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Related Experiment Videos

Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data

S I Hay1, R W Snow, D J Rogers

  • 1Trypanosomiasis and Land-use in Africa (TALA) Research Group, Department of Zoology, University of Oxford, UK. simon.hay@zoo.ox.ac.uk

Transactions of the Royal Society of Tropical Medicine and Hygiene
|August 6, 1998
PubMed
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Satellite remote sensing effectively predicts malaria seasonality in Kenya. Normalized Difference Vegetation Index (NDVI) data from satellites accurately forecast malaria case numbers, aiding public health interventions.

Area of Science:

  • Environmental science
  • Public health
  • Remote sensing technology

Background:

  • Malaria seasonality in Kenya presents challenges for public health interventions.
  • Accurate prediction of malaria outbreaks is crucial for resource allocation and disease control.

Purpose of the Study:

  • To predict malaria seasonality in Kenya using satellite remote sensing data.
  • To establish relationships between environmental variables and malaria admissions.
  • To develop a spatial map of malaria transmission periods.

Main Methods:

  • Utilized long-term paediatric severe malaria admissions data.
  • Collected simultaneous data from Advanced Very High Resolution Radiometer (AVHRR) and Meteosat High Resolution Radiometer (HRR) satellites.
  • Processed remotely sensed data for land surface temperature, middle infra-red reflectance, rainfall, and Normalized Difference Vegetation Index (NDVI).

Related Experiment Videos

  • Applied temporal Fourier processing to environmental variables and compared with monthly malaria admission data.
  • Determined NDVI thresholds associated with malaria case presentation.

    • Main Results:

    • The Normalized Difference Vegetation Index (NDVI) from the preceding month showed the strongest correlation with malaria cases (mean adjusted r2 = 0.71).
    • An NDVI threshold between 0.35-0.40 indicated months with over 5% of annual malaria cases.
    • Spatial extrapolation of NDVI data created an 8x8 km resolution map of expected malaria transmission months across Kenya.

    Conclusions:

    • Remote sensing techniques are appropriate for mapping malaria seasonality and informing national intervention strategies.
    • Satellite-derived NDVI is a valuable predictor for malaria risk assessment.
    • The developed maps offer a data-driven approach to malaria control planning in Kenya.