Dynamic intimate contact social networks and epidemic interventions Page: 16
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16 C.D. Corley, A.R. Mikler, D.J. Cook and K. Singh
infectious disease, public health professionals need tools to help them to make
decisions regarding where the most effective measures would be taken. Sexually
transmitted diseases and infections are, by definition, transferred among intimate
social settings. Although the circumstances under which these social settings are
established and maintained may vary, the common prerequisite remains an intimate
level of social atmosphere. For this reason, the development of sexually transmitted
disease mathematical and computational models must utilize a precise and efficient
social networking tool.
Our social network generator is in the foundation phase of development and
there is exciting future work to be accomplished. We analyzed the current networks
which are generated by using only preferential attachment, solely cosine similarity
and an aggregate of the previous two as the contact likelihood. The next phase
of development will assign social demographic feature distributions other than uni-
form, such as Gaussian or Poisson to each node and combine preferential attachment
with the likelihood of mixing between these social demographic groups. Evaluating
several different contact placement options will lead to a more precise social network
generated. Examples of these contact placement strategies include placing edges by
randomly choosing a node from each bipartition subset and stochastically choosing
placement, exhausting a single nodes total degree before iterating to the next node
and exhausting only one bipartition subsets total degree. One future case study is to
evaluate demographic disparity in HIV/AIDS prevalence in the population and the
effect of targetted public health information programs. This setting will incorpo-
rate behavioral data from national surveys; such as, the National Health and Social
Life Survey (NHSLS) the Center for Disease Control and Intervention's Youth Risk
Behavior Surveillance Survey (YRBSS) and integrate concepts from information
theory to study diffusion of information and the demographic-level consequences of
that information, in the population (Laumann 1994, Centers for Disease Control
and Prevention 2004).
We introduced a novel algorithm to generate social networks of intimate con-
tacts. The general algorithm generates a contact driven network with specific de-
gree distribution and a dynamic population. Next a simple heuristic was introduced
capable of performing bipartite matching in polynomial time reducing the computa-
tion power needed for the simulation from NP to E log V. Several graph-analytic
methodologies were introduced that facilitate evaluation of the generated social
networks; in particular, bipartite graph statistics. Disease dynamics can then be
analyzed on the generated networks along with tailored intervention strategies to
provide what-if analyses.
We would like to thank the National Science Foundation for support under grant
NSF IIS-0505819 and the authors of the boost graph libraries (www.boost.org) for
the use of their C++ stl graph packages in our simulator. This publication was also
made possible by Grant Number P20-MD001633 from NCMHD, its contents are
solely the responsibility of the authors and do not necessarily represent the official
views of the NCMHD.
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Corley, Courtney; Mikler, Armin R.; Cook, Diane J., 1963- & Singh, Karan P. Dynamic intimate contact social networks and epidemic interventions, article, 2008; [Geneva, Switzerland]. (digital.library.unt.edu/ark:/67531/metadc132993/m1/16/: accessed September 21, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.