Geospatial assessment of dengue fever risk and spatial patterns in Phayao Province, Thailand.
TL;DR
It was revealed that socio-economic conditions, housing environments and inconsistent preventive behaviours influenced dengue incidence, with strong community participation linked to more effective prevention, and geospatial evidence is provided to guide targeted vector control, strengthen community-based interventions, and support evidence-based public health strategies in northern Thailand.
Abstract
This study employed geospatial approaches to assess the risk and spatial distribution of Dengue Fever (DF) and Dengue Hemorrhagic Fever (DHF) in Phayao Province, Thailand. Epidemiological data from 2016 to 2024, comprising 3,600 reported cases, were analysed alongside demographic and climatic variables. Temporal analysis revealed a major epidemic in 2023 lasting 20 weeks, coinciding with peak rainfall, with adolescents and young adults (13-24 years old) being the most affected group. Spatial autocorrelation (Moran's I) indicated significant clustering of dengue morbidity rates, with hotspots concentrated in urbanised districts such as Mueang Phayao, Dok Khamtai and Chiang Kham, while Kernel Density Estimation (KDE) highlighted shifts of hotspots toward eastern districts in later years. Local Indicators of Spatial Association (LISA) identified 24 high-high clusters in 2019, predominantly in Mae Chai District. Case-control analysis further revealed that socio-economic conditions, housing environments and inconsistent preventive behaviours influenced dengue incidence, with strong community participation linked to more effective prevention. These findings underscore the spatial heterogeneity of dengue transmission and provide geospatial evidence to guide targeted vector control, strengthen community-based interventions, and support evidence-based public health strategies in northern Thailand.