Network-Based Analysis of Beijing SARS Data

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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.

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Most cited references 12

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Epidemic Spreading in Scale-Free Networks

Romualdo Pastor-Satorras, Alessandro Vespignani (2001)

The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and persistence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.

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Network theory and SARS: predicting outbreak diversity

Lauren Ancel Meyers, Babak Pourbohloul, M.E.J. Newman … (2004)

Many infectious diseases spread through populations via the networks formed by physical contacts among individuals. The patterns of these contacts tend to be highly heterogeneous. Traditional “compartmental” modeling in epidemiology, however, assumes that population groups are fully mixed, that is, every individual has an equal chance of spreading the disease to every other. Applications of compartmental models to Severe Acute Respiratory Syndrome (SARS) resulted in estimates of the fundamental quantity called the basic reproductive number R 0 —the number of new cases of SARS resulting from a single initial case—above one, implying that, without public health intervention, most outbreaks should spark large-scale epidemics. Here we compare these predictions to the early epidemiology of SARS. We apply the methods of contact network epidemiology to illustrate that for a single value of R 0 , any two outbreaks, even in the same setting, may have very different epidemiological outcomes. We offer quantitative insight into the heterogeneity of SARS outbreaks worldwide, and illustrate the utility of this approach for assessing public health strategies.

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Superspreading SARS Events, Beijing, 2003

Zhuang Shen, Fang Ning, Weigong Zhou … (2004)

Superspreading events were pivotal in the global spread of severe acute respiratory syndrome (SARS). We investigated superspreading in one transmission chain early in Beijing’s epidemic. Superspreading was defined as transmission of SARS to at least eight contacts. An index patient with onset of SARS 2 months after hospital admission was the source of four generations of transmission to 76 case-patients, including 12 healthcare workers and several hospital visitors. Four (5%) case circumstances met the superspreading definition. Superspreading appeared to be associated with older age (mean 56 vs. 44 years), case fatality (75% vs. 16%, p = 0.02, Fisher exact test), number of close contacts (36 vs. 0.37) and attack rate among close contacts (43% vs. 18.5%, p < 0.025). Delayed recognition of SARS in a hospitalized patient permitted transmission to patients, visitors, and healthcare workers. Older age and number of contacts merit investigation in future studies of superspreading.

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Author and article information

Contributors

Daniel Zeng: zeng@email.arizona.edu

Hsinchun Chen: hchen@eller.arizona.edu

Henry Rolka: hrr2@cdc.gov

Bill Lober: lober@u.washington.edu

Journal

Journal ID (publisher-id): 978-3-540-89746-0

Journal ID (doi): 10.1007/978-3-540-89746-0

Journal ID (nlm-ta): Biosurveillance and Biosecurity

Title: Biosurveillance and Biosecurity

Subtitle: International Workshop, BioSecure 2008, Raleigh, NC, USA, December 2, 2008. Proceedings

ISBN (Print): 978-3-540-89745-3

ISBN (Electronic): 978-3-540-89746-0

Publication date (Print): 2008

Volume: 5354

Pages: 64-73

Affiliations

[1 ]GRID grid.134563.6, ISNI 000000012168186X, MIS Department, , University of Arizona and Chinese Academy of Sciences, ; Tucson, AZ USA

[2 ]GRID grid.134563.6, ISNI 000000012168186X, Department of Management Information Systems, Eller College of Management, , The University of Arizona, ; 85721 AZ USA

[3 ]US CDC, National Center for Public Health Informatics, Atlanta, GA USA

[4 ]GRID grid.34477.33, ISNI 0000000122986657, University of Washington, Health Sciences Building, ; 1959 NE Pacific St., WA 98195 Seattle, USA

[5 ]GRID grid.429126.a, ISNI 000000040644477X, The Key Lab of Complex Systems and Intelligence Science, , Institute of Automation, Chinese Academy of Sciences, ; China

[6 ]GRID grid.134563.6, ISNI 000000012168186X, Department of Management Information Systems, , The University of Arizona, ; USA

[7 ]GRID grid.198530.6, ISNI 0000000088032373, Beijing Center for Disease Control and Prevention, ; China

Article

Publisher ID: 7

DOI: 10.1007/978-3-540-89746-0_7

PMC ID: 7121587

SO-VID: 4410fdda-c86c-45d1-9532-c4a29022dc0c

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This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

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Keywords: sars,complex network analysis,weighted networks

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Keywords: sars, complex network analysis, weighted networks

Network-Based Analysis of Beijing SARS Data

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Coronaviruses, COVID-19 and SARS-CoV-2

Most cited references 12

Epidemic Spreading in Scale-Free Networks

Network theory and SARS: predicting outbreak diversity

Superspreading SARS Events, Beijing, 2003

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