Abstract
IntroductionThe use of remote sensing has found its way into the field of epidemiology within the last decades. With the increased sensor resolution of recent and future satellites new possibilities emerge for high resolution risk modeling and risk mapping.MethodsA SPOT 5 satellite image, taken during the rainy season 2009 was used for calculating indices by combining the image's spectral bands. Besides the widely used Normalized Difference Vegetation Index (NDVI) other indices were tested for significant correlation against field observations. Multiple steps, including the detection of surface water, its breeding appropriateness for Anopheles and modeling of vector imagines abundance, were performed. Data collection on larvae, adult vectors and geographic parameters in the field, was amended by using remote sensing techniques to gather data on altitude (Digital Elevation Model = DEM), precipitation (Tropical Rainfall Measurement Mission = TRMM), land surface temperatures (LST).ResultsThe DEM derived altitude as well as indices calculations combining the satellite's spectral bands (NDTI = Normalized Difference Turbidity Index, NDWI Mac Feeters = Normalized Difference Water Index) turned out to be reliable indicators for surface water in the local geographic setting. While Anopheles larvae abundance in habitats is driven by multiple, interconnected factors - amongst which the NDVI - and precipitation events, the presence of vector imagines was found to be correlated negatively to remotely sensed LST and positively to the cumulated amount of rainfall in the preceding 15 days and to the Normalized Difference Pond Index (NDPI) within the 500 m buffer zone around capture points.ConclusionsRemotely sensed geographical and meteorological factors, including precipitations, temperature, as well as vegetation, humidity and land cover indicators could be used as explanatory variables for surface water presence, larval development and imagines densities. This modeling approach based on remotely sensed information is potentially useful for counter measures that are putting on at the environmental side, namely vector larvae control via larviciding and water body reforming.
Highlights
The use of remote sensing has found its way into the field of epidemiology within the last decades
Reliable information about vector density and malaria transmission risk is essential for understanding variations in disease epidemiology and targeting intervention programs, which are useful tools at the continental and national scales, but are less appropriate in a context of localscale variations in disease patterns that often vary within a few kilometers distance
High local variation in malaria epidemiology is common in the Sahel region of Africa, where malaria is characterized by very focal and seasonal transmission [10,17,18,19]
Summary
The use of remote sensing has found its way into the field of epidemiology within the last decades. During the past two decades, remotely sensed data has been used to describe and predict geographical and temporal patterns in vector-borne disease transmission and disease prevalence [1,2,3]. One of the main goals of this approach could be the detection of breeding habitats or the mapping of vector densities through remote sensing techniques, while some other studies linked climate and environmental parameters directly to malaria prevalence [2,4,5,6]. Reliable information about vector density and malaria transmission risk is essential for understanding variations in disease epidemiology and targeting intervention programs, which are useful tools at the continental and national scales, but are less appropriate in a context of localscale variations in disease patterns that often vary within a few kilometers distance. High local variation in malaria epidemiology is common in the Sahel region of Africa, where malaria is characterized by very focal and seasonal transmission [10,17,18,19]
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