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      Incorporating Geographical Contacts into Social Network Analysis for Contact Tracing in Epidemiology: A Study on Taiwan SARS Data

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          Abstract

          In epidemiology, contact tracing is a process to control the spread of an infectious disease and identify individuals who were previously exposed to patients with the disease. After the emergence of AIDS, Social Network Analysis (SNA) was demonstrated to be a good supplementary tool for contact tracing. Traditionally, social networks for disease investigations are constructed only with personal contacts. However, for diseases which transmit not only through personal contacts, incorporating geographical contacts into SNA has been demonstrated to reveal potential contacts among patients. In this research, we use Taiwan SARS data to investigate the differences in connectivity between personal and geographical contacts in the construction of social networks for these diseases. According to our results, geographical contacts, which increase the average degree of nodes from 0 to 108.62 and decrease the number of components from 961 to 82, provide much higher connectivity than personal contacts. Therefore, including geographical contacts is important to understand the underlying context of the transmission of these diseases. We further explore the differences in network topology between one-mode networks with only patients and multi-mode networks with patients and geographical locations for disease investigation. We find that including geographical locations as nodes in a social network provides a good way to see the role that those locations play in the disease transmission and reveal potential bridges among those geographical locations and households.

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          Network theory and SARS: predicting outbreak diversity

          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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            Is Open Access

            Superspreading SARS Events, Beijing, 2003

            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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              Social networks and infectious disease: The Colorado Springs study

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                Author and article information

                Contributors
                zeng@eller.arizona.edu
                ijg01@health.state.ny.us
                komatsk@azdhs.gov
                colynch@ucdavis.edu
                mcthurmond@usdavis.edu
                dmadigan@rci.rutgers.edu
                lober@u.washington.edu
                jandk30@aol.com
                hchen@eller.arizona.edu
                ydchenb@eller.arizona.edu
                chunjue@eller.arizona.edu
                hchen@eller.arizona.edu
                Journal
                978-3-540-72608-1
                10.1007/978-3-540-72608-1
                Intelligence and Security Informatics: Biosurveillance
                Intelligence and Security Informatics: Biosurveillance
                Second NSF Workshop, BioSurveillance 2007, New Brunswick, NJ, USA, May 22, 2007. Proceedings
                978-3-540-72607-4
                978-3-540-72608-1
                2007
                : 4506
                : 23-36
                Affiliations
                [1 ]GRID grid.134563.6, ISNI 000000012168186X, Department of Management Information Systems, The University of Arizona, Tucson, AZ 85721, ; USA
                [2 ]GRID grid.19188.39, ISNI 0000000405460241, Graduate Institute of Epidemiology, National Taiwan University, Taipei, ; Taiwan
                Article
                3
                10.1007/978-3-540-72608-1_3
                7122638
                53223392-2f7c-4de0-8756-66ca895e6d3c
                © Springer Berlin Heidelberg 2007

                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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                © Springer-Verlag Berlin Heidelberg 2007

                social network analysis,contact tracing,epidemiology, personal contacts,geographical contacts,sars

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