NeuralPALS - learning exposure functions and infection probabilities for contagion spread

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.

Bibliographic Details
Main Author: Anand, Advaith.
Other Authors: John Guttag.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2019
Subjects:
Online Access:https://hdl.handle.net/1721.1/122999
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author Anand, Advaith.
author2 John Guttag.
author_facet John Guttag.
Anand, Advaith.
author_sort Anand, Advaith.
collection MIT
description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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spelling mit-1721.1/1229992019-11-22T03:03:28Z NeuralPALS - learning exposure functions and infection probabilities for contagion spread Anand, Advaith. 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. 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 2019-11-22T00:00:59Z 2019-11-22T00:00:59Z 2019 2019 Thesis https://hdl.handle.net/1721.1/122999 1127388347 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 48 pages application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Anand, Advaith.
NeuralPALS - learning exposure functions and infection probabilities for contagion spread
title NeuralPALS - learning exposure functions and infection probabilities for contