Modeling Uncertainty Based on Spatial Models in Spreading Diseases

Abstract

Lately, spatial models have become a powerful, necessary statistical tool to estimate parameters where data are represented by regions of interests using the window method . Estimation processes based on the high dimensionality of the data have become difficult to implement especially in cases where variability in the spatial models is the main task to investigate. Variability between spatial models considering hierarchical levels of scale, most of the time, involves errors leading to uncertainty in spatial regions. Solving the problem with uncertainty via the estimation of errors in spatial models, complex models could be simplified in easiest ones and important decisions for the quality of data could be taken.