Analyzing the Patterns of Space-Time Distances for Tracking the Diffusion of an Epidemic - Space-Time Integration in Geography and GIScience - page 275

Table 15.3 Average degree centrality and network clustering coefficient of each space-time
cluster of the dengue epidemic in Kaohsiung, 2009
Cluster A Cluster B Cluster C Cluster D

Average degree centrality 3.51 25.60 51.93 36.27

Average network clustering coefficient 0.36 0.09 0.10 0.04

Note: The largest number of each network indicator is shown in bold

Table 15.4 Temporal progression of the infected cases with top 10 % of the degree centrality in
each space-time cluster
Period 1 Period 2 Period 3 Period 4 Period 5

Cluster A 37 18 19 6 0

Cluster B 0 1 52 27 0

Cluster C 0 0 69 11 0

Cluster D 0 3 55 22 0

Note: The largest number in each cluster is shown in bold

Table 15.5 Temporal progression of the infected cases with top 10 % of the network clustering
coefficient in each space-time cluster
Period 1 Period 2 Period 3 Period 4 Period 5

Cluster A 39 13 30 26 0

Cluster B 0 9 27 39 5

Cluster C 0 21 36 14 7

Cluster D 0 8 27 36 6

Note: The largest number in each cluster is shown in bold

of infection and areas with high transmission risk. This method also provides
broader insights into modeling spatial-temporal epidemic dynamics.
However, this study had some limitations. Not only spatial-temporal relation-
ships of infected persons, but molecular surveillance with genotyping technology
and known epidemiological links could confirm disease transmission occurred.
Although the methodology proposed in this paper does not provide biological
evidences of tracking disease transmission, it could be the complementary procedure
of improving geographic targeting of interventions, rather than replacing current
epidemiological investigation practices. Social relationships are also other important
clues to tracking disease transmission. The procedure of measuring geospatial
proximity between infected persons would extend social behaviors/distances (such
as population mobility, contact patterns, and so on) in the further study.

Acknowledgements The research was supported by the grants of National Science Council in Tai-
wan (NSC 98-2410-H-002-168-MY2, NSC 101-2119-M-002-020). The authors also acknowledge
the financial support provided by Infectious Diseases Research and Education Center, Department
of Health and National Taiwan University. The funder had no role in study design, data collection
and analysis, or preparation of the manuscript.