Analyzing the Patterns of Space-Time Distances for Tracking the Diffusion of an Epidemic - Space-Time Integration in Geography and GIScience - page 270

Y

illness onset time

6

7

5

5

4

3

6

4

3

9

1

2

8

2 10

1

X

0

3 4 6

1 2 5 7

( X-coordinate)

Fig. 15.4 The structure of a network constructed from selected space-time pairs

Table 15.2 Degree centrality
and network clustering
coefficients of each infected
case in the hypothetical
epidemic

ID Degree centrality Network clustering coefficient
1 1 -
2 1 -
3 4 0.67
4 4 0.83
5 5 0.60
6 4 0.67
7 3 0.67
8 3 0.67
9 7 0.43
10 2 1
Note: The largest number of each network indicator is
shown in bold

15.4 A Dengue Epidemic in Kaohsiung as a Case Study

This study focused on a dengue epidemic in Kaohsiung as a case study to assess
the feasibility of our proposed methodology. Kaohsiung City, located in southern
Taiwan, is the second largest metropolitan city, the largest commercial harbor and
an important land-, marine- and air-transportation hub in Taiwan. Figure 15.5 shows
the location of Kaohsiung City and one of its satellite cities, Fengshan City. This
two-city area was used as our study area because it was the site of a major dengue
epidemic in southern Taiwan.