Sensitivity Analysis on Hyperprior Distribution of the Variance Components of Hierarchical Bayesian Spatiotemporal Disease Mapping

I Gede Nyoman Mindra Jaya (), Farah Kristiani, Yudhie Andriyana and Anna Chadidjah
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I Gede Nyoman Mindra Jaya: Department of Statistics, Universitas Padjadjaran, Sumedang 45363, Indonesia
Farah Kristiani: Department of Mathematics, Parahyangan University, Kota Bandung 40141, Indonesia
Yudhie Andriyana: Department of Statistics, Universitas Padjadjaran, Sumedang 45363, Indonesia
Anna Chadidjah: Department of Statistics, Universitas Padjadjaran, Sumedang 45363, Indonesia

Mathematics, 2024, vol. 12, issue 3, 1-16

Abstract: Spatiotemporal disease mapping modeling with count data is gaining increasing prominence. This approach serves as a benchmark in developing early warning systems for diverse disease types. Spatiotemporal modeling, characterized by its inherent complexity, integrates spatial and temporal dependency structures, as well as interactions between space and time. A Bayesian approach employing a hierarchical structure serves as a solution for spatial model inference, addressing the identifiability problem often encountered when utilizing classical approaches like the maximum likelihood method. However, the hierarchical Bayesian approach faces a significant challenge in determining the hyperprior distribution for the variance components of hierarchical Bayesian spatiotemporal models. Commonly used distributions include logGamma for log inverse variance, Half-Cauchy, Penalized Complexity, and Uniform distribution for hyperparameter standard deviation. While the logGamma approach is relatively straightforward with faster computing times, it is highly sensitive to changes in hyperparameter values, specifically scale and shape. This research aims to identify the most optimal hyperprior distribution and its parameters under various conditions of spatial and temporal autocorrelation, as well as observation units, through a Monte Carlo study. Real data on dengue cases in West Java are utilized alongside simulation results. The findings indicate that, across different conditions, the Uniform hyperprior distribution proves to be the optimal choice.

Keywords: spatiotemporal disease mapping; hierarchical Bayesian; hyperprior; variance component (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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