DOI: https://doi.org/10.14710/medstat.16.2.148-159

MAKING BAYESIAN DISEASE MAPPING EASY AND INTERACTIVE: AN R SHINY APPLICATION

*Aswi Aswi orcid scopus publons  -  Statistics Study Program, Universitas Negeri Makassar, Indonesia
Muhammad Arif Tiro  -  Statistics Department, Universitas Negeri Makassar, Indonesia
Sudarmin Sudarmin  -  Statistics Department, Universitas Negeri Makassar, Indonesia
Sukarna Sukarna  -  Mathematics Department, Universitas Negeri Makassar, Indonesia
Awi Awi  -  Mathematics Department, Universitas Negeri Makassar, Indonesia
Nurwan Nurwan  -  Statistics Department, Universitas Negeri Makassar, Indonesia
Susanna Cramb  -  Australian Centre for Health Services Innovation & Centre for Healthcare Transformation, Queensland University of Technology, Australia, Australia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

How to cite (APA): Aswi, A., Tiro, M. A., Sudarmin, S., Sukarna, S., Awi, A., Nurwan, N., & Cramb, S. (2024). MAKING BAYESIAN DISEASE MAPPING EASY AND INTERACTIVE: AN R SHINY APPLICATION. MEDIA STATISTIKA, 16(2), 148-159. https://doi.org/10.14710/medstat.16.2.148-159
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Abstract
Spatial analysis of count data is important in epidemiology and other domains to identify spatial patterns. While Bayesian spatial models are a popular approach, they do require detailed knowledge of the process for model fitting, checking, and visualising results. Although a number of R packages are available to simplify running the model, there are still complexities when checking the model. This paper aims to provide a user-friendly and interactive R Shiny web application for the analysis of spatial data using Bayesian spatial Conditional Autoregressive Leroux models. The web application is built with the integration of the R packages shiny and CARBayes. The required data are the number of cases, population, and optionally some covariates for each region. In this case, we used Covid-19 data in 2021 in South Sulawesi province, Indonesia. This application enables fitting a Bayesian spatial CAR Leroux model under several hyperpriors and selecting the most appropriate through comparing several goodness of fit measures. The application also enables checking convergence, plus obtaining and visualising in an interactive map the relative risk of disease for each region.
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Keywords: Bayesian spatial; CAR Leroux; Relative Risk; R Shiny; Disease Mapping.

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