As human contact with domestic and wild animals increases, zoonotic infectious diseases are becoming increasingly common. Pasteurella multocida, as one of the important pathogens, cannot be ignored due to its high prevalence in animal populations and potential threat to human health. This paper reviews the identification and classification methods of P. multocida and analyzes in detail its prevalence and host specificity in animals and humans, as well as the pathological changes and immune responses after infection. This paper offers a reference for fully understanding the basic characteristics and transmission rules of P. multocida and optimizing the prevention and treatment of P. multocida infection.

Objective To investigate the distribution of the subtypes, location, and number of lesion in echinococcosis cases in China, so as to provide a theoretical basis for the diagnosis, prevention, and management of echinococcosis. Methods Cases with complete information on lesion types based on images obtained in the 2012-2016 epidemiological survey of echinococcosis in China were selected for descriptive analysis of lesion subtype distribution. Regression analysis was performed to determine the relationship between the number of lesions in each subtype and the number of cases in the corresponding subtype. Results Among the 4 014 cases included in the study, there were no significant differences between male and female patients in the proportions of cystic and alveolar echinococcosis cases in each subtype of lesion (all P>0.005). For cystic echinococcosis cases, there were significant differences in the proportion of cases among different provinces of each subtype （χ²=58.803～534.097, all P<0.05）, and there were significant differences in the proportion of cases among different occupations except for type III （subtype CL,Ⅱ,Ⅳ,Ⅴ：P<0.05 by Fisher's exact probability test; subtypeⅠ：χ²=104.534, P<0.001）. For alveolar echinococcosis cases, there were significant differences in the proportion of cases among different provinces of each subtype, i.e., infiltrative, colliquative and cavitary, calcification （all P<0.05 by Fisher's exact probability test）, and there were significant differences in the proportion of cases among different occupation of each subtype （Fisher's exact probability test： P<0.001; P=0.009; P<0.001）. Colliquative and cavitary subtype with cystic lesion as well as cystic and calcified type with alveolar lesion were not found in cases with both cystic and alveolar lesions. The proportions of cystic lesions in different stages decreased significantly in turn in Sichuan Province (Z=-2.630, P=0.009) and Xinjiang Uygur Autonomous Region (Z=-2.254, P=0.024). Not less than 90.92% of the lesions of all subtypes of cystic echinococcosis cases were located in the liver. Regression analysis showed a significant negative exponential relationship between the number of lesions of each subtype and the number of cases with the corresponding subtype lesion (all P<0.05). Conclusions There are regional and occupational characteristics in the subtype of lesions in echincoccosis cases in China, and the lesions are mainly located in the liver. The cross-sectional analysis of lesion classification can be used as an important supplement to the traditional epidemiological analysis and provide an important theoretical basis for the control, prevention, and treatment of echinococcosis.

Objective To develop a marmot information collection and reporting system to improve the efficiency of surveillance of epizootic plague in marmots, and to provide a scientific basis for the formulation of plague prevention and control strategy and the basic research. Methods A field investigation was performed in marmot plague foci of Qinghai province, China, 2014, and the spatial distribution and location data of marmots were collected by the Global Positioning System(GPS), involving 352 sample sites of Yushu Tibetan autonomous prefecture, Guoluo Tibetan autonomous prefecture, Wulan county, Jianzha county, Xinghai county, and Qilian county. Five environment variables of height, slope, slope aspect, vegetation coverage, and grassland type, as well as the latitude and longitude of sample sites were obtained. The data of the sample sites were displayed in China Geodetic Coordinate System 2000 using ArcGIS 10.2 software. By analyzing the work requirements and workflow of Marmota himalayana surveillance, database model design, system structure design and system model construction were carried out. Through the application of ArcGIS Runtime SDK for Android related components and the lightweight spatial database supported by SpatiaLite mobile terminals to develop the marmot information collection system in the Eclipse development environment by intergrating various data into the mobile geographic information system platform. Results Based on the habitat information of M. himalayana in natural plague foci of Qinghai province, the established marmot information collection system V3.0 had the following basic functions:management and analysis of plague surveillance data; generation of spatial data, attribute data, and associated pictures, as well as storage and export of spatial data and attribute data with database files; import of data about historical plague foci, route planning, real-time positioning and navigation, route playback, etc. The marmot information collection system changed the traditional paper-based combined GPS, reduced the workload of investigators, and improved the work efficiency. The unified survey method ensured the consistency of data and the standardization of work. Conclusion The marmot information collection and reporting system V3.0 can improve the monitoring mode of marmot plague, increase the probability of detection of animal plague, and assist in making decisions on emergency response to animal or human plague.

