TY - JOUR
T1 - Characterizing diffusion dynamics of disease clustering
T2 - A modified space-time DBSCAN (MST-DBSCAN) algorithm
AU - Kuo, Fei Ying
AU - Wen, Tzai Hung
AU - Sabel, Clive E.
N1 - Funding Information:
This research was supported by grants from the Ministry of Science and Technology (MOST) in Taiwan (MOST 106-2627-M-002-010; MOST 105-2410-H-002-150-MY3). The authors also acknowledge the financial support provided by the Taiwan Centers for Disease Control and Infectious Diseases Research and Education Center, Ministry of Health and Welfare, and National Taiwan University. The funders had no role in study design, data collection, analysis, or preparation of the article.
Publisher Copyright:
© 2018 by American Association of Geographers.
PY - 2018/1/25
Y1 - 2018/1/25
N2 - Epidemic diffusion is a space-time process, and showing time-series disease maps is a common way to demonstrate an epidemic progression in time and space. Previous studies used time-series maps to demonstrate the animation of diffusion process. Epidemic diffusion patterns were determined subjectively by visual inspection, however. There currently are still methodological concerns in developing effective analytical approaches for profiling diffusion dynamics of disease clustering and epidemic propagation. The objective of this study is to develop a geocomputational algorithm, the modified space-time density-based spatial clustering of application with noise (MST-DBSCAN), for detecting, identifying, and visualizing disease cluster evolution, which takes the effect of the incubation period into account. We also map the MST-DBSCAN algorithm output to visualize the diffusion process. Dengue fever case data from 2014 were used as an illustrative case study. Our results show that compared to kernel-smoothed mapping, the MST-DBSCAN algorithm can better identify the evolution type of any cluster at any epoch. Furthermore, using only one two-dimensional map (and graphs), our approach can demonstrate the same diffusion process that time-series maps or three-dimensional space-time kernel plotting displays but in an easy-to-read manner. We conclude that our MST-DBSCAN algorithm can profile the spatial pattern of epidemic diffusion in detail by identifying disease cluster evolution.
AB - Epidemic diffusion is a space-time process, and showing time-series disease maps is a common way to demonstrate an epidemic progression in time and space. Previous studies used time-series maps to demonstrate the animation of diffusion process. Epidemic diffusion patterns were determined subjectively by visual inspection, however. There currently are still methodological concerns in developing effective analytical approaches for profiling diffusion dynamics of disease clustering and epidemic propagation. The objective of this study is to develop a geocomputational algorithm, the modified space-time density-based spatial clustering of application with noise (MST-DBSCAN), for detecting, identifying, and visualizing disease cluster evolution, which takes the effect of the incubation period into account. We also map the MST-DBSCAN algorithm output to visualize the diffusion process. Dengue fever case data from 2014 were used as an illustrative case study. Our results show that compared to kernel-smoothed mapping, the MST-DBSCAN algorithm can better identify the evolution type of any cluster at any epoch. Furthermore, using only one two-dimensional map (and graphs), our approach can demonstrate the same diffusion process that time-series maps or three-dimensional space-time kernel plotting displays but in an easy-to-read manner. We conclude that our MST-DBSCAN algorithm can profile the spatial pattern of epidemic diffusion in detail by identifying disease cluster evolution.
KW - Cluster evolution
KW - DBSCAN
KW - Epidemic diffusion
KW - Geographical visualization
KW - Incubation period
UR - http://www.scopus.com/inward/record.url?scp=85041001788&partnerID=8YFLogxK
U2 - 10.1080/24694452.2017.1407630
DO - 10.1080/24694452.2017.1407630
M3 - Article
AN - SCOPUS:85041001788
SN - 2469-4452
VL - 108
SP - 1168
EP - 1186
JO - Annals of the American Association of Geographers
JF - Annals of the American Association of Geographers
IS - 4
ER -