Association of Population Density and Distance to the City with the Risks of COVID-19: A Bayesian Spatial Analysis

Tiro, Muhammad Arif and Aswi, Aswi and Rais, Zulkifli Association of Population Density and Distance to the City with the Risks of COVID-19: A Bayesian Spatial Analysis. Journal of Physics: Conference Series, 2123. ISSN 1742-6596

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Abstract

The outbreak of Coronavirus disease-2019 (Covid-19) poses a severe threat around the world. Although several studies of modelling Covid-19 cases have been done, there appears to have been limited research into modelling Covid-19 using Bayesian hierarchical spatial models. This study aims to examine the most suitable Bayesian spatial CAR Leroux models in modelling the number of confirmed Covid-19 cases without and with covariates namely distance to the capital city and population density. Data on the number of confirmed positive cases of Covid-19 (March 20, 2020 - August 30, 2021) in 15 sub-districts in Makassar City, the number of populations, population density, and distance to the city are used. The best model selection is based on several criteria, namely Deviance Information Criteria (DIC), Watanabe Akaike Information Criteria (WAIC), residuals from Moran's I Modification (MMI), and the 95% credible interval does not contain zero. The results showed that the best model in modelling Covid-19 is spatial CAR Leroux with hyperprior Inverse-Gamma (0.5, 0.05) model with the incorporation of distance to the capital city. It is found that there was a negative correlation between the distance to the capital city and Covid-19 risk, but the association between population density and the relative risk of Covid-19 was not statistically significant. Ujung Pandang district and Sangkarrang Island have the highest and the lowest relative risk respectively.

Item Type:	Article
Subjects:	FMIPA > STATISTIKA - (S1) KARYA ILMIAH DOSEN Universitas Negeri Makassar > KARYA ILMIAH DOSEN
Divisions:	KOLEKSI KARYA ILMIAH UPT PERPUSTAKAAN UNM MENURUT FAKULTAS > KARYA ILMIAH DOSEN KARYA ILMIAH DOSEN
Depositing User:	Dr. Aswi Aswi
Date Deposited:	17 Jul 2022 06:12
Last Modified:	28 Nov 2022 00:37
URI:	http://eprints.unm.ac.id/id/eprint/23857

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