Uncovering dengue hotspots and climatic drivers in an urban district of Malaysia: A five-year geospatial analysis

<h2>Abstract</h2><h3>Problem Considered</h3> Dengue fever remains a major urban public health threat in Malaysia, where rapid urbanization and climatic fluctuations intensify transmission. While national and state-level analyses have been conducted, district-level geospatial assessments in Kuantan remain scarce, limiting locally adaptive control strategies. This study aimed to uncover fine-scale dengue hotspots and climate thresholds to guide targeted interventions. <h3>Methods</h3> A retrospective ecological analysis was conducted on 5,277 laboratory-confirmed dengue cases from 2019 to 2023. Case data were geocoded and analyzed using GIS-based hotspot detection, mean center shift, and standard deviational ellipse (SDE) methods. Meteorological parameters (rainfall, temperature, humidity, and wind speed) were obtained from the Malaysian Meteorological Department. Temporal correlations and regression analyses assessed associations between climatic factors and dengue incidence. <h3>Results</h3> Over 70% of cases were concentrated in Kuala Kuantan 1 and 2, confirming persistent urban hotspots. Dengue peaks in 2019–2020 coincided with rainfall exceeding 350 mm/month and humidity above 92%. Regression analyses indicated a negative association between rainfall and dengue incidence (r = –0.32) due to habitat flushing, while temperature showed a mild positive correlation (r = 0.21) within the optimal transmission range (26–28°C). Mean center shifts reflected spatial evolution of transmission foci, highlighting urban expansion effects. <h3>Conclusion</h3> This district-level geospatial analysis provides novel, localized evidence that dengue persistence in Kuantan is shaped by urban density and climate thresholds. The findings emphasize that integrating meteorological signals into real-time surveillance can improve early outbreak prediction and targeted vector control. Limitations include the absence of human mobility and intervention data, which should be incorporated in future multi-factorial models. These results offer actionable insights for local policymakers to strengthen climate-sensitive dengue control and early warning frameworks.