The goal of the study was to estimate missing public health data in a spatial context by using information from nearby areas and considering the natural patterns in the data. The researchers simulated different scenarios and found that when there are missing values in highly correlated data, certain spatial regression models can accurately estimate those missing values. These models include spatial lag, spatial Durbin, and spatial Durbin error models. In cases of low correlation, the spatial lag X model was also effective in estimating missing values. This method can help public health professionals make better decisions by providing complete and accurate data for interventions.