Modeling spatial pattern of dengue in North Central Mexico using survey data and logistic regression
Daniel Sánchez-Hernández1, Carlos Arturo Aguirre-Salado1, Guillermo Sánchez-Díaz1
1Faculty of Engineering, Universidad Autonoma de San Luis Potosí, San Luis Potosí, Mexico.
International Journal of Environmental Health Research
|December 15, 2019
Summary
This study models dengue fever spatial distribution in Mexico, identifying high-risk areas. The findings support targeted prevention programs for this significant public health concern.
Area of Science:
- Epidemiology
- Spatial Analysis
- Public Health
Background:
- Dengue fever is a significant global public health issue, particularly in tropical and subtropical regions.
- Existing dengue control efforts in Mexico's tropical areas have been insufficient.
Purpose of the Study:
- To conduct spatial modeling of confirmed dengue fever cases in Mexico's Huasteca Potosina region from 2010-2014.
- To identify key environmental factors associated with dengue presence and absence.
Main Methods:
- Utilized Multivariable Logistic Regression Modeling (MLRM) to analyze dengue case data.
- Employed spatial modeling techniques to create high-resolution risk maps.
- Evaluated model performance using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve, achieving AUC > 0.95.
Main Results:
- Developed a high-resolution spatial model identifying probable dengue risk patterns.
- The model demonstrated high predictive accuracy (AUC > 0.95).
- Identified significant relationships between environmental factors and dengue occurrence.
Conclusions:
- The spatial modeling approach effectively identifies dengue risk areas.
- Results provide a foundation for targeted dengue prevention and control strategies.
- The methodology is adaptable for studying other environmentally influenced diseases.
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