Spatial interpolation and image-integrative geostatistical prediction of mosquito vectors for arboviral surveillance

Abstract

Remote sensing can augment traditional methods of mosquito species surveillance for arboviruses. Abundance and patterns of mosquito vectors of West Nile virus in Chesapeake, Virginia, USA, were studied using light trap collection data and a Landsat-7 Enhanced Thematic Mapper+ digital image for spatial interpolation and geostatistical mapping of the abundance of 24 species of mosquitoes capable of transmitting West Nile virus to humans. We evaluated spatial interpolation techniques including inverse distance weighting, ordinary kriging, co-kriging geostatistics using combined Landsat-7 tasselled cap transform indices (brightness, greenness, and wetness) to characterize habitats and breeding conditions. Results highlight gaps in surveillance coverage, geostatistical improvement of vector patterns and abundance, and spatial patterns of error. Constraints and opportunities for adoption of remote sensing and spatial analysis for mosquito control are identified and discussed.