Learning the probability of activation in the presence of latent spreaders
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/111924 |
| _version_ | 1826209124085399552 |
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| author | Makar, Maggie, S.M. Massachusetts Institute of Technology |
| author2 | John Guttag. |
| author_facet | John Guttag. Makar, Maggie, S.M. Massachusetts Institute of Technology |
| author_sort | Makar, Maggie, S.M. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. |
| first_indexed | 2024-09-23T14:17:56Z |
| format | Thesis |
| id | mit-1721.1/111924 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T14:17:56Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1119242019-04-10T15:56:26Z Learning the probability of activation in the presence of latent spreaders Makar, Maggie, S.M. Massachusetts Institute of Technology John Guttag. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 71-74). When an infection spreads among members of a community, an individual's probability of becoming infected depends on both his susceptibility to the infection and exposure to the disease through contact with others. While one often has knowledge regarding an individual's susceptibility, in many cases, whether or not an individual's contacts are contagious and spreading the infection is unknown or latent. We propose a new generative model in which we model the neighbors' spreader states and the individuals' exposure states as latent variables. Combined with an individual's characteristics, we estimate the risk of infection as a function of both exposure and susceptibility. We propose a variational inference algorithm to learn the model parameters. Through a series of experiments on simulated data, we measure the ability of the proposed model to identify latent spreaders, estimate exposure as a function of one's spreading neighbors, and predict the risk of infection. Our work can be helpful in both identifying potential asymptomatic carriers of infections, and in identifying characteristics that are associated with an increased likelihood of being an undiagnosed source of contagion. by Maggie Makar. S.M. 2017-10-18T15:10:14Z 2017-10-18T15:10:14Z 2017 2017 Thesis http://hdl.handle.net/1721.1/111924 1005706741 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 74 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Makar, Maggie, S.M. Massachusetts Institute of Technology Learning the probability of activation in the presence of latent spreaders |
| title | Learning the probability of activation in the presence of latent spreaders |
| title_full | Learning the probability of activation in the presence of latent spreaders |
| title_fullStr | Learning the probability of activation in the presence of latent spreaders |
| title_full_unstemmed | Learning the probability of activation in the presence of latent spreaders |
| title_short | Learning the probability of activation in the presence of latent spreaders |
| title_sort | learning the probability of activation in the presence of latent spreaders |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/111924 |
| work_keys_str_mv | AT makarmaggiesmmassachusettsinstituteoftechnology learningtheprobabilityofactivationinthepresenceoflatentspreaders |