Explainable features responsible for the high or low spread of SARS-CoV-2: Africa in view.

The low spread of the global pandemic in Africa has raised concerns. Consequently, many commentators have misconstrued concerns suspecting weather, and immunity to be prime reasons. This study investigates the factors associated with the high and low spread of the SARS-CoV-2 (also known as COVID-19) and employs graphical Bayesian models to investigate feature interactions and causality.

Spatial regression and geostatistics discourse with empirical application to precipitation data in Nigeria.

In this study, we propose a robust approach to handling geo-referenced data and discuss its statistical analysis. The linear regression model has been found inappropriate in this type of study. This motivates us to redefine its error structure to incorporate the spatial components inherent in the data into the model.