Objective The prediction model of dengue fever based on back propagation (BP) neural network was constructed and verified, which provided a reference for the prevention and control of dengue. Methods Based on the temporal and spatial data of dengue fever epidemics and geographical environment, the spatio-temporal distribution characteristics of dengue fever and the spatial autocorrelation of dengue fever cases were analyzed. Pearson's method was used to analyze the correlation between dengue fever and various influencing factors in Guangzhou and Foshan areas. Then, Matlab 7.0 software was used to complete BP neural network prediction model construction, training and simulation. Results From August to October 2014, the highest incidence of dengue cases in Guangzhou and Foshan area was 90, 386, 456 cases/km², respectively, and the spatial distribution of the epidemics mainly concentrated in Guangzhou (P=0.001, Z=134.402 5). The global Moran's I index was 0.606 5. In the same month of dengue fever, the local epidemic situation of dengue in Guangzhou and Foshan district was significantly different. The outbreaks of the local cases were correlated to the epidemics of the previous month (July, August and September) (local cases and imported cases), meteorological (temperature, humidity and precipitation), and social (population density, urban and rural residential land, forest, farmland)factors. The correlation coefficient between the predicted value and the true value was 0.773 and the root mean square error was 7.522 0. Conclusion Dengue epidemics in Guangzhou and Foshan areas was not randomly distributed but obviously spatially clustered. The occurrence of dengue fever is influenced by many factors and the BP neural network model can effectively predict the temporal and spatial distribution of dengue fever in Guangzhou and Foshan areas.

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.

Objective To describe the procedure of building Elman neural network model, and explore the value of potential application of the above model. Methods Monthly incidence of hemorrhagic fever with renal syndrome(HFRS) in China from 2004 to 2013 was used to build Elman neural network model and SARIMA model and forecasted the monthly incidence of HFRS in China from January 2014 to September 2014. The fitting and prediction effects of the two models were compared. Results For training sample, MAE, MAPE and RMSE of Elman neural network were 0.0088, 0.1191 and 0.0127 respectively; MAE, MAPE and RMSE of SARIMA model were 0.0111, 0.1268 and 0.0206 respectively. For predicting sample, MAE, RMSE and MAPE of Elman neural network were 0.0079, 0.1180 and 0.0096 respectively; MAE, RMSE and MAPE of SARIMA model were 0.0178, 0.2778 and 0.1861 respectively. Conclusion Elman neural network fits and forecasts the HFRS incidence trend in China well, and the fitting and prediction effect is superior to the SARIMA model, which is of great application value for the prevention and control of hemorrhagic fever with renal syndrome.

Objective To analyze the epidemiological features of hemorrhagic fever with renal syndrome (HFRS) and associated environmental risk factors for HFRS and associated environmental risk factors for HFRS in Liaoning province, China during 2005-2007, and to provide a scientific basis for HFRS control measures. Methods The epidemic data of HFRS in Liaoning province were collected. Analysis was performed to determine the correlation between the epidemic features of HFRS and environmental factors such as mean temperature, relative humidity, rainfall, sunshine, urban rodent density, rural rodent density, and virus?carrying rate. Results There were 7298 cases of HFRS in Liaoning province from 2005 to 2007, and 78 of them died. The mean annual incidence of HFRS was 5.78/100 000, and the mortality was 0.06/100 000; the incidence and mortality were higher in males than in females; 59.55% of the cases and 69.23% of fatal cases were aged 35-60 years; 61.98% of the cases and 56.41% of fatal cases were farmers. The peak of incidence appeared mainly in November to January and March to May, while the trough period was in July to October, showing the seasonal characteristics in mixed epidemic area; the mean annual incidence of HFRS was relatively high in the cities of Benxi (13.70/100 000), Huludao (12.92/100 000), Jinzhou (11.30/100 000), Dandong (10.21/100 000), and Fushun (9.84/100 000). The incidence of HFRS was negatively correlated with temperature but positively correlated with rainfall, rural rodent density, and virus-carrying rate; the Spearman rank correlation coefficients were -0.351, 0.400, 0.449, and 0.377, respectively, and the P values were 0.023, 0.009, 0.003, and 0.016, respectively. Conclusion In Liaoning province, HFRS is prevalent mainly in winter and spring and among young male farmers. The prevalence of HFRS is closely related to temperature, rainfall, rural rodent density, and virus-carrying rate in the same year.

