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Modeling tools for dengue risk mapping - a systematic review.

Valérie R Louis1, Revati Phalkey, Olaf Horstick

  • 1Institute of Public Health, Heidelberg University Medical School, Heidelberg, Germany. valerie.louis@uni-heidelberg.de.

International Journal of Health Geographics
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Summary
This summary is machine-generated.

Effective dengue surveillance requires understanding critical risk predictors and spatial modeling. Current risk maps are often descriptive, limiting public health applications and early warning systems.

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Area of Science:

  • Epidemiology
  • Geographic Information Systems (GIS) & Remote Sensing
  • Public Health

Background:

  • Rising global dengue epidemics necessitate improved surveillance and control tools.
  • This review systematically examines critical dengue risk predictors and spatial modeling approaches for risk mapping.

Purpose of the Study:

  • To provide a comprehensive overview of predictors and spatial/spatio-temporal modeling techniques for dengue risk mapping.
  • To identify key factors and methodologies that can enhance dengue surveillance and prevention strategies.

Main Methods:

  • Systematic literature search across major scientific databases (PubMed, Web of Science, etc.).
  • Inclusion of studies with dengue cases, analyzing predictor types and modeling approaches for risk mapping.
  • Manual search and data extraction based on predefined criteria.

Main Results:

  • Diverse predictors (demographic, socio-economic, environmental) and modeling approaches were identified.
  • Common predictors include age, gender, education, income, precipitation, and temperature; remote sensing data also utilized.
  • Descriptive maps identified hotspots, while predictive maps showed potential but limited public health applicability.

Conclusions:

  • Most dengue risk maps are descriptive and use retrospective data.
  • Map accuracy and public health utility depend on resources, data quality, and technical expertise.
  • Challenges remain in incorporating entomological, virological, serological, and mobility data for effective early warning systems.