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

Jing Gong, Yong-Ping Wu and Li Li

Physica A: Statistical Mechanics and its Applications, 2019, vol. 536, issue C

Abstract: This paper presents the application of machine learning to parameter estimation in bio-mathematical 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.

Keywords: Ebola; Probabilistic machine learning; Multi-output Gaussian process; Kernel function (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:536:y:2019:i:c:s037843711931489x

DOI: 10.1016/j.physa.2019.122604

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