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All Journal Journal of Mathematics, Computation and Statistics (JMATHCOS) Limits: Journal of Mathematics and Its Applications
Ananda Shafira
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Computer Science & IT Mathematics

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Bayesian Spatial BYM CAR Model for Estimating the Relative Risk of Dengue Hemmorhagic Fever in Bandung Ananda Shafira; Asep Saefuddin; Kusman Sadik
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9272

Abstract

Dengue Hemorrhagic Fever (DHF) is an endemic disease whose transmission is influenced by spatial and environmental factors, including population density, altitude, household sanitation, and clean and healthy living behaviors. In 2022, the city of Bandung reported a high incidence of DHF cases, highlighting the need for spatial modeling to capture interdependencies among geographic regions. This study aims to examine the impact of different parameter settings in hyperprior distributions on the Besag-York-Mollie conditional autoregressive (BYM CAR) model, estimate the relative risk (RR) of DHF, and map district-level risk to support the identification of priority areas for targeted prevention. The BYM CAR model was employed within a Bayesian framework, and the posterior distributions were obtained using Markov Chain Monte Carlo (MCMC) with the Gibbs sampling algorithm. Five hyperprior scenarios based on the Inverse-Gamma distribution were compared to evaluate their influence on model performance. The results show that hyperprior selection substantially affects model outcomes, with the best model obtained when the prior for the structured spatial component was specified as Inverse-Gamma(0.1, 0.1), and the unstructured spatial component as Inverse-Gamma(1, 0.01). Gedebage, Arcamanik, and Rancasari districts were identifies as high-risk areas, while Babakan Ciparay and Bandung Kulon exhibited the lowest RR estimates. This spatial risk mapping offers insights for policymakers in formulating more targeted and efficient DHF prevention strategies.
Penerapan Metode Klasifikasi Perangkat Lunak ArcMap pada Pemetaan Penyebaran Penyakit Dengue di Bandung Ananda Shafira; Farah Kristian; Benny Yong
Limits: Journal of Mathematics and Its Applications Vol. 20 No. 1 (2023): Limits: Journal of Mathematics and Its Applications Volume 20 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Bandung is the city with the highest cases of Dengue disease in West Java. The effectiveness of the vaccine of Dengue disease are still not very high and there is no specific medicine for Dengue disease. In this study, we estimate the relative risk of Dengue disease in each sub-district in Bandung. The results of the relative risk estimation can be used as a reference to cure and prevent this disease more effective and efficient because we can focus more on critical area. The relative risks are estimated using two approaches, the frequentist with the Standardized Morbidity Ratio (SMR) model and Bayesian with the Localized model of Bayesian Conditional Autoregressive (CARBayes). The results show that the sub-districts with the highest and lowest relative risk are Cibeunying Kidul and Bandung Kulon, respectively. Furthermore, each sub-districts are depicted based on their relative risk using some classification methods. The classification methods from ArcMap software that will be used are Manual Interval, Defined Interval, Equal Interval, Quantile, Natural Breaks, and Standard Deviation. The classification results with each method show that each method has its own characteristics.
Co-Authors ASEP SAEFUDDIN Benny Yong Farah Kristian Kusman Sadik