Dynamic intimate contact social networks and epidemic interventions Page: 3
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Dynamic intimate contact social networks and epidemic interventions 3
nature, and the public health industry would benefit from the predictive measures
capable of intimate social networking computational tools. Professionals within
this field often have limited budgets and resources must be aimed in the proper
direction in order to achieve maximum results. The utilization of computational
social networking tools would allow for those within the public health industry to
anticipate the impact of demographic specific predictions, and tailor awareness, ed-
ucational, vaccination, and prophylactic programs for the greatest impact within
With limited funding and resources available to help prevent infectious disease,
public health professionals need tools to facilitate decision making regarding where
the most effective measures would be taken. Based on collected data and statis-
tical analysis, it is evident that certain demographic groups are at higher risk for
contracting certain sexually transmitted diseases and infections. For example, pre-
vious research has indicated specific achieved levels of education have a positive
correlation with higher incidence of HIV/AIDS infection (Reiche & et al. 2005).
This type of understanding, when applied to a computational social model, would
allow the individual within the public health industry to model an awareness or ed-
ucational campaign to the population with the greatest risk factors and to predict
the potential impact on this target group from avoiding future infection.
In this paper we first introduce several methodologies to analyze graphs, in par-
ticular classical graphs and their mapping on to bipartite networks; for example:
size, density, and clustering coefficients. The general algorithm of our dynamic so-
cial network of intimate contacts (DynSNIC) simulator is presented. This algorithm
generates a dynamic contact driven network with a specific degree distribution, dis-
ease dynamics and evolving population. We then describe in detail how DynSNIC
optimizes the bipartite network with a predetermined degree distribution, minimiz-
ing the number of unresolved degrees. The networks generated are then analyzed
using the graph statistics introduced earlier in this paper. A sample case study
is presented demonstrating DynSNIC's capabilities and this paper concludes with
future work in our simulator's development.
2 Previous Work
Previous work employing social network schemes has varied in context. The
EPISIMS computational analysis tool, created at the University of Maryland in
conjunction with the Los Alamos National Laboratory, estimates social network-
ing based on the transportation patterns evident within the target city, Portland,
Oregon (Eubank, Guclu, Kumar, Marathe, Srinivasan, Toroczkai & Wang 2004).
This computational model may be used to handle diverse social networking in re-
gards to the transmission of infectious disease agents. Public health officials may
utilize this model to help predict where preventive measures, including quarantine
and vaccination, would be most useful and cost effective within their populations.
Since its inception, EPISIMS research has relocated to the Virginia Bioinformatics
Institute at Virginia Tech. Their research has expanded to include simulation of
most cities, a coarser grained simulation of the entire U.S, and multiple versions
of EPISIMS based on various modeling paradigms. The Center for Computational
Analysis of Social and Organizational Systems at Carnegie Melon University has
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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/m1/3/: accessed April 29, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.