Learning the probability of activation in the presence of latent spreaders
Name
1005706741-MIT.pdf
Description
Full printable version
Size
3.99 MB
Format
Adobe PDF
Checksum (MD5)
2452ca16923317c47d60486a84952fb2
Author(s)
Makar, Maggie, S.M. Massachusetts Institute of Technology
Advisor(s)
John Guttag.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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