Network-Based Analysis of Beijing SARS Data
Identifieur interne : 003318 ( Main/Exploration ); précédent : 003317; suivant : 003319Network-Based Analysis of Beijing SARS Data
Auteurs : Xiaolong Zheng [République populaire de Chine] ; Daniel Zeng [République populaire de Chine, États-Unis] ; Aaron Sun [États-Unis] ; Yuan Luo [République populaire de Chine] ; Quanyi Wang [République populaire de Chine] ; Feiyue Wang [République populaire de Chine]Source :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: In this paper, we analyze Beijing SARS data using methods developed from the complex network analysis literature. Three kinds of SARS-related networks were constructed and analyzed, including the patient contact network, the weighted location (district) network, and the weighted occupation network. We demonstrate that a network-based data analysis framework can help evaluate various control strategies. For instance, in the case of SARS, a general randomized immunization control strategy may not be effective. Instead, a strategy that focuses on nodes (e.g., patients, locations, or occupations) with high degree and strength may lead to more effective outbreak control and management.
Url:
- https://api.istex.fr/ark:/67375/HCB-RJ9M78BP-2/fulltext.pdf
- http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121587
DOI: 10.1007/978-3-540-89746-0_7
Affiliations:
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Intelligence Science, Institute of Automation, Chinese Academy of Sciences</wicri:regionArea><wicri:noRegion>Chinese Academy of Sciences</wicri:noRegion></affiliation></author><author><name sortKey="Zeng, Daniel" sort="Zeng, Daniel" uniqKey="Zeng D" first="Daniel" last="Zeng">Daniel Zeng</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>The Key Lab of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences</wicri:regionArea><wicri:noRegion>Chinese Academy of Sciences</wicri:noRegion></affiliation><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Management Information Systems, The University of Arizona</wicri:regionArea><wicri:noRegion>The University of Arizona</wicri:noRegion></affiliation></author><author><name sortKey="Sun, Aaron" sort="Sun, Aaron" uniqKey="Sun A" first="Aaron" last="Sun">Aaron Sun</name><affiliation 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Prevention</wicri:regionArea></affiliation></author><author><name sortKey="Wang, Feiyue" sort="Wang, Feiyue" uniqKey="Wang F" first="Feiyue" last="Wang">Feiyue Wang</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>The Key Lab of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences</wicri:regionArea><wicri:noRegion>Chinese Academy of Sciences</wicri:noRegion></affiliation></author></analytic><monogr></monogr><series><title level="s" type="main" xml:lang="en">Lecture Notes in Computer Science</title><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: In this paper, we analyze Beijing SARS data using methods developed from the complex network analysis literature. 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