Objective To analyze the molecular genetic characteristics of Hantavirus strain DX1101 isolated from the imported Rattus norvegicus. Methods We separated the rat lung, extracted virus RNA, and then used codehop RT-PCR to amplify L gene segment of Hantavirus and nested RT-PCR for M gene segment. The obtained products were then sequenced and analysed by phylogenetic tree. Results Homological and phylogenetic analysis of the two gene amplification products showed that the Hantavirus strain belongs to genetic subtype SEOV. The phylogenetic tree of M gene showed that the strain is genetically closest to the strain IR461 found in England. Conclusion This study identified Hantavirus in R. norvegicus seized from inboard containers. Therefore, it is of great significance to enhance the quarantine of inboard containers.

Objective To investigate the breeding features of tick population and the Lyme disease pathogen carriers in Ningbo port. Methods All ticks were manually collected from grassland with white banner collection method, and the DNA fragments of Lyme leptospira were detected by PCR. Results A total of 872 ticks were captured from Ningbo port, which were identified to be of 1 family,3 genuses and 3 species,including the Ixodes sinensis Teng,Haemaphysalis longicornis Neumann and Rhipicephalus haemaphysaloides Supino. The H. longicornis Neumann was the dominant specie, accounting for 97.36%. Ticks were prevalent from March to September, with peak population seen from March to June and a sharp decline in July. The DNA fragments of Lyme leptospira were negative. Conclusion There is no evidence indicating that Ningbo port is a natural focus of Lyme disease

Objective To explore the frontier quarantine strategies for prevention and control of exotic vectors. Methods Exotic vectors first intercepted at Ningbo port from 2004 to 2009 were analyzed along with the case study of hazards brought by exotic vectors. Results With many years of strict frontier quarantine on international sailing ships, containers and cargos, 39 exotic vectors of 3 species, Supella supellectilium, Cochliomyia macellaria and Panchlora nivea were captured for the first time from 10 batches, as well as 9 species from 12 batches, which were sparsely distributed in a handful of provinces other than Zhejiang province. Hence, hazardous exotic vectors were effectively prevented from invading or spreading in China. Conclusion Frontier quarantine is irreplaceably essential to preventing the import of exotic hazardous vectors.

Objective A network system with electronic warning functions was developed for monitoring and control of regional termite infestation, providing a new technique and approach for termite control in China. Methods The electronic monitoring and control devices were planted in the areas of active termites to assess the reliability of infestation alarms made by the devices. Results The infestation warning network system based on type B monitoring and control devices provided accurate warnings in terms of the infestations of common termites such as Coptotermes formosanus, Reticulitermes speratus and Odontotermes formosanus. Conclusion The electronic warning network system for termite monitoring and control may be used for the termite prevention and control in housing construction, reservoirs, dams and landscaping and greening.

　　【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.

　　【Abstract】　Objective　To grasp the dynamic change of the population distribution and seasonal fluctuation of cockroach in Dalian. Methods　The baits and traps method was used in the surveillance. Results　There were 3 species of cockroaches, and Blattella germanica was the dominant species, accounting for 96.89%. Periplaneta japonica and P.fuliginosa accounted for 3.02% and 0.09% respectively. The activity peak of the cockroach was from July to  October. The order of cockroach distributed in different trade was farm produce trade market, residential area, restaurant, hotel and hospital. Conclusion　B.germanica was the dominant species in Dalian, suggesting that it should put the emphasis on the control of it 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 study the superiority and application of generalized regression neural network(GRNN) combination forecast model in the forecast of hemorrhagic fever with renal syndrome(HFRS) incidence. Methods Establish the GM(1,1) model and auto regressive integrated moving average(ARIMA) model based on the data of HFRS of Dandong, Shenyang and Chaoyang Liaoning province, from 1990 to 2001 respectively. The forecasting values of the two models were used as input of GRNN. Train the sample and forecast the value. Compare the forecasting effect of the three models. Results The mean error rate(MER) of GM(1,1) model, ARIMA model and GRNN combination model for Liaoning province were 13.5143%, 25.0814% and 5.5755% respectively. The R² of the three models were 0.8961, 0.6997 and 0.9837 respectively. The MER of GM(1,1) model, ARIMA model and GRNN combination model for Dandong were 19.7329%, 20.6275% and 14.0789% respectively. The R² values of three models were 0.8112, 0.7628 and 0.8750 respectively. The MER of GM(1,1) model, ARIMA model and GRNN combination model for Shenyang were 15.1421%, 18.0584% and 14.3592% respectively. The R² values of three models were 0.8757, 0.7889 and 0.8585 respectively. The MER of GM(1,1) model, ARIMA model and GRNN combination model for Chaoyang were 51.5090%, 28.6593% and 28.5927% respectively. The R²values of three models were 0.7863, 0.8291 and 0.7753 respectively. The forecasting efficacy of combination model for Liaoning province was better than other two single models. For the forecasting efficacy of Shenyang, the GRNN combination model and the GM(1,1) model were similar, and the ARIMA model was the worst. The incidence of HFRS for Chaoyang is not fit for the establishment of the models we mentioned above. Conclusion GRNN combination model had more advantage in the forecast of small sample and the forecasting efficacy was better than GM(1,1) model and ARIMA model, which had practical value in the treatment of time series data such as the incidence of HFRS.

