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Modeling Spatio-temporal Malaria Risk Using Remote Sensing and Environmental Factors.

Muhammad Haris Mazher1, Javed Iqbal1, Muhammad Ahsan Mahboob1

  • 1Institute of Geographic Information Systems, School of Civil and Environmental Engineering, National University of Sciences and Technology, Islamabad, Pakistan.

Iranian Journal of Public Health
|October 16, 2018
PubMed
Summary

Remote sensing effectively models malaria risk, identifying high-risk zones and seasonal variations. This spatial epidemiology approach aids in prioritizing malaria control efforts.

Keywords:
Climatic/environmental variablesMalariaMalaria risk modelingPakistanRemote sensing

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Area of Science:

  • Environmental Science
  • Epidemiology
  • Geographic Information Systems

Background:

  • Remote sensing applications are expanding, yet underutilized in spatial epidemiology.
  • Understanding environmental determinants is crucial for disease surveillance.

Purpose of the Study:

  • To model malaria risk and its spatiotemporal seasonal variation using remote sensing data.
  • To create a categorized malaria risk map for Rawalpindi region.

Main Methods:

  • Utilized two years (2009-2010) of Landsat TM satellite data.
  • Developed criterion maps for vegetation, water bodies, air temperature, and humidity.
  • Employed weighted overlay analysis for final malaria risk categorization.

Main Results:

  • Significant portions of Rawalpindi were categorized as high-risk zones across different months (e.g., 25% in Jun 2009, 68% in Oct 2009).
  • Malaria risk peaked during the monsoon season.
  • Air temperature and relative humidity were identified as key factors influencing seasonal malaria risk variations.

Conclusions:

  • Malaria risk maps generated through remote sensing are valuable tools.
  • These maps can guide the prioritization of areas for targeted malaria control interventions.