Estimating SVCV waterborne transmission and predicting experimental epidemic development:A modeling study using a machine learning approach  

作　　者：Jiaji Pan Qijin Zeng Wei Qin Jixiang Chu Haibo Jiang Haiyan Chang Jun Xiao Hao Feng 

机构地区：[1]State Key Laboratory of Developmental Biology of Freshwater Fish,College of Life Science,Hunan Normal University,Changsha,410081,China [2]College of Engineering and Design,Hunan Normal University,Changsha,410081,China

出　　处：《Water Biology and Security》2024年第1期60-70,共11页水生生物与安全（英文）

基　　金：the National Natural Science Foundation of China(U21A20268,31920103016,32173010);the fellowship of China Postdoctoral Science Foundation(No.2022M711128);Hunan Provincial Science and Technology Department(2021RC2076,2021NK2025,2022JJ40276,2022JJ30383);College Student Innovation and Entrepreneurship Training Program(2022123,2023227);the Modern Agricultural Industry Program of Hunan Province,and the Fish Disease and Vaccine Research and Development Platform for Postgraduates in Hunan Province.

摘　　要：Viral infectious diseases significantly threaten the sustainability of freshwater fish aquaculture.The lack of studies on epidemic transmission patterns and mechanisms inhibits the development of containment strategies from the viewpoint of veterinary public health.This study raises an epidemic mathematical model considering water transmission with the aim of analyzing the transmission process more accurately.The basic reproduction number R0 was derived by the model parameter including the water transmission coefficient and was used for the analysis of the virus transmission.Spring viremia of carp virus(SVCV)and zebrafish were used as model viruses and animals,respectively,to conduct the transmission experiment.Transmission through water was achieved by connecting two aquarium tanks with a water channel but blocking the fish movement between the tanks.With the collected experimental data,we determined the optimal hybrid machine learning algorithm to analyze the transmission process using an established mathematical model.In addition,future transmission was predicted and validated using the epidemic model and an optimal algorithm.Finally,the sensitivity of model parameters and the simulations of R0 variation were performed based on the modified complex epidemic model.This study is of significance in providing theoretical guidance for minimizing R0 by manipulating model parameters with containment measures.More importantly,since the modified model and algorithm demonstrated better performance in handling freshwater fish transmission problems,this study advances the future application of transmissible disease modeling with larger datasets in freshwater fish aquaculture.

关 键 词：Epidemic mathematical model Hybrid machine learning algorithm Reproduction number Sensitivity analysis SVCV transmission 

分 类 号：S94[农业科学—水产养殖]