Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Mathematics, Computation and Statistics (JMATHCOS)
Ananda Shafira
Unknown Affiliation
Mathematics

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Mathematics, Computation and Statistics (JMATHCOS)

 1 
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.
Co-Authors ASEP SAEFUDDIN Kusman Sadik