2025, Vol. 6, Issue 2, Part B
Bayesian spatial modeling of COVID-19 risk across Iraqi governorates using the BYM model and INLA framework
Author(s): Ameer Musa Imran Alhseeni and Ali Abdulmohsin Abdulraeem Al rubaye
Abstract: This study investigates the spatial distribution of COVID-19 risk across Iraq using a Bayesian hierarchical framework. The Besag-York-Mollié (BYM) model is employed to estimate relative risks at the governorate level while accounting for both structured and unstructured spatial effects. A simulation study based on realistic spatial settings demonstrates the accuracy and robustness of the model in recovering latent risk surfaces. Real data analysis is conducted using total COVID-19 case counts from the World Health Organization for the period 2020-2021. The results reveal distinct geographic clusters of elevated risk, particularly in urban and densely populated regions. Covariates such as population density and urbanization rate are incorporated to enhance model interpretability. The findings support the use of Bayesian spatial models for epidemiological surveillance and public health planning in low-resource settings.
DOI: https://doi.org/10.22271/math.2025.v6.i2b.224
Pages: 191-197 | Views: 229 | Downloads: 113
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How to cite this article:
Ameer Musa Imran Alhseeni and Ali Abdulmohsin Abdulraeem Al rubaye. Bayesian spatial modeling of COVID-19 risk across Iraqi governorates using the BYM model and INLA framework. Journal of Mathematical Problems, Equations and Statistics. 2025; 6(2): 191-197. DOI: 10.22271/math.2025.v6.i2b.224
