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Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China

Author

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  • Zongyuan Xia

    (College of Mathematics and System Science, Xinjiang University, Urumqi 830046, China)

  • Bo Tang

    (Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA)

  • Long Qin

    (EClinCloud (Shenzhen) Technology Co., Ltd., Shenzhen 518000, China
    Faculty of Economics and Business Administration, Yibin University, Yibin 644000, China)

  • Huiguo Zhang

    (College of Mathematics and System Science, Xinjiang University, Urumqi 830046, China)

  • Xijian Hu

    (College of Mathematics and System Science, Xinjiang University, Urumqi 830046, China)

Abstract

Geostatistics data in regions always have highly spatial heterogeneous, yet the regional features of the data itself cannot be ignored. In this paper, a novel latent Bayesian spatial model is proposed, which incorporates the spatial dependence of different subjects and the spatial random effects to further analysis the influence of spatial effect. The model is verified to be compatible with the integrated nested Laplace approximation (INLA) framework and is fitted using INLA and stochastic partial differential equation (SPDE). The posterior marginal distribution of parameters is estimated with high precision. Additionally, a practical application of the model is presented using tuberculosis (TB) incidence data in China from 2015 to 2019. The results show that the fitting accuracy of our model is higher than the general Bayesian spatial model using INLA-SPDE.

Suggested Citation

  • Zongyuan Xia & Bo Tang & Long Qin & Huiguo Zhang & Xijian Hu, 2023. "Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China," Mathematics, MDPI, vol. 11(19), pages 1-15, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4193-:d:1255182
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    References listed on IDEAS

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