The Influence of Social Network Graph Structure on Disease Dynamics in a Simulated Environment Page: 77
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key individuals can be identified for vaccination. Vaccination methods include high contact, trans-
mission, spread, random, and low contact. After vaccination of ten percent of the population, an
outbreak is simulated and measurements of R0, duration, and the proportion of the population
infected are recorded. All vaccination methods are found to lower the value of Ro0 and decrease
the proportion of the population infected. Vaccination of individuals who make fewer contacts is
found to be the least effective strategy, but none of the vaccination methods are consistently more
effective than the others. Random vaccination generally attains better results than low contact
vaccination, but is found to be slightly less successful than the other strategies.
5.1. Implications to Public Health and Policy Development
The experiments presented in this dissertation suggest that both reducing an individual's ef-
fective neighborhood size and limiting the number random contacts have the potential to decrease
the severity of a disease outbreak. This does not necessarily require an alteration of the actual
personal connections, rather a reduction in the ability for the connections to transfer disease. Pub-
lic awareness, prophylactic use, quarantine, and vaccination are all methods that can effectively
reduce disease transfer. Early intervention may prevent the occurrence of an epidemic/pandemic
or limit the severity of an outbreak. When vaccination methods are employed, the findings herein
suggest that random vaccination is nearly as effective as targeted policies if the proportion of the
population to be vaccinated is low (10% for the studies conducted in this research). Future stud-
ies may reveal that targeted strategies are more effective if the proportion of individuals that are
vaccinated is increased.
It is not possible to simulate a disease outbreak with complete accuracy. The variance in disease
and population parameters along with the random nature of disease spread make it a tremendous
challenge to portray an epidemic/pandemic in a simulated environment. Nevertheless, this is a
challenge that must be addressed in order to advance our knowledge and understanding of disease
Here’s what’s next.
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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/m1/87/: accessed May 22, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; .