Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2026
Spatio-temporal Distribution and Relative Risk of Dengue Fever in Peninsular Malaysia.
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Hamedin Mohamad Afiq Amsyar, Musa Kamarul Imran, Sulong Mohd Rahim
Аннотация
OBJECTIVES: Dengue fever remains a major public health concern in Malaysia, with marked spatial heterogeneity and evolving temporal dynamics. Although the incidence of dengue has increased substantially in recent years, district-level risk patterns and their changes over time remain insufficiently characterized. This study aimed to assess the spatio-temporal distribution of dengue risk in Peninsular Malaysia.
METHODS: A retrospective analysis of dengue surveillance data was conducted using district-level monthly case counts from 2022 to 2024. Dengue risk was modeled with Bayesian spatio-temporal models incorporating spatial effects, temporal effects, and space-time interaction terms. Spatial variation was modeled using the Besag-York-Mollié 2 specification, while temporal trends were modeled using a first-order random walk. Competing models were compared using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC). Relative risk (RR) and exceedance probability maps were generated from the best-fitting model.
RESULTS: Dengue incidence increased markedly after 2022, with incidence rates nearly doubling in 2023 and remaining high in 2024. Incidence maps showed pronounced spatial heterogeneity, with persistent high-incidence districts concentrated in urban and peri-urban regions. Spatio-temporal models incorporating space-time interaction substantially outperformed the additive model, with lower DIC and WAIC values. The best-fitting model identified the highest RRs in districts within Selangor, while several districts in Penang, and Johor had high posterior probabilities of elevated risk [P(RR>1)>0.8].
CONCLUSIONS: This study demonstrates substantial spatial heterogeneity and evolving temporal dynamics in dengue risk across Peninsular Malaysia. Bayesian spatio-temporal disease mapping provides robust, localized, time-specific estimates of dengue risk and may support targeted surveillance and intervention in endemic settings.
METHODS: A retrospective analysis of dengue surveillance data was conducted using district-level monthly case counts from 2022 to 2024. Dengue risk was modeled with Bayesian spatio-temporal models incorporating spatial effects, temporal effects, and space-time interaction terms. Spatial variation was modeled using the Besag-York-Mollié 2 specification, while temporal trends were modeled using a first-order random walk. Competing models were compared using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC). Relative risk (RR) and exceedance probability maps were generated from the best-fitting model.
RESULTS: Dengue incidence increased markedly after 2022, with incidence rates nearly doubling in 2023 and remaining high in 2024. Incidence maps showed pronounced spatial heterogeneity, with persistent high-incidence districts concentrated in urban and peri-urban regions. Spatio-temporal models incorporating space-time interaction substantially outperformed the additive model, with lower DIC and WAIC values. The best-fitting model identified the highest RRs in districts within Selangor, while several districts in Penang, and Johor had high posterior probabilities of elevated risk [P(RR>1)>0.8].
CONCLUSIONS: This study demonstrates substantial spatial heterogeneity and evolving temporal dynamics in dengue risk across Peninsular Malaysia. Bayesian spatio-temporal disease mapping provides robust, localized, time-specific estimates of dengue risk and may support targeted surveillance and intervention in endemic settings.