Dynamic intimate contact social networks and epidemic interventions Page: 4
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4 C.D. Corley, A.R. Mikler, D.J. Cook and K. Singh
also built a simulation framework, BIOWAR, to predict the effect of a large-scale
terrorist attack or infection outbreak. BIOWAR incorporates multi-agent systems,
census track data, human social behavior and wind dispersion data(?).
Avenues of previous social models of sexually transmitted diseases and infec-
tions have included the categorization of individuals into groups based on the dif-
fering stages of infection of each disease condition versus demographic factors such
as age, sex, and geographic location. Gonorrheal and Chlamydial infections have
been predicted using these types of models, which have been validated through
available statistics. Due to the nature of these illnesses, statistical data is often
very difficult to collect (Aral, Hughes, Stoner, Whittington, Handsfield, Ander-
son & Holmes 1999, Ward, Ison, Day, Martin, Ghani, Garnett, Bell, Kinghorn
& Weber 2000, Wylie & Jolly 2001). Epidemiological models for sexually trans-
mitted conditions have also been created based on the accumulation of contact
tracing data. This type of data may be unreliable due to individual recall error and
privacy constraints, but is the common method of understanding Syphilis trans-
mission (Chen, Kodagoda, Lawerence & Kerndt 2002, Gunn, Harper, Borntrager,
Gonzales & St.Louis 2000, Rosenberg, Moseley, Kahn, Kissinger, Rice, Kendall,
Coughlin & Farley 1999, Williams, Klausner, Whittington, Handsfield, Celum &
Holmes 1999, Wylie & Jolly 2001).
Providing social networks for sexually transmitted diseases and infections de-
pend upon numerous implications, each of which must be taken into account. While
studying the epidemiological patterns of these conditions, one must individually
analyze the interaction potential between host and pathogen, whether viral or bac-
terial, as well as interventions regarding health-care, before analyzing the potential
causative associations where the pathogen may have been acquired. Since pathogen
acquisition may hold the answer to interventions and preventative measures in the
future, the use of social networking is a practice which may save much needed time
and resources.
3 Graph statistics
3.1 Classical graph statistics
Analyzing graphs with various statistical properties has become an important
component in describing real world complex networks. First, we briefly introduce
basic graph-theoretic statistics including clustering coefficients. Next, we describe
the modification of these methods to analyze properties of bipartite graphs..
The analysis of classical graphs is a well studied field in graph-theory and many
methodologies exist to describe the nature of these graphs. Traditionally, a classical
graph, G, is defined G = (V, E) where V is the set of vertices and E is the set of
edges in the graph E C V x V. The neighborhood N(v) of vertex v is defined as
N(v) { {u} : es,, E E}. The degree of vertex v is the cardinality of the set of
edge connections from v to its neighborhood, d - N(v). Basic statistics that
describe this graph include its size n V, number of edges m E, average
degree k 2 and its density,6(G), which represents the probability any two
randomly chosen vertices are connected, 6(G) ( 1
In addition, we consider two more statistics in the context of graphs, degree dis-
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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, September 9, 2008; [Geneva, Switzerland]. (https://digital.library.unt.edu/ark:/67531/metadc132993/m1/4/: accessed April 19, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Engineering.