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All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA)
Lalu Ramzy Rahmanda
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Medicine & Pharmacology

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Bayesian Spatio-Temporal Conditional Autoregressive Modelling of Factors Affecting Pneumonia Cases in Indonesia Risma Mastory; Aswi, Aswi; Muhammad Fahmuddin; Lalu Ramzy Rahmanda
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 2 (2025): VOLUME 13 NO 2, 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i2.56315

Abstract

The Bayesian Spatio-Temporal Conditional Autoregressive (BST CAR) method is a statistical approach used to analyze data with both spatial and temporal components. While the BST CAR model has been widely applied in various studies, no research has yet explored using the Localized BST CAR model for pneumonia cases in Indonesia. This study aims to identify and model the factors influencing pneumonia incidence in Indonesia using the Localized BST CAR framework. The data analyzed in this study consist of the number of pneumonia cases in Indonesia from 2018 to 2022, along with variables believed to affect the incidence. The findings indicate that the Localized BST CAR model with G=3 provides the best fit for modeling the relative risk of pneumonia cases in Indonesia. Key factors found to significantly influence pneumonia cases include the percentage of exclusively breastfed infants, the percentage of infants with complete basic immunization, and the percentage of the population living in poverty. Notably, the percentage of exclusively breastfed infants and the percentage of fully immunized infants were positively associated with pneumonia cases, while the percentage of the poor population had a negative effect
Co-Authors Aswi, Aswi Muhammad Fahmuddin Risma Mastory