Network-based analysis of Beijing SARS data

Xiaolong Zheng, Daniel Zeng, Aaron Sun, Yuan Luo, Quanyi Wang, Feiyue Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationBiosurveillance and Biosecurity - International Workshop, BioSecure 2008, Proceedings
Pages64-73
Number of pages10
DOIs
StatePublished - Dec 1 2008
EventInternational Workshop on Biosurveillance and Biosecurity, BioSecure 2008 - Raleigh, NC, United States
Duration: Dec 2 2008Dec 2 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5354 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshop on Biosurveillance and Biosecurity, BioSecure 2008
CountryUnited States
CityRaleigh, NC
Period12/2/0812/2/08

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Keywords

  • Complex network analysis
  • SARS
  • Weighted networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zheng, X., Zeng, D., Sun, A., Luo, Y., Wang, Q., & Wang, F. (2008). Network-based analysis of Beijing SARS data. In Biosurveillance and Biosecurity - International Workshop, BioSecure 2008, Proceedings (pp. 64-73). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5354 LNBI). https://doi.org/10.1007/978-3-540-89746-0_7