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

Author

Listed:
  • Gong, Jing
  • Wu, Yong-Ping
  • Li, Li

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.

Suggested Citation

  • Gong, Jing & Wu, Yong-Ping & Li, Li, 2019. "Parameters estimation in Ebola virus transmission dynamics model based on machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    • Handle: RePEc:eee:phsmap:v:536:y:2019:i:c:s037843711931489x
    DOI: 10.1016/j.physa.2019.122604
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    References listed on IDEAS

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    1. Hadfield, Jarrod D., 2010. "MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i02).
    2. Li, Li, 2017. "Transmission dynamics of Ebola virus disease with human mobility in Sierra Leone," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 575-579.
    3. Li, Li & Zhang, Jie & Liu, Chen & Zhang, Hong-Tao & Wang, Yi & Wang, Zhen, 2019. "Analysis of transmission dynamics for Zika virus on networks," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 566-577.
    4. Zhang, Hai-Feng & Shu, Pan-Pan & Wang, Zhen & Tang, Ming & Small, Michael, 2017. "Preferential imitation can invalidate targeted subsidy policies on seasonal-influenza diseases," Applied Mathematics and Computation, Elsevier, vol. 294(C), pages 332-342.
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