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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.

Citation Format:
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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  1. Adin, A., Goicoa, T., & Ugarte, M. D. (2019). Online Relative Risks/Rates Estimation in Spatial and Spatio-Temporal Disease Mapping. Computer Methods and Programs in Biomedicine, 172, 103-116. doi: 10.1016/j.cmpb.2019.02.014
  2. Aswi. (2020). Bayesian Spatio-Temporal Modelling of Small Areas: Dengue Fever in Makassar Indonesia. Queensland University of Technology
  3. Aswi, A., Cramb, S., Duncan, E., & Mengersen, K. (2021). Detecting Spatial Autocorrelation for a Small Number of Areas: a Practical Example. Journal of Physics. Conference series, 1899(1), 12098. doi: 10.1088/1742-6596/1899/1/012098
  4. Bachl, F. E., Lindgren, F., Borchers, D. L., Illian, J. B., & Freckleton, R. (2019). inlabru: an R package for Bayesian Spatial Modelling from Ecological Survey Data. Methods in Ecology and Evolution, 10(6), 760-766. doi: 10.1111/2041-210X.13168
  5. Badan Pusat Statistik. (2021). Sulawesi Selatan dalam Angka 2021
  6. Badan Pusat Statistik. (2022). Sulawesi Selatan dalam Angka 2022
  7. Carrijo, T. B., & Da Silva, A. R. (2017). Modified Moran's I for Small Samples. Geographical Analysis, 49(4), 451-467. doi: 10.1111/gean.12130
  8. Chang, W., Cheng, J., Allaire, J. J., Sievert, C., Schloerke , B., Xie, Y., McPherson, J, Dipert, A., Borges, G. (2022). shiny: Web Application Framework for R. R Package Version 1.7.2. Retrieved from https://cran.r-project.org/web/packages/shiny/index.html
  9. Cressie, N. A. C. (1993). Statistics for Spatial Data (Rev. ed. ed.). New York: Wiley
  10. Duncan, L. (2013). CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors. Journal of Statistical Software, 55(1), 1-24. doi: 10.18637/jss.v055.i13
  11. Finn, L., & Håvard, R. (2015). Bayesian Spatial Modelling with R-INLA. Journal of Statistical Software, 63(1), 1-25. doi: 10.18637/jss.v063.i19
  12. Lee, D. (2013). CARBayes: An R Package for Spatial Areal Unit Modelling with Conditional Autoregressive Priors. Journal of Statistical Software, 55(1), 1-24
  13. Leroux, B. G., Lei, X., & Breslow, N. (2000). Estimation of Disease Rates in Small Areas: A New Mixed Model for Spatial Dependence. Statistical Models in Epidemiology, the Environment, and Clinical Trials, 116, 179-191
  14. MacNab, Y. C. (2022). Bayesian Disease Mapping: Past, Present, and Future. Spatial Statistics, 50, 100593-100593. doi: 10.1016/j.spasta.2022.100593
  15. Moraga, P. (2017). SpatialEpiApp : A Shiny Web Application for the Analysis of Spatial and Spatio-Temporal Disease Data. Spat Spatiotemporal Epidemiology, Nov:23:47-57. doi: 10.1016/j.sste.2017.08.001
  16. Moraga, P. (2020). Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Boca Raton: CRC Press
  17. R Core Team. (2019). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org
  18. Rao, A. S. R. S., Payne, S., & Rao, C. R. (2017). Disease Modelling and Public Health. Part A. Amsterdam, Netherlands: North Holland is an imprint of Elsevier
  19. Shalabh. (2021). Interactive Web‐based Data Visualization with R, Plotly, and shinyCarsonSievert; Chapman & Hall/CRC; 2020; ISBN 978‐1‐138‐33145‐7. Journal of the Royal Statistical Society. Series A, Statistics in society, 184(3), 1150-1150. doi: 10.1111/rssa.12692
  20. Sievert, C. (2020). Interactive Web-based Data Visualization with R, Plotly, and Shiny. Boca Raton, Florida ;: CRC Press
  21. Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian Measures of Model Complexity and Fit. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 64(4), 583-639. doi: 10.1111/1467-9868.00353
  22. Wan, Z., & Hudak, P. (2000). Functional Reactive Programming from First Principles. Paper presented at the Conference on Programming Language Design and Implementation
  23. Watanabe, S. (2010). Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory. Journal of Machine Learning Research, 11, 3571-3594

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