Sains Malaysiana 54(10)(2025): 2567-2576

http://doi.org/10.17576/jsm-2025-5410-18

 

Development of Melioidosis Mapping in Malaysia using Various Relative
Risk Models

(Pembangunan Pemetaan Melioidosis di Malaysia menggunakan Pelbagai Model Risiko Relatif)

 

NAZRINA BINTI AZIZ^1,*, OOI PEI WEN¹, IJLAL BINTI MOHD DIAH² & WAQAR HAFEEZ³

 

¹School of Quantitative Science, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia
²Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris,
35900 Tanjung Malim, Perak, Malaysia
³Haide College, Ocean University of China, Qingdao, Shandong, China

 

Received: 12 March 2025/Accepted: 11 August 2025

 

Abstract

Melioidosis is a significant infectious disease caused by Burkholderia pseudomallei, which is commonly found in soil and water. The disease is highly endemic in Malaysia, with an estimated 2000 deaths annually, surpassing fatalities from dengue and tuberculosis. Despite its severity, understanding the geographical distribution of melioidosis remains a challenge. In this study, the melioidosis data from 2014 to 2023 in Malaysia were analyzed using Excel and WinBUGS software. Relative risk, a measure comparing the risk in one group to another, was used to map melioidosis risk geographically by using ArcGIS. Four models - Susceptible-Infected-Recovered (SIR), Standardized Morbidity Ratios (SMR), Poisson-Gamma, and Besag-York-Mollie (BYM) - were applied to assess their effectiveness. Mapping highlighted consistently higher relative risk in northern Malaysia, particularly in Perlis and Kedah across multiple models while most other states remained in the very low risk category. Besides, the model performance was compared using the Deviance Information Criterion (DIC) to assess goodness of fit. Findings suggest the Poisson-Gamma model is most suitable and reliable for accurate disease risk mapping to better epidemiological surveillance and targeted public health interventions as it accounts for local variations while maintaining computational efficiency in Malaysia.

 

Keywords: Disease mapping; epidemiology; melioidosis; relative risk estimation; statistical models

 

Abstrak

Melioidosis ialah penyakit berjangkit yang serius yang disebabkan oleh Burkholderia pseudomallei, yang sering ditemui dalam tanah dan air. Penyakit ini adalah endemik di Malaysia dengan anggaran 2000 kematian setiap tahun, melebihi jumlah kematian akibat denggi dan tuberkulosis. Walaupun impaknya besar, pemetaan taburan geografi melioidosis masih menjadi cabaran. Dalam kajian ini, data melioidosis dari tahun 2014 hingga 2023 dianalisis menggunakan perisian Excel dan WinBUGS. Risiko relatif, ukuran membandingkan risiko dalam satu kumpulan dengan kumpulan lain digunakan untuk memetakan risiko melioidosis secara geografi dijana menggunakan ArcGIS. Empat model - Susceptible-Infected-Recovered (SIR), Standardized Morbidity Ratios (SMR), Poisson-Gamma, dan Besag-York-Mollie (BYM) - digunakan untuk menilai ketepatannya. Hasil pemetaan menunjukkan risiko relatif yang lebih tinggi dan tekal di utara Malaysia, khususnya di Perlis dan Kedah merentasi pelbagai model manakala kebanyakan negeri lain kekal dalam kategori risiko sangat rendah. Selain itu, prestasi model dibandingkan dengan Deviance Information Criterion (DIC) untuk menilai kesesuaian. Hasil kajian menunjukkan bahawa model Poisson-Gamma memberikan anggaran risiko relatif yang paling sesuai untuk pemetaan risiko melioidosis dalam membantu meningkatkan pemantauan epidemiologi dan strategi intervensi kesihatan awam yang lebih berkesan kerana ia mengambil kira variasi tempatan sambil mengekalkan kecekapan pengiraan.

 

Kata kunci: Anggaran risiko relatif; epidemiologi; melioidosis; model statistik; pemetaan penyakit

 

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*Corresponding author; email: nazrina@uum.edu.my