A CARTOGRAPHIC ANALYSIS USING SPATIAL FILTER LOGISTIC MODEL SPECIFICATIONS FOR IMPLEMENTING MOSQUITO CONTROL IN KENYA

摘要

Negative spatial autocorrelation (NSA), the tendency for dissimilar neighboring values to cluster on a map, may go undetected in statistical analyses of immature Anopheles gambiae s.l., a leading malaria mosquito vector in Sub-Saharan Africa. Unquantified NSA generated from an inverse variance-covariance matrix may generate misspecifications in an An. gambiae s.l. habitat model. In this research, we used an eigenfunction decomposition algorithm based on a modified geographic connectivity matrix to compute the Moran's / statistic, to uncover hidden NSA in a dataset of georeferenced An. gambiae s.l. habitat explanatory predictor variables spa-tiotemporally sampled in Malindi and Kisumu, Kenya. The Moran's / statistic was decomposed into orthogonal synthetic map patterns. Global tests revealed that lzMCls generated were less than 1.11 for the presence of latent autocorrelation. The algorithm captured NSA in the An. gambiae s.l. habitat data by quantifying all non-normal random variables, space-time heterogeneity, and distributional properties of the spatial filters.