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

We have an opportunity not only to interact with 3D content but also to immerse ourselves in it via Virtual Reality (VR). This work is deeply inspired by my experience as a Ukrainian witnessing the recent turmoil in my homeland. I wanted people around the globe to experience the horrors that are unfolding. The Voice of the People, explores narrative storytelling through VR. Ultimately, the viewer will be able to put on a VR headset and become deeply immersed in the story. With this technology, the user intimately experiences the war and devastation created by Russian occupation first hand. The end of World War II is a critical time in my country’s history. Included in the 3D environment are video segments of those affected by the current Russian occupation; historical themes underscore the narrative and help the viewer understand the recurring aggression by Russia that is part of Ukraine’s history. / Includes bibliography. / Thesis (M.F.A.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection