The Influence of Social Network Graph Structure on Disease Dynamics in a Simulated Environment Metadata

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Title

  • Main Title The Influence of Social Network Graph Structure on Disease Dynamics in a Simulated Environment

Creator

  • Author: Johnson, Tina V.
    Creator Type: Personal

Contributor

  • Chair: Mikler, Armin R.
    Contributor Type: Personal
    Contributor Info: Major Professor
  • Committee Member: Atkinson, Samuel F.
    Contributor Type: Personal
  • Committee Member: Ramisetty-Mikler, Suhasini
    Contributor Type: Personal
  • Committee Member: Sweany, Philip H.
    Contributor Type: Personal
  • Committee Member: Yuan, Xiaohui
    Contributor Type: Personal

Publisher

  • Name: University of North Texas
    Place of Publication: Denton, Texas

Date

  • Creation: 2010-12

Language

  • English

Description

  • Content Description: The fight against epidemics/pandemics is one of man versus nature. Technological advances have not only improved existing methods for monitoring and controlling disease outbreaks, but have also provided new means for investigation, such as through modeling and simulation. This dissertation explores the relationship between social structure and disease dynamics. Social structures are modeled as graphs, and outbreaks are simulated based on a well-recognized standard, the susceptible-infectious-removed (SIR) paradigm. Two independent, but related, studies are presented. The first involves measuring the severity of outbreaks as social network parameters are altered. The second study investigates the efficacy of various vaccination policies based on social structure. Three disease-related centrality measures are introduced, contact, transmission, and spread centrality, which are related to previously established centrality measures degree, betweenness, and closeness, respectively. The results of experiments presented in this dissertation indicate that reducing the neighborhood size along with outside-of-neighborhood contacts diminishes the severity of disease outbreaks. Vaccination strategies can effectively reduce these parameters. Additionally, vaccination policies that target individuals with high centrality are generally shown to be slightly more effective than a random vaccination policy. These results combined with past and future studies will assist public health officials in their effort to minimize the effects of inevitable disease epidemics/pandemics.
  • Physical Description: ix, 91 p. : ill., maps

Subject

  • Keyword: Social networks
  • Keyword: centrality simulation
  • Keyword: epidemiology
  • Library of Congress Subject Headings: Epidemics -- Social aspects.
  • Library of Congress Subject Headings: Communicable diseases -- Social aspects.
  • Library of Congress Subject Headings: Social networks.
  • Library of Congress Subject Headings: Vaccines -- Social aspects.

Collection

  • Name: UNT Theses and Dissertations
    Code: UNTETD

Institution

  • Name: UNT Libraries
    Code: UNT

Rights

  • Rights Access: public
  • Rights License: copyright
  • Rights Holder: Johnson, Tina V.
  • Rights Statement: Copyright is held by the author, unless otherwise noted. All rights reserved.

Resource Type

  • Thesis or Dissertation

Format

  • Text

Identifier

  • OCLC: 723137568
  • UNT Catalog No.: b3992823
  • Archival Resource Key: ark:/67531/metadc33173

Degree

  • Degree Name: Doctor of Philosophy
  • Degree Level: Doctoral
  • Degree Discipline: Computer Science and Engineering
  • Academic Department: Department of Computer Science and Engineering
  • Degree Grantor: University of North Texas

Note