DOI: https://doi.org/10.14710/medstat.15.1.48-59

THE INTERPLAY BETWEEN CLUSTERS, COVARIATES, AND SPATIAL PRIORS IN SPATIAL MODELLING OF COVID-19 IN SOUTH SULAWESI PROVINCE, INDONESIA

*Aswi Aswi orcid scopus publons  -  Statistics Study Program, Universitas Negeri Makassar, Indonesia
Muhammad Arif Tiro  -  Statistics Study Program, Universitas Negeri Makassar, Indonesia
Sudarmin Sudarmin  -  Statistics Study Program, Universitas Negeri Makassar, Indonesia
Sukarna Sukarna  -  Mathematics Department, Universitas Negeri Makassar, Indonesia
Susanna Cramb publons  -  Australian Centre for Health Services Innovation & Centre for Healthcare Transformation, Queensland University of Technology, Australia
Open Access Copyright (c) 2022 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
A number of previous studies on Covid-19 have used Bayesian spatial Conditional Autoregressive (CAR) models. However, basic CAR models are at risk of over-smoothing if adjacent areas genuinely differ in risk. More complex forms, such as localised CAR models, allow for sudden disparities, but have rarely been applied to modelling Covid-19, and never with covariates. This study aims to evaluate the most suitable Bayesian spatial CAR localised models in modelling the number of Covid-19 cases with and without covariates, examine the impact of covariates and spatial priors on the identified clusters and which factors affect the Covid-19 risk in South Sulawesi Province. Data on the number of confirmed cases of Covid-19 (19 March 2020 -25 February 2022) were analyzed using the Bayesian spatial CAR localised model with a different number of clusters and priors. The results show that the Bayesian spatial CAR localised model with population density included fits the data better than a corresponding model without covariates. There was a positive correlation between the Covid-19 risk and population density. The interplay between covariates, spatial priors, and clustering structure influenced the performance of models. Makassar city and Bone have the highest and the lowest relative risk (RR) of Covid-19 respectively.
Fulltext View|Download
Keywords: Bayesian CAR Localised; Clustering; Covid-19; Relative Risk

