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Journal of Public Health Advance Access published online on April 26, 2008
Journal of Public Health, doi:10.1093/pubmed/fdn023
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© The Author 2008. Published by Oxford University Press on behalf of the Faculty of Public Health. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oupjournals.org

Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China


Jin-Feng Wang, Professor of Geoinformatics1
George Christakos, Professor of Geography2
Wei-Guo Han, Scientist of Computation3
Bin Meng, Lecturer of Human Geography4

1 Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, A11, Datun Rd, Anwai, Beijing 100101, China
2 Department of Geography, San Diego State University, San Diego, CA 92182-4493, USA
3 Center for Spatial Information Science and System, George Mason University, 6301 Ivy Lane, Greenbelt, MD 20770, USA
4 College of Applied Sciences and Humanities, Beijing Union University, Beijing 100083, China


Address correspondence to Jin-Feng Wang, E-mail: wangjf{at}igsnrr.ac.cn


  Abstract

Background Severe Acute Respiratory Syndrome (SARS) was first reported in November 2002 in China, and spreads to about 30 countries over the next few months. While the characteristics of epidemic transmission are individually assessed, there are also important implicit associations between them.

Methods A novel methodological framework was developed to overcome barriers among separate epidemic statistics and identify distinctive SARS features. Individual statistics were pair-wise linked in terms of their common features, and an integrative epidemic network was formulated.

Results The study of associations between important SARS characteristics considerably enhanced the mainstream epidemic analysis and improved the understanding of the relationships between the observed epidemic determinants. The response of SARS transmission to various epidemic control factors was simulated, target areas were detected, critical time and relevant factors were determined.

Conclusion It was shown that by properly accounting for links between different SARS statistics, a data-based analysis can efficiently reveal systematic associations between epidemic determinants. The analysis can predict the temporal trend of the epidemic given its spatial pattern, to estimate spatial exposure given temporal evolution, and to infer the driving forces of SARS transmission given the spatial exposure distribution.

Keywords: associations, determinants, epidemic, SARS, spatial pattern, statistics, time evolution


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