Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China
Identifieur interne : 000519 ( Istex/Curation ); précédent : 000518; suivant : 000520Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China
Auteurs : Jin-Feng Wang [République populaire de Chine] ; George Christakos [États-Unis] ; Wei-Guo Han [États-Unis] ; Bin Meng [République populaire de Chine]Source :
- Journal of Public Health [ 1741-3842 ] ; 2008.
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.
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DOI: 10.1093/pubmed/fdn023
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wicri:level="1"><mods:affiliation>Address correspondence to Jin-Feng Wang, E-mail: wangjf@igsnrr.ac.cn</mods:affiliation><country wicri:rule="url">République populaire de Chine</country><wicri:regionArea>Address correspondence to Jin-Feng Wang</wicri:regionArea></affiliation></author><author><name sortKey="Christakos, George" sort="Christakos, George" uniqKey="Christakos G" first="George" last="Christakos">George Christakos</name><affiliation wicri:level="1"><mods:affiliation>Department of Geography, San Diego State University, San Diego, CA 92182-4493, USA</mods:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Geography, San Diego State University, San Diego, CA 92182-4493</wicri:regionArea></affiliation></author><author><name sortKey="Han, Wei Guo" sort="Han, Wei Guo" uniqKey="Han W" first="Wei-Guo" last="Han">Wei-Guo Han</name><affiliation wicri:level="1"><mods:affiliation>Center for Spatial Information Science and System, George Mason 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92182-4493</wicri:regionArea></affiliation></author><author><name sortKey="Han, Wei Guo" sort="Han, Wei Guo" uniqKey="Han W" first="Wei-Guo" last="Han">Wei-Guo Han</name><affiliation wicri:level="1"><mods:affiliation>Center for Spatial Information Science and System, George Mason University, 6301 Ivy Lane, Greenbelt, MD 20770, USA</mods:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Spatial Information Science and System, George Mason University, 6301 Ivy Lane, Greenbelt, MD 20770</wicri:regionArea></affiliation></author><author><name sortKey="Meng, Bin" sort="Meng, Bin" uniqKey="Meng B" first="Bin" last="Meng">Bin Meng</name><affiliation wicri:level="1"><mods:affiliation>College of Applied Sciences and Humanities, Beijing Union University, Beijing 100083, China</mods:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Applied Sciences and Humanities, Beijing Union University, Beijing 100083</wicri:regionArea></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Journal of Public Health</title><idno type="ISSN">1741-3842</idno><idno type="eISSN">1741-3850</idno><imprint><publisher>Oxford University Press</publisher><date type="published">2008</date><date type="e-published">2008</date><biblScope unit="vol">30</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="234">234</biblScope><biblScope unit="page" to="244">244</biblScope></imprint><idno type="ISSN">1741-3842</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1741-3842</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="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.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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