文献类型：期刊文献

英文题名：Data-driven exploration of 'spatial pattern-time process-driving forces' associations of SARS epidemic in Beijing, China

作者：Wang, Jin-Feng[1];Christakos, George[2];Han, Wei-Guo[3];Meng, Bin[4]

第一作者：Wang, Jin-Feng

通讯作者：Wang, JF[1]

机构：[1]Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;[2]San Diego State Univ, Dept Geog, San Diego, CA 92182 USA;[3]George Mason Univ, Ctr Spatial Informat Sci & Syst, Greenbelt, MD 20770 USA;[4]Beijing Union Univ, Coll Appl Sci & Humanities, Beijing 100083, Peoples R China

第一机构：Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, A11,Datun Rd, Beijing 100101, Peoples R China.

年份：2008

卷号：30

期号：3

起止页码：234-244

外文期刊名：JOURNAL OF PUBLIC HEALTH

收录：;Scopus(收录号：2-s2.0-54249157422);WOS:【SSCI(收录号:WOS:000259375100008)，SCI-EXPANDED(收录号:WOS:000259375100008)】；

基金：The work was funded by the NSFC (40471111, 70571076), the MOST (2006AA12Z215, 2007AA12Z241) and CAS (KZCX2-YW-308).

语种：英文

外文关键词：associations; determinants; epidemic; SARS; spatial pattern; statistics; time evolution

摘要：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.