Geography Reference
In-Depth Information
Chapter 15
Analyzing the Patterns of Space-Time Distances
for Tracking the Diffusion of an Epidemic
Tzai-Hung Wen and Yu-Shiuan Tsai
15.1
Introduction
Understanding the dynamics of how infectious diseases spread in time and space
is the primary concern of epidemic control and prevention. Most spatial epidemi-
ological 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.
The pattern of interpersonal contacts is one important risk factor in modeling
epidemic dynamics. Contact networks can be formulated such that individuals
are modeled as nodes, with contacts modeled as links. Therefore, social-network
analysis (SNA) is used to model individual contact behaviors with respect to
disease transmission (Firestone et al.
2011
; Jones and Handcock
2003
;Tsaietal.
2011
). Network topological characteristics are then used as epidemiological risk
indices to measure patterns of spread within a group of susceptible populations.
For example, the degree of network clustering could indicate the infection risk of
an individual. A higher network-clustering coefficient indicates a closer connection
to his/her neighbors and, therefore, a higher probability of infection. Degrees of
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