Parameters estimation in Ebola virus transmission dynamics model based on machine learning

This paper presents the application of machine learning to parameter estimation in biomathematical model. The background of Ebola disease was introduced, including the structure and morphology of the virus, the causes of disease, the mode of transmission, prevention and control measures. Meanwhile, it is essential to present the mechanism of this method, the application and calculation process, and the parameters. Compared with other methods, this method can not only obtain more accurate parameter values based on fewer and scattered data, but also estimate the parameters appearing anywhere in the partial differential equation, and automatically filter arbitrary noise data through Gaussian priori hypothesis. (C) 2019 Elsevier B.V. All rights reserved.