Related Experiment Video
Updated: Jan 9, 2026

Protocol for Dengue Infections in Mosquitoes A. aegypti and Infection Phenotype Determination
Published on: July 4, 2007
Characterization of Colombian departments based on climatic factors, infrastructure, basic service access, and dengue
Jennyfer Portilla1, José Tovar-Cuevas2, Diego Manotas1
1Escuela de Ingeniería Industrial, Facultad de Ingeniería, Universidad del Valle, Cali, Colombia.
Insights
Dengue incidence in Colombia is linked to climate, socioeconomic status, and access to services. A study identified vulnerable regions with high dengue rates, informing targeted public health interventions.
Area of Science:
- Epidemiology
- Public Health
- Environmental Science
Background:
- Dengue fever is endemic in Colombia, exhibiting significant spatial variations.
- Disease distribution is influenced by climatic, socioeconomic, and basic service access factors.
- Territorial characterization is crucial for understanding dengue patterns and developing effective control strategies.
Purpose of the Study:
- To group Colombian departments based on dengue incidence rates.
- To analyze the relationship between dengue incidence and environmental, socioeconomic, and service access factors.
- To identify distinct territorial patterns of dengue transmission in Colombia.
Main Methods:
- Utilized data from the Instituto Nacional de Salud, Encuesta Nacional de Calidad de Vida, and satellite sources (ERA5, CHIRPS).
- Analyzed variables including access to basic services, housing deficit, temperature, precipitation, and NDVI.
- Employed multiple factor analysis, hierarchical clustering, and self-organizing maps for departmental grouping.
Main Results:
- Identified three distinct groups of departments based on dengue incidence and influencing factors.
- The most vulnerable group (group 3) exhibited a high incidence rate (1,046.87 cases/100,000 inhabitants).
- This vulnerable group was characterized by extreme housing deficits, poor access to basic services, and favorable climatic conditions for vector proliferation.
Conclusions:
- The study revealed key territorial patterns in dengue incidence across Colombia.
- Structural factors significantly influence dengue transmission dynamics.
- Findings support strengthening public policies and designing targeted prevention strategies for vulnerable regions.
Introduction:
Dengue is an endemic disease in Colombia, with spatial variations influenced by climatic, socioeconomic, and basic service access factors. Territorial characterization based on these determinants supports a better understanding of disease distribution and enables the design of more effective control strategies.
Objectives:
To identify groups of departments in Colombia based on the relationship between dengue incidence rates and climatic, socioeconomic, and basic service access factors in 2023.
Materials And Methods:
Data were collected from the Instituto Nacional de Salud of Colombia, the Encuesta Nacional de Calidad de Vida, and satellite sources, such as ERA5 and CHIRPS. Variables related to access to basic services (drinking water, sewage, and waste collection), housing deficit, temperature, precipitation, and the normalized difference vegetation index (NDVI) were analyzed. A multiple factor analysis was applied to reduce dimensionality, followed by hierarchical clustering and self-organizing maps to identify department groupings.
Results:
Three groups of departments with distinct characteristics were identified. The most vulnerable group (group 3) showed an average incidence rate of 1,046.87 cases per 100,000 inhabitants, associated with extreme housing deficits, limited access to basic services, and climatic conditions favorable for vector proliferation.
Conclusions:
The analysis identified key territorial patterns in dengue incidence and highlighted the influence of structural factors on disease transmission. These findings provide a foundation to strengthen public policies and design more targeted prevention and control strategies in the most vulnerable regions.
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Principles of Disease Surveillance
Precipitation and Co-precipitation
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment
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