Analyzing the patterns of space-time distances for tracking the diffusion of an epidemic

Abstract

Understanding the dynamics of how infectious diseases spread in time and space is the primary concern of epidemic control and prevention. Most spatial epidemiological studies use innovative spatial statistical methods to identify spatial or spatial-temporal clusters in an epidemic and their associations with environmental risk factors (Carpenter 2001; Cowled et al. 2009; Wen et al. 2006). Epidemic risk areas could be identified as abnormal clusters in which the observed number of cases exceeds the expected number of cases in a given time and location. A geographic information system (GIS) is a computational tool used to visualize and analyze spatial-temporal clustering of diseases; however, complex human behaviors, such as contacts and movement, are difficult to incorporate into clustering analyses.