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Spatio-temporal Distribution and Relative Risk of Dengue Fever in Peninsular Malaysia
Mohamad Afiq Amsyar Hamedin1, Kamarul Imran Musa2, Mohd Rahim Sulong3
1Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia.
Summary
Dengue fever risk in Malaysia shows significant spatial and temporal variations. Bayesian spatio-temporal models identified persistent high-risk districts, aiding targeted public health interventions.
Area of Science:
- Epidemiology
- Public Health
- Spatial Statistics
Background:
- Dengue fever is a significant public health issue in Malaysia, characterized by uneven geographical distribution and changing patterns over time.
- Previous studies have not fully captured the detailed district-level dengue risk dynamics, necessitating advanced analytical approaches.
Purpose of the Study:
- To assess the spatio-temporal distribution of dengue fever risk in Peninsular Malaysia.
- To identify specific districts with persistently high dengue risk and understand temporal trends.
Main Methods:
- Retrospective analysis of district-level monthly dengue case data from 2022 to 2024.
- Application of Bayesian spatio-temporal models, including Besag-York-Mollié 2 for spatial effects and random walks for temporal trends.
- Model comparison using Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC), generating relative risk (RR) and exceedance probability maps.
Main Results:
- Dengue incidence significantly increased post-2022, nearly doubling in 2023 and remaining elevated.
- Pronounced spatial heterogeneity was observed, with high-incidence areas concentrated in urban and peri-urban regions.
- Spatio-temporal models incorporating interactions outperformed additive models; persistent high-risk districts were identified in Selangor, Penang, and Johor.
Conclusions:
- Dengue risk in Peninsular Malaysia exhibits substantial spatial heterogeneity and dynamic temporal changes.
- Bayesian spatio-temporal disease mapping offers precise, localized, and time-specific dengue risk estimates.
- Findings support enhanced targeted surveillance and intervention strategies in dengue-endemic areas.
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