Submission Type
Poster
Start Date
April 2020
Abstract
Tuberculosis is a highly contagious disease and is particularly problematic in confined communities such as prisons. I simulated how Tuberculosis moves through a prison population and tested how much vaccination effort is needed to control its spread. To explore this, I tested adding ever increasing numbers of randomly placed edges in a network and determined the size of the largest component. Afterwards, I removed edges in the model using two different methods, one illustrating if the edges were removed randomly and the other starting with prisoners that had the most connections, to simulate the effect of vaccination. My results show that as edges are taken off, one would have to put in less vaccination effort if distributing based on the degree of the vertex, at about 30-40% effort, rather than removing them randomly, which would need 40-60% effort. This research could help prison administrators reduce the likelihood of prisoners contracting diseases and can also aid scientists when they’re scrambling to develop a vaccine in a short time frame to see how much effort they need to reduce an epidemic.
Recommended Citation
Mundackal, Kaitlyn, "465— Modeling Vaccine Efficacy for Tuberculosis in a Prison Population" (2020). GREAT Day Posters. 42.
https://knightscholar.geneseo.edu/great-day-symposium/great-day-2020/posters-2020/42
Included in
Applied Mathematics Commons, Biology Commons, Diseases Commons, Immunology and Infectious Disease Commons, Statistics and Probability Commons
465— Modeling Vaccine Efficacy for Tuberculosis in a Prison Population
Tuberculosis is a highly contagious disease and is particularly problematic in confined communities such as prisons. I simulated how Tuberculosis moves through a prison population and tested how much vaccination effort is needed to control its spread. To explore this, I tested adding ever increasing numbers of randomly placed edges in a network and determined the size of the largest component. Afterwards, I removed edges in the model using two different methods, one illustrating if the edges were removed randomly and the other starting with prisoners that had the most connections, to simulate the effect of vaccination. My results show that as edges are taken off, one would have to put in less vaccination effort if distributing based on the degree of the vertex, at about 30-40% effort, rather than removing them randomly, which would need 40-60% effort. This research could help prison administrators reduce the likelihood of prisoners contracting diseases and can also aid scientists when they’re scrambling to develop a vaccine in a short time frame to see how much effort they need to reduce an epidemic.
Comments
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