Risk Analysis Of Peste Des Petits Ruminants Epidemic In China

Peste des Petits Ruminants（PPR）is an acute,highly infectious and deadly animal disease caused by PPR virus,which caused serious economic losses to global animal husbandry.At present,the epidemic risk analysis is difficult to meet the needs of decision-making departments due to the low epidemic surveillance coverage of imported animals and products in China.The existence of a large number of susceptible animals in China,the impact of the epidemic in neighboring countries and frequent trade exchanges increase the risk of infection with PPR virus.In this dissertation,genetic evolution analysis,machine learning algorithm and spatio-temporal characteristic method were used to analyze the spatial distribution characteristics of PPR epidemic in China,Mongolia and India.The transmission dynamics model of PPR-SEIR（Susceptible-Exposed-Infected-Recovery）in the three countries is established.A quantitative risk assessment model for carrying PPR virus in Mongolian lamb export and Indian live animal（goat and sheep）export trade was established.The main research contents and conclusions of this paper are as follows:1 Evolutionary analysis of global PPR virus and establishment of PPR epidemic outbreak prediction modelThe genetic sequences of 69 strains of PPR virus were analyzed,and the results showed that there were differences among strains isolated from different years,with nucleotide homology ranging from 86.9 to 99.9%.PPR virus evolved slowly on a global scale.Then,the phylogenetic tree was constructed by Neighbor-Joining method,and the results showed that the Mongolian,Indian and Bangladeshi strains are closely related to the Chinese strains,and were in the same lineage（Ⅳ）.The machine learning algorithm is applied to build PPR prediction model with temperature,precipitation and climatic factors as characteristic variables.The results show that the prediction performance of the PPR epidemic outbreak prediction model constructed by the Random Forest（RF）algorithm is better,and the prediction accuracy of the model on both the training set and the test set is higher than 99.00%.The results of single factor modeling showed that the precipitation in the warmest season had the greatest influence on the model（AUC=0.96）,indicating that the occurrence of PPR epidemic may be related to precipitation to a certain extent.Based on the above prediction model,we built a global PPR outbreak online prediction system.2 Analysis of spatio-temporal characteristics,transmission dynamics and potential distribution of PPR epidemic in ChinaThe spatial and temporal characteristics analysis method was used to analyze the PPR epidemic in China from 2007 to 2018.The results of heat map analysis show that the hot spots of PPR epidemic in China are concentrated in the southeast region.The results of global autocorrelation analysis showed that there was a positive spatial correlation between the epidemics of PPR in China in 2014,2015 and 2018,showing a cluster distribution pattern,while the other years showed a random distribution pattern.The analysis results of standard deviation ellipse and linear direction mean to indicate that the overall transmission direction of PPR in China from 2007 to 2018 was from west to east.The simulation results of the PPR-SEIR transmission dynamics model in China showed that the number of infections reached its peak in about 25 days under the natural infection state.The prediction results of the potential distribution of PPR in China based on the Max Ent model showed that the high potential distribution area of PPR in China was in the southeast,accounting for about 12.35%of the total area of the country.The major climatic factors contributing to the model are Precipitation in November,Altitude,Precipitation in the driest month,and Precipitation in May.These variables may played an important role affecting the occurrence of PPR epidemic.3 Analysis of spatio-temporal characteristics,transmission dynamics and establishment of quantitative risk assessment model of PPR epidemic in Mongolia and IndiaOur analysis of PPR outbreak data in Mongolia shows that the PPR epidemic in Mongolia is concentrated in the western region and spreads to wild animals.The results of global spatial autocorrelation analysis showed that there was a positive spatial correlation of PPR epidemic in Mongolia from 2016 to 2017,presenting an aggregation distribution pattern.The transmission dynamics model of PPR-SEIR in Mongolia showed that the transmission dynamics of PPR in Mongolia was slow in the early stage and reached the peak of infection in about 400 days.On this basis,we further established a quantitative risk analysis model of PPR epidemic in Mongolia.The results showed that the average probability of carrying PPR virus in Mongolia’s exported mutton was 2.45×10^-6 each year,and the average probability of carrying PPR virus in Mongolia’s imported mutton was 8.1×10^-7,indicating a low risk.According to the analysis of the outbreak data of PPR in India,the epidemic of PPR in India is relatively serious.The simulation results of the PPR-SEIR transmission dynamics model show that the number of infections in India reached a peak in about 15days,and the epidemic is in a state of rapid transmission.The results of global autocorrelation analysis showed that there was a positive spatial correlation between the number of PPR outbreaks and the number of deaths in India,showing a clustered distribution pattern.The results of spatio-temporal scanning statistical analysis showed that there were clusters in West Bengal and Assam,and the potential risk of PPR outbreak in these regions was high.The quantitative risk export model of PPR virus from India through live animal（sheep and goats）trade shows that the average probability of carrying PPR virus in live sheep exported from India is 1.45×10^-4.4 Construction of risk early warning platform for peste des petits ruminantsBased on the above research results,we design and build the PPR epidemic risk early warning platform（http://1.15.226.101:8080/PPRYJ/）.The early warning platform includes two systems:the outbreak prediction system and the quantitative risk analysis system of petit ruminant.