A comprehensive, beginner-friendly walkthrough demonstrating how to implement Bayesian spatial disease mapping using R-INLA (Integrated Nested Laplace Approximations). This tutorial guides users through the entire epidemiological data pipeline: from importing local administrative shapefiles (using Kenya as a study case) to simulating realistic disease data using Poisson distributions and accounting for background populations. It explicitly breaks down how to construct spatial neighborhood graphs and configure the classic Besag-York-Mollié (BYM) model to split geographic variation into smooth regional patterns and localized noise. Finally, it demonstrates how to extract posterior mean relative risks and visualize public health hot-spots using advanced continuous color styling in ggplot2. Ideal for epidemiologists, data scientists, and students transitioning into spatial statistics.

Habibah Al Rabiah

Niska Pradila 2507016073 Kelas B

Sofi Fakidhotul Mutafida(2507016014)

NAMA:NADIA NIM:2507016010 KELAS A