 6.33. Conclusion There are significant spatio-temporal cluster pattern for the distribution of HFRS cases in mainland China, the epidemic status showed a weakening trend and the gravity center moved from northeastern part to northern China.

　　【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.

Objective To investigate the relationship between meteorological factors and incidence of arbo infectious disease,including endemic encephalitis B and endemic typhus,to built the Back-Propagation artificial neural network model and evaluate it. Methods The data of incidence of cerebrospinal meningitis and meteorological factors from 1981 to 1994 were collected and analyzed by SPSS for windows version 10.0; The model of Back-Propagation artificial neural network was built by Matlab,version 6.5. Results The incidences of endemic encephalitis B was negative correlated to annual mean atmospheric pressure ( P< 0.01),and was positive correlated to annual mean evaporation and the maximum temperature ( P< 0.05). The incidences of endemic typhus was negative correlated to annual mean temperature,the groud temperature( P< 0.05) and the minimum temperature ( P= 0.062). The MER and R² of endemic encephalitis B and endemic typhus model were 27.44%,98.09%and 29.00%,65.35%respectively. The relative error of endemic encephalitis B and endemic typhus was 80.00%and 120.86%respectively. Conclusion The incidence of arbo infectious disease is correlated to atmospheric pressure,evaporation,and temperature. The BP neural network model fits commonly for the study of arbo infectious diseases and needs further study.