Both directly and indirectly transmitted infectious diseases in humans are spatial-related. Spatial dimensions include: distances between susceptible humans and the environments shared by people, contaminated materials, and infectious animal species. Therefore, spatial concepts in managing and understanding emerging infectious diseases are crucial. Recently, due to the improvements in computing performance and statistical approaches, there are new possibilities regarding the visualization and analysis of disease spatial data. This review provides commonly used spatial or spatial-temporal approaches in managing infectious diseases. It covers four sections, namely: visualization, overall clustering, hot spot detection, and risk factor identification. The first three sections provide methods and epidemiological applications for both point data (i.e., individual data) and aggregate data (i.e., summaries of individual points). The last section focuses on the spatial regression methods adjusted for neighbour effects or spatial heterogeneity and their implementation. Understanding spatial-temporal variations in the spread of infectious diseases have three positive impacts on the management of diseases. These are: surveillance system improvements, the generation of hypotheses and approvals, and the establishment of prevention and control strategies. Notably, ethics and data quality have to be considered before applying spatial-temporal methods. Developing differential global positioning system methods and optimizing Bayesian estimations are future directions.
Keywords: hot spot; neighbourhood effect; spatial epidemiology; spatial heterogeneity; spatial pattern; spatial regression; spatial-temporal analysis; visualization.