Author Type

Graduate Student

Date of Award

2017

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Agarwal, Ankur

Thesis/Dissertation Co-Chair

Furht, Borko

Abstract

Identifying and tracking individuals affected by this virus in densely populated areas is a unique and an urgent challenge in the public health sector. Currently, mapping the spread of the Ebola virus is done manually, however with the help of social contact networks we can model dynamic graphs and predictive diffusion models of Ebola virus based on the impact on either a specific person or a specific community. With the help of this model, we can make more precise forward predictions of the disease propagations and to identify possibly infected individuals which will help perform trace – back analysis to locate the possible source of infection for a social group. This model will visualize and identify the families and tightly connected social groups who have had contact with an Ebola patient and is a proactive approach to reduce the risk of exposure of Ebola spread within a community or geographic location.

Extent

70 p.

Comments

Includes bibliography.|Thesis (M.S.)--Florida Atlantic University, 2017.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00004919

Format

pdf

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