2026-09-19T22:56:01Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1229992026-06-06T00:56:06Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
John Guttag.
Anand, Advaith.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-11-22T00:00:59Z
2019-11-22T00:00:59Z
2019
2019
https://hdl.handle.net/1721.1/122999
1127388347
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 47-48).
Understanding how contagions spread is an important task, particularly when considering infectious diseases. An individual's likelihood of getting infected by a contagion is determined by a combination of inherent susceptibility and exposure to other individuals who may spread the disease. In a real world setting, an individual's infection status may be directly observable, but it is difficult to identify whether an individual is spreading the disease. As a result, the exact influence function by which disease is transmitted is difficult to understand as well. We present a neural network based method, NeuralPALS, to learn the spreader, exposure, and infection status of individuals in a network. Unlike previously developed methods, we do not assume an exposure function and instead devise methods to learn this function. Through experiments on synthetic data we illustrate our method's efficacy in determining both spreader and infection states. We also demonstrate NeuralPALS's ability to learn different exposure functions. In addition, we utilize a dataset of patients from a large urban hospital and demonstrate our preliminary results in determining the spread of Clostridioides difficile.
by Advaith Anand.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
48 pages
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
NeuralPALS - learning exposure functions and infection probabilities for contagion spread
Thesis
application/pdf
2019-11-22T00:00:58Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Understanding how contagions spread is an important task, particularly when considering infectious diseases. An individual's likelihood of getting infected by a contagion is determined by a combination of inherent susceptibility and exposure to other individuals who may spread the disease. In a real world setting, an individual's infection status may be directly observable, but it is difficult to identify whether an individual is spreading the disease. As a result, the exact influence function by which disease is transmitted is difficult to understand as well. We present a neural network based method, NeuralPALS, to learn the spreader, exposure, and infection status of individuals in a network. Unlike previously developed methods, we do not assume an exposure function and instead devise methods to learn this function. Through experiments on synthetic data we illustrate our method's efficacy in determining both spreader and infection states. We also demonstrate NeuralPALS's ability to learn different exposure functions. In addition, we utilize a dataset of patients from a large urban hospital and demonstrate our preliminary results in determining the spread of Clostridioides difficile.</Abstract>
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