Using remotely sensed data to explore spatial and temporal relationships between photosynthetic productivity of vegetation and malaria transmission intensities in selected parts of Africa

Abstract: Spatial and temporal variations in malaria transmission are naturally associated with prevailing climatic and environmental factors, for example rainfall, humidity, temperature and human activities. These factors influence malaria transmission mainly in non-deterministic ways, making them less appropriate for accurate geographical mapping of malaria risk. One distinctive phenomenon, ‘photosynthetic productivity of vegetation’, is similarly affected by these factors, yet it can be easily estimated from remotely sensed data using standardized indices. In this study, multiple linear regression techniques are used to explore spatial and temporal associations between photosynthetic productivity of vegetation (measured as Normalized Difference Vegetation Index (NDVI)) and malaria transmission intensities (measured as Entomological Inoculation Rate (EIR)). The study shows significant relationships between NDVI and EIR both at continental level and at a number of the selected study sites. Moreover, in three of four sites where temporal analysis was conducted, a similarity of linear trends is observed between EIRs and means of current and previous month NDVIs. Both NDVI and EIR are significantly associated with altitude as well as to a rural/urban dummy variable. It is concluded that spatial and temporal variations in photosynthetic productivity of vegetation are strongly related to variations in malaria transmission at respective places and periods. Results of this basic exploration imply that vegetation production is a potential indicator of situations favourable for malaria transmission, and can therefore be used to improve mapping of geographical extents of risk of malaria, and perhaps several other vector borne diseases.