Employing Second Order Geospatial Autocorrelation Statistics and an Eigen-Bayesian Semi-Parametric Markovian Non-Gaussian Model for Interpolating Culex quinquefasciatus Storm Sewer Habitats in Bexar and Dallas Counties Texas, U.S.A

Second-order eigenfunction eigen-decomposition eigen-algorithms have been used to determine hyper/hypo-endemic, aggregation/nonaggregation- oriented, aquatic larval habitat foci of multiple nuisance and vector arthropods. Unfortunately, a major West Nile Virus (WNV) mosquito vector, Culex quinquefasciatus, has not been geospatially forecasted for determining geolocations of unknown foci for implementing county level larval control. In this article, we would like to employ geospatial autocorrelation eigen-algorithms to determine georeferenceable hot and cold spots of storm sewer Cx. quinquefasciatus habitats in Dallas County and Bexar County in Texas. We aimed to predictively map unknown storm sewer foci of Cx. quinquefasciatus by employing interpolated capture point sentinel sites. Initially, we employed Google Earth Pro tools to identify sentinel site storm sewers in Dallas and Bexar. We then generated a Red, Green, and Blue (RGB) wavelength reflectance signature using Sentinel-2 visible and near Infrared, 10-meter spatial resolution data. We interpolated previously unknown storm sewer habitats in both entomological intervention counties. Remote validation models revealed sensitivity and specificity approaching 100 %. Currently we are implementing Larval Source Management (LSM) tactics from "Seek and Destroy" (e.g., targeted habitat modification/destruction) for treating these important foci.