PREDICTING MALARIA SEASONS IN KENYA USING MULTITEMPORAL METEOROLOGICAL SATELLITE SENSOR DATA

This article describes research that predicts the seasonality of malar ia in Kenya using remotely sensed images from satellite sensors. The p redictions were made using relationships established between long-term data on paediatric severe malaria admissions and simultaneously colle cted data from the Advanced Very High Resolution Radiometer (AVHRR) on the National Oceanic and Atmospheric Administrations (NOAA) polar-orb iting meteorological satellites and the High Resolution Radiometer (HR R) on the European Organization for the Exploitation of Meteorological Satellites' (EUMETSAT) geostationary Meteosat satellites. The remotel y sensed data were processed to provide surrogate information on land surface temperature, reflectance in the middle infra-red, rainfall, an d the normalized difference vegetation index (NDVI). These variables w ere then subjected to temporal Fourier processing and the fitted Fouri er data were compared with the mean percentage of total annual malaria admissions recorded in each month. The NDVI in the preceding month co rrelated most significantly and consistently with malaria presentation s across the 3 sites (mean adjusted r(2) = 0.71, range 0.61-0.79). Reg ression analyses showed that an NDVI threshold of 0.35-0.40 was requir ed for more than 5% of the annual malaria cases to be presented in a g iven month. These thresholds were then extrapolated spatially with the temporal Fourier-processed NDVI data to define the number of months, in which malaria admissions could be expected across Kenya in an avera ge year, at an 8 x 8 km resolution. The resulting maps were compared w ith the only existing map (Butler's) of malaria transmission periods f or Kenya, compiled from expert opinion. Conclusions are drawn on the a ppropriateness of remote sensing techniques for compiling national str ategies for malaria intervention.