Browsing by Subject "Epidemics"
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Item Alafia(2009-12-01) Nyaphaga, Issa; Okediji, Moyo; Willmann, Travis (photographer)On December 1, 2009 the Fine Arts Library hosted Alafia, a performance and installation in honor of World Aids Day. The performance and installation of African art was presented by Issa Nyaphaga and UT Art History professor Moyo Okediji. Alafia – which means “health” in Yoruba – focused on health matters (art and healing go hand-in-hand in African and African diasporic arts), in particular the scourge of epidemic and pandemic ailments such as AIDS, swine flu, tuberculosis and Ebola. A procession of masks was to start from the “Igbale” (or shrine) at the Warfield Center for African and African American Studies and lead to the Fine Arts Library, where the grand performance and installation took place. Although the procession did not take place due to rain, the masks were on display on the third floor of the FAL through December 8. Photos by Travis Willman. Design by Mark Doroba.Item Epidemics on graphs under uncertainty(2020-08) Hoffmann, Jessica Hélène; Caramanis, Constantine; Klivans, Adam; Dimakis, Alexandros G; Shakkottai, SanjayEpidemic processes can model anything that spreads. As such, they are a useful tool for studying not only human diseases, but also network attacks, spikes in the brain, the propagation of real or fake news, the spread of viral tweets, and other processes. This proposed thesis focuses on epidemics spreading on an underlying graph. Currently, most state-of-the-art research in this field assumes some form of perfect observation of the epidemic process. This is an unrealistic assumption for many real-life applications, as the recent COVID-19 pandemic tragically demonstrated: data is scarce, delayed, and/or imprecise for human epidemics, and symptoms may appear in a non-deterministic fashion - if they appear at all. We show in this work not only that the algorithms developed previously are not robust to adding noise into the observation, but that some theoretical results cannot be adapted to this setting. In other words, uncertainty fundamentally changes how we must approach epidemics on graphs.