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

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 perfor-mance in handling freshwater fish transmission problems, this study advances the future application of trans-missible disease modeling with larger datasets in freshwater fish aquaculture.