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

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.

Creator(s): Johnson, Tina V.
Creation Date: December 2010
Partner(s):
UNT Libraries
Collection(s):
UNT Theses and Dissertations
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Total Uses: 294
Past 30 days: 2
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Publisher Info:
Publisher Name: University of North Texas
Place of Publication: Denton, Texas
Date(s):
  • Creation: December 2010
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.

Degree:
Level: Doctoral
Physical Description:

ix, 91 p. : ill., maps

Language(s):
Subject(s):
Keyword(s): Social networks | centrality simulation | epidemiology
Contributor(s):
Partner:
UNT Libraries
Collection:
UNT Theses and Dissertations
Identifier:
  • OCLC: 723137568 |
  • UNTCAT: b3992823 |
  • ARK: ark:/67531/metadc33173
Resource Type: Thesis or Dissertation
Format: Text
Rights:
Access: Public
License: Copyright
Holder: Johnson, Tina V.
Statement: Copyright is held by the author, unless otherwise noted. All rights reserved.