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RELATIVE RISK OF CORONAVIRUS DISEASE (COVID-19) IN SOUTH SULAWESI PROVINCE, INDONESIA: BAYESIAN SPATIAL MODELING

*Aswi Aswi orcid scopus publons  -  Statistics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia
Andi Mauliyana  -  Statistics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia
Muhammad Arif Tiro  -  Statistics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia
Muhammad Nadjib Bustan scopus  -  Statistics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia
Open Access Copyright (c) 2021 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
The Covid-19 has exploded in the world since late 2019. South Sulawesi Province has the highest number of Covid-19 cases outside Java Island in Indonesia. This paper aims to determine the most suitable Bayesian spatial conditional autoregressive (CAR) localised models in modeling the relative risk (RR) of Covid-19 in South Sulawesi Province, Indonesia. Bayesian spatial CAR localised models with different hyperpriors were performed adopting a Poisson distribution for the confirmed Covid-19 counts to examine the grouping of Covid-19 cases. All confirmed cases of Covid-19 (19 March 2020-18 February 2021) for each district were included. Overall, Bayesian CAR localised model with G = 5 with a hyperprior IG (1, 0.1) is the preferred model to estimate the RR based on the two criteria used. Makassar and Toraja Utara have the highest and the lowest RR, respectively. The group formed in the localised model is influenced by the magnitude of the mean and variance in the count data between areas. Using suitable Bayesian spatial CAR localised models enables the identification of high-risk areas of Covid-19 cases. This localised model could be applied in other case studies.
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Keywords: COVID-19; Relative risk; Bayesian CAR localised
Funding: None

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  1. Annas, Suwardi, Isbar Pratama, Muh, Rifandi, Muh, Sanusi, Wahidah, & Side, Syafruddin. (2020). Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia. Chaos, solitons and fractals, 139. doi: 10.1016/j.chaos.2020.110072
  2. Aswi, Aswi, Cramb, Susanna, Duncan, Earl, Hu, Wenbiao, White, Gentry, & Mengersen, Kerrie. (2020). Climate variability and dengue fever in Makassar, Indonesia: Bayesian spatio-temporal modelling. Spatial and Spatio-temporal Epidemiology, 33
  3. Aswi, Aswi, Cramb, Susanna, Duncan, Earl, & Mengersen, Kerrie. (2020). Evaluating the impact of a small number of areas on spatial estimation. International Journal of Health Geographics, 19(1), 39. doi: 10.1186/s12942-020-00233-1
  4. Aswi, Aswi, Cramb, Susanna, Duncan, Earl, & Mengersen, Kerrie. (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
  5. Austin, Peter C, Brunner, Lawrence J, SM, Hux MD, & Janet, E. (2002). Bayeswatch: an overview of Bayesian statistics. Journal of Evaluation in Clinical Practice, 8(2), 277-286
  6. Badan Pusat Statistik. (2020). Sulawesi Selatan dalam Angka 2020. Retrieved from Makassar:
  7. CNN, Indonesia. (2020). Sulsel Jadi Wilayah Kasus Corona Tertinggi di Luar Pulau Jawa. Retrieved from https://www.cnnindonesia.com/nasional/20200407154248-20-491274/sulsel-jadi-wilayah-kasus-corona-tertinggi-di-luar-pulau-jawa
  8. Dinkes, Provinsi Sulawesi Selatan. (2021). Sulsel Tanggap COVID-19. Retrieved from https://covid19.sulselprov.go.id/data
  9. Lee, Duncan. (2013). CARBayes: an R package for Bayesian spatial modeling with conditional autoregressive priors. Journal of Statistical Software, 55(13), 1-24
  10. Lee, Duncan, Rushworth, Alastair, & Sahu, Sujit K. (2014). A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution. Biometrics, 70(2), 419-429. doi: 10.1111/biom.12156
  11. Lee, Duncan, & Sarran, Christophe. (2015). Controlling for unmeasured confounding and spatial misalignment in long‐term air pollution and health studies. Environmetrics, 26(7), 477-487
  12. Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1-2), 17. doi: 10.1093/biomet/37.1-2.17
  13. Oyana, Tonny J, & Margai, Florence. (2015). Spatial analysis: statistics, visualization, and computational methods Boca Raton: CRC Press
  14. 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
  15. Samer, A. Kharroubi. (2020). Modeling the Spread of COVID-19 in Lebanon: A Bayesian Perspective. Frontiers in applied mathematics and statistics, 6. doi: 10.3389/fams.2020.00040
  16. SATGAS, Penanganan COVID-19. (2020). Analisis Data COVID-19 di Indonesia
  17. Shereen, Muhammad Adnan, Khan, Suliman, Kazmi, Abeer, Bashir, Nadia, & Siddique, Rabeea. (2020). COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses. Journal of advanced research, 24, 91-98. doi: 10.1016/j.jare.2020.03.005
  18. WHO. (2021). Weekly Epidemiological and Weekly Operational Updates February 2021. Retrieved from https://www.who.int/publications/m/item/weekly-epidemiological-update---16-february-2021
  19. Yeni, Najmah, & Davies, Sharyn Graham. (2020). Predicitive modeling, empowering women, and COVID-19 in South Sumatra, Indonesia. ASEAN journal of community engagement, 4(1), 104-133. doi: 10.7454/ajce.v4i1.1094
  20. Zuhairoh, Faihatuz, & Rosadi, Dedi. (2020). Real-time Forecasting of the COVID-19 Epidemic using the Richards Model in South Sulawesi, Indonesia. Indonesian Journal of Science & Technology, 5(3), 456-462. doi: https://doi.org/10.17509/ijost.v5i3.26

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