Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

Int J Environ Res Public Health · 2016 · PMC4924041 · PMID 27314369

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highly significantno p-value reported

NDVI, DWP and night LST were the highly significant predictors in the high and low malaria transmission areas, and the NDVI and DWP improved the ARIMAX models in all areas significantly.

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