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
ESTIMATION OF IBNR AND RBNS RESERVES USING RDC METHOD AND GAMMA GENERALIZED LINEAR MODEL ESTIMATION OF SEMIPARAMETRIC REGRESSION CURVE WITH MIXED ESTIMATOR OF MULTIVARIABLE LINEAR TRUNCATED SPLINE AND MULTIVARIABLE KERNEL THIRD MOLAR MATURITY INDEX IN INDONESIAN JUVENILES: COMPARING LINEAR AND POLYNOMIAL KERNEL PERFORMANCE IN SUPPORT VECTOR REGRESSION FOR DENTAL AGE ESTIMATION More recent articles
Most cited articles
ANALISIS PENGARUH LOKASI DAN KARAKTERISTIK KONSUMEN DALAM MEMILIH MINIMARKET DENGAN METODE REGRESI LOGISTIK DAN CART ANALISIS PERBANDINGAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS ANALISIS KLASIFIKASI KABUPATEN DI JAWA TENGAH BERDASARKAN POPULASI TERNAK MENGGUNAKAN FUZZY CLUSTER MEANS APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG PEMETAAN PENYAKIT DEMAM BERDARAH DENGUE DENGAN ANALISIS POLA SPASIAL DI KABUPATEN PEKALONGAN More cited articles
  1. Arbel, Y., Fialkoff, C., Kerner, A., & Kerner, M. (2021). Do Population Density, Socio-Economic Ranking and Gini Index of Cities Influence Infection Rates from Coronavirus? Israel as a Case Study. The Annals of Regional Science, 68(1), 181-206. doi: 10.1007/s00168-021-01073-y
  2. 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
  3. Aswi, A., Cramb, S. M., Moraga, P., & Mengersen, K. (2019). Bayesian Spatial and Spatio-Temporal Approaches to Modelling Dengue Fever: a Systematic Review. Epidemiology and Infection, 147. doi: 10.1017/S0950268818002807
  4. Aswi, A., Mauliyana, A., Tiro, M. A., & Bustan, M. N. (2022). Relative Risk of Coronavirus Disease (Covid-19) In South Sulawesi Province, Indonesia: Bayesian Spatial Modeling. Media Statistika, 14(2), 158-169. doi: 10.14710/medstat.14.2.158-169
  5. Aswi, A., & Sukarna, S. ( 2022). Factors Affecting the Covid-19 Risk in South Sulawesi Province, Indonesia: A Bayesian Spatial Model. Inferensi, 5(1), 51-58
  6. Badan Pusat Statistik. (2015). Sulawesi Selatan dalam Angka 2015
  7. Badan Pusat Statistik. (2021). Sulawesi Selatan dalam Angka 2021
  8. 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
  9. Dadar, M., Fakhri, Y., Bjørklund, G., & Shahali, Y. (2020). The Association Between the Incidence of COVID-19 and the Distance from the Virus Epicenter in Iran. Archives of Virology, 165(11), 2555. doi: 10.1007/s00705-020-04774-5
  10. Gelman, A. (2013). Bayesian Data Analysis, Third Edition (3rd ed.). Hoboken: CRC Press
  11. Lee, D. (2013). CARBayes: an R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors. Journal of Statistical Software, 55(13), 1-24
  12. Lee, D., & Sarran, C. (2015). Controlling for Unmeasured Confounding and Spatial Misalignment in Long‐Term Air Pollution and Health Studies. Environmetrics, 26(7), 477-487
  13. Moosa, I. A., & Khatatbeh, I. N. (2021). The Density Paradox: are Densely‐Populated Regions More Vulnerable to Covid‐19? The International Journal of Health Planning and Management, 36(5), 1575-1588. doi: 10.1002/hpm.3189
  14. Moran, P. A. P. (1950). Notes on Continuous Stochastic Phenomena. Biometrika, 37(1-2), 17. doi: 10.1093/biomet/37.1-2.17
  15. Mugglin, A. S., Cressie, N., & Gemmell, I. (2002). Hierarchical Statistical Modelling of Influenza Epidemic Dynamics in Space and Time. Statistics in Medicine, 21(18), 2703-2721. doi: 10.1002/sim.1217
  16. Ntzoufras, I. (2011). Bayesian Modeling Using WinBUGS (Vol. 698): John Wiley & Sons
  17. Peng, D., Qian, J., Wei, L., Luo, C., Zhang, T., Zhou, L., Yin, F. (2022). COVID-19 Distributes Socially in China: A Bayesian Spatial Analysis. PLoS ONE, 17(4), e0267001-e0267001. doi: 10.1371/journal.pone.0267001
  18. Polo, G., Soler-Tovar, D., Villamil Jimenez, L. C., Benavides-Ortiz, E., & Mera Acosta, C. (2022). Bayesian Spatial Modeling of COVID-19 Case-Fatality Rate Inequalities. Spatial and Spatio-temporal Epidemiology, 41. doi: 10.1016/j.sste.2022.100494
  19. 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
  20. Robert, C. (2007). The Bayesian Choice From Decision-Theoretic Foundations to Computational Implementation (2nd ed. ed.). New York, NY: Springer New York
  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. Sy, K. T. L., White, L. F., & Nichols, B. E. (2021). Population Density and Basic Reproductive Number of COVID-19 Across United States Counties. PLoS ONE, 16(4), e0249271-e0249271. doi: 10.1371/journal.pone.0249271
  23. Tiro, M. A., Aswi, A., & Rais, Z. (2021). 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(1), 12001. doi: 10.1088/1742-6596/2123/1/012001
  24. 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
  25. Whittle, R. S., & Diaz-Artiles, A. (2020). An Ecological Study of Socioeconomic Predictors in Detection of COVID-19 Cases Across Neighborhoods in New York City. BMC medicine, 18(1), 271-271. doi: 10.1186/s12916-020-01731-6
  26. Wong, D. W. S., & Li, Y. (2020). Spreading of COVID-19: Density Matters. PLoS ONE, 15(12), e0242398-e0242398. doi: 10.1371/journal.pone.0242398

Last update:

No citation recorded.

Last update: 2024-11-27 23:48:26

No citation recorded.