Objective To study the superiority and application prospect of generalized regression neural network(GRNN) which is used in forecasting the incidence of hemorrhagic fever with renal syndrome(HFRS). Methods Use meteorological data, including average temperature, relative humidity, precipitation and sunshine time, and epidemiologic information of animal diseases, including rodent density and viral carriage of rodents from 1984 to 2002 as the input of neural network. Use the incidence of HFRS from 1985 to 2003 as the output of neural network. Construct the GRNN forecasting model and BP neural network forecasting model respectively with the neural network toolbox of Matlab7.0. Fit and forecast the sample and compare the performance between the two different neural networks. Results The optimize smooth factor of GRNN is 0.35; the hidden layers of BP neural network is 6. From the fitting effect, the MER of GRNN and BP neural network are 25.42% and 25.55% respectively; their r² are 0.9438 and 0.9729. On the whole, the fitting effect is satisfactory, and the difference of the two neural networks is not very significant. From the forecasting effect, the MER between the two neural networks are 4.90% and 15.16% respectively. The MER of GRNN is less than the MER of BP neural network; their r² are 0.9897 and 0.9516. Conclusion GRNN is more superior in small sample forecasting than BP neural network, and the forecasting effect is better. GRNN has practical value in solving epidemic problem which has complicated influencing factor such as HFRS.

Objective To study the application of back propagation (BP) artificial neural network model in prediction for incidence of hemorrhagic fever with renal syndrome(HFRS).Methods Meteorological data,including average temperature,relative humidity,precipitation and sunshine time,obtained from Shenyang Municipal Meteorological Bureau,and epidemiologic information of animal diseases,including rat density and viral carriage of rats obtained from Shenyang Municipal Center for Disease Control and Prevention,were collected as input of artificial neural networks.And,incidence data of HFRS in Shenyang during 1984 to 2003 were collected as output of artificial neural networks.A predictive model of BP artificial neural networks was established using the data during 1984 to 2001 with STATISTICA Neural Network(ST NN) software.The

Objective To analyze the case feature and epidemic tendency of Epidemic Encephalitis B(EEB) in Dalian during 1951-2002 for understanding the epidemic regularity and formulating a countermeasure to control it.Methods Epidemiology and statistics.Results There had been 7 902 patients with EEB and 5 prevalent peaks in whole city during 1951-2002.The annual average incidence rate was 3.49/10⁵.The cases had characteristic of disperse and focus.There were the major areas of patients in Zhuanghe,Pulandian and Wafangdian,according for 68.43% of total cases.There were greatest numbers of cases in August,September,according for 94.01% of total cases.The chief vehicle of disseminate of EEB was Culex tritaeniorhynchus.Culex pipiens pallens was dominant mosquito in Dalian recently years.Conclusion By inoculating the inactived vaccine and clearing the heavy infestation of mosquitoes,the spread of disease can be controlled effectively.

Objective To investigate the rodent infestation in rice field in Daqing and to observe the rodent control effect by rodenticide.Methods To evaluate the control effect by the capture rates with night trap method.Results There were five species of hamful rodent in the paddy of Daqing,Cricetulus triton,Apodemus agrarius,Cricetulus barabensis,Rattus norvegicus and Mus musculus,among them Cricetulus triton and Apodemus agrarius were dominant species 52% and 24%;the peak period ranged from the rice returning the green in spring to the rice mature in autumn;brodifacoum and bromadiolone were used to control rats of paddy field in spring and autumn,with the control effect respectively for 87.4%,78.1% and 83.9%,74.4%;Seedling damaged by rats was obviously lower.There had no secondary poisonation phenomenon.Conclusion The rodent control measures were suitable and worth to propaganda our city paddy field.