Related Experiment Video
Updated: Jul 4, 2026
Protocol for Dengue Infections in Mosquitoes A. aegypti and Infection Phenotype Determination
Published on: July 4, 2007
Identifying high-risk subdistricts for targeted dengue interventions: a spatio-temporal study in Mueang Chonburi
Monot Rattananen1, Naiyana Sahavechaphan2, Darin Areechokchai3
1National Electronics and Computer Technology Center, National Science and Technology Development Agency, 112 Thailand Science Park, Phahonyothin Road, Pathum Thani, 12120, Thailand.
Background:
In Thailand, the Department of Disease Control (DDC) under the Ministry of Public Health typically implements dengue interventions at the district level, especially in urban districts and those with recurrent outbreaks. However, such district-level assessments often encompass diverse subdistricts with varying levels of risk. Consequently, more granular, subdistrict-level interventions may offer greater precision and effectiveness than uniform, district-wide approaches.
Methods:
This observational ecological study with spatio-temporal components analyzed daily dengue incidence data reported by hospitals nationwide from 2011 to 2020, using consecutive 5-year time frames to account for interannual variability in dengue transmission. It assessed dengue endemic intensity and identified spatio-temporal clusters at the subdistrict level within Mueang Chonburi district. Endemic intensity was measured by calculating the 5-year average incidence rate per one million population. Significant spatial and temporal clusters were detected using the spatial scan statistic method implemented via SaTScan software. Additionally, a key risk factor analysis was conducted based on five evidence-based factors: hidden population proportion, population density, number of temples and schools, number of cemeteries, and waste management performance.
Results:
All 18 subdistricts in Mueang Chonburi district exhibited dengue endemic characteristics across all consecutive 5-year intervals from 2011 to 2020, with varying intensity levels. Nong Mai Daeng, Nong Ri, and Nong Khang Khok were identified as the top three high-endemic subdistricts and consistently appeared as statistically significant spatial clusters (p < 0.05). Temporal clusters for these subdistricts primarily occurred during the rainy season, with extended periods in Nong Mai Daeng. The risk factor analysis revealed that subdistricts with multiple unfavorable conditions were more likely to correspond with high endemic levels and recurrent clusters.
Conclusions:
The findings highlight the importance of timely, subdistrict-level interventions tailored to local outbreak dynamics and risk profiles. Combining spatial-temporal analysis with key environmental and demographic risk factors enables more precise and targeted dengue prevention efforts. Sustained public awareness campaigns and active community engagement remain vital for mitigating outbreaks in both high-risk subdistricts and the wider district.
