Spatial Clustering and Determinants of Dengue Incidence Among the Young Population of Northern Thailand During the COVID-19 Pandemic
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Issued Date
2026-01-01
Resource Type
ISSN
26729016
eISSN
26512416
Scopus ID
2-s2.0-105029922475
Journal Title
Nakhara Journal of Environmental Design and Planning
Volume
25
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Nakhara Journal of Environmental Design and Planning Vol.25 No.1 (2026)
Suggested Citation
Supotina S., Pooseesod K., Silawan T., Puttanapong N., Saita S. Spatial Clustering and Determinants of Dengue Incidence Among the Young Population of Northern Thailand During the COVID-19 Pandemic. Nakhara Journal of Environmental Design and Planning Vol.25 No.1 (2026). doi:10.54028/NJ202625601 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115191
Title
Spatial Clustering and Determinants of Dengue Incidence Among the Young Population of Northern Thailand During the COVID-19 Pandemic
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Abstract
Dengue infection remains a significant public health concern in Thailand, particularly among young populations. The emergence of COVID-19 introduced additional complexity to disease surveillance and control efforts. This study aimed to determine the spatial clustering and determinants of dengue incidence among individuals under 25 years of age in Northern Thailand during the COVID-19 pandemic. Ecological analysis was conducted across 103 districts in eight northern provinces. District-level dengue incidence rates of individuals under 25 years of age for 2021 were calculated and analyzed using global Moran’s I and local indicators of spatial association (LISA) to detect spatial clustering. Bivariate LISA was employed to explore spatial correlations between dengue incidence and sociodemographic, environmental, and health service factors. Spatial regression models were applied to identify significant predictors while accounting for spatial dependence. There were 18 districts (17.48%) with dengue incidence rates higher than the national target. Global Moran’s I indicated a positive spatial autocorrelation (Moran’s I = 0.087), and LISA identified significant high-high clusters in two remote border districts. Bivariate LISA analysis revealed significant positive spatial associations between dengue incidence and the proportion of the population under 25 years of age, COVID-19 morbidity rate, and minimum, maximum, and average rainfall. In contrast, significant negative spatial associations were observed with the proportion of the urban population, COVID-19 fatality rate, and both minimum and average temperatures. Given the low spatial dependence observed, the ordinary least squares model was considered appropriate and identified the number of schools, the ratio of village health volunteers to households, and average temperature as significant determinants of dengue incidence (R<sup>2</sup> = 0.102). These findings indicated the need for geographically targeted health planning strategies and community design, school-based vector control, and climate-informed surveillance strategies. Integrated and resilient public health systems are essential for managing concurrent health threats.