The Influence of Social Network Graph Structure on Disease Dynamics in a Simulated Environment Page: 8
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Although the results from simulated environments are not likely to completely transfer to real
life, the insight gained from such research can certainly help direct investigations in applied set-
tings. The questions addressed here are designed to promote interest in graph theory as it applies to
disease spread through social networks, particularly as an approach that can be used to prevent or
impede an epidemic/pandemic. Targeted vaccination policies are explored at the theoretical level
in this research in expectation that the results will have relevance in practice.
This chapter has introduced key concepts and provided the motivation for the research pre-
sented herein. The remainder of this dissertation is structured as follows: Chapter 2, where most
of the significant literature is reviewed, establishes the necessary background in the areas of epi-
demiology, disease models, the basic reproduction number, and graph theory. Historical infor-
mation in the field of epidemiology is presented, highlighting several of the main contributors to
this area of interest, followed by an overview of an established disease model, SIR (susceptible-
infectious-removed). Next, the basic reproduction number, Ro0 is formally defined and discussed.
The remainder of Chapter 2 focuses on graph theory concepts and definitions. Chapter 3 presents
and discusses the experimental results related to the analysis of graph structure and outbreak sever-
ity. The findings in Chapter 3 relate to Research Question i. Chapter 4 illustrates the importance
of key individuals in a disease outbreak. Vaccination methods are simulated to address Research
Questions ii and iii. Chapter 5 presents the main perspectives of this study and summarizes the
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Reference the current page of this Dissertation.
Johnson, Tina V. The Influence of Social Network Graph Structure on Disease Dynamics in a Simulated Environment, dissertation, December 2010; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc33173/m1/18/: accessed May 27, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; .