Spatio-temporal Distribution and Relative Risk of Dengue Fever in Peninsular Malaysia

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.