Current Statistical Methods for Spatial Epidemiology: A Review

The current advances in technology and disease surveillance systems have often made available the spatial/geographical orientation of disease occurrences. Statistical analysis of such data is often complicated by the spatial structure of the data which manifest itself as spatial autocorrelation. Methods to account for spatial autocorrelation rarely found in the mainstream classical statistics literature. However, current practices in spatial epidemiology seek to unveil and understand the spatial distribution of diseases. Therefore any determination to model spatial autocorrelation is a non-trivial effort which complements the classical statistics approaches. The objective of this review is to discuss the current statistical methods in spatial epidemiology as well as their relative weaknesses. Much attention and focus is provided for methods which are relatively advantageous and widely used.