Determine the best spatial model appropriate to estimate number of deaths from chronic diseases

The spatial linear regression model is one of the statistical methods used to represent the relationship between two or more spatial phenomena. Paying attention to the effect of space or spatial factors in analyzing phenomena leads to the discovery of important information, rather than relying solely on time. Therefore, it is necessary to develop mathematical models that allow the inclusion of spatial factors, represented by spatial regression, which explains the influence of explanatory variables on response variables in the presence of spatial effects from neighboring locations. Three spatial regression models were used: the Spatial Durbin Model, the Spatial Durbin Error Model, and the Spatial Autoregressive Model. The Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) were used as criteria to determine the most appropriate model for the available data, which were collected in the context of studying the impact of explanatory variables (diabetes and malignant tumors) on the response variable, the number of deaths in Iraq in the year 2021. The results showed that the Spatial Durbin Model was the best-fitting model for this research.