Dissertation/Thesis Abstract

Insights from Respondent Driven Sampling Datasets Via Complex Network Analysis
by Grubb, Jacob, M.S., Southern Illinois University at Edwardsville, 2020, 37; 28031133
Abstract (Summary)

This research applies network theory to the Sexual Acquisition and Transmission of HIV Cooperative Agreement Program (SATHCAP) dataset to determine unique and/or shared attributes of important nodes within three US cities: Chicago, Los Angeles, and Raleigh-Durham. We generate large forests of nodes representing successful chains of recruitment for each city, then attempt to identify valuable nodes. We use betweenness centrality as a metric for a nodes importance, valuing the bridging qualities of high-betweenness nodes. After identifying these high-betweenness nodes, we find attributes that distinguish these nodes from the underlying population, presenting both the shared exceptional attributes between the high-betweenness nodes and the unique exceptional attributes within the high betweenness nodes. By assessing these exceptional attributes, we are able to draw conclusions about several key biomarkers that represent prime candidates for a targeted intervention program to slow the spread of an HIV pandemic.

Indexing (document details)
Advisor: Matta, John
Commitee: McKenney, Mark, Ercal, Gunes
School: Southern Illinois University at Edwardsville
Department: Computer Science
School Location: United States -- Illinois
Source: MAI 82/3(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Computer science, Epidemiology, Information Technology
Keywords: Complex Networks, Respondent Driven Sampling, Sexual Acquisition and Transmission of HIV Cooperative Agreement Program
Publication Number: 28031133
ISBN: 9798672111087
Copyright © 2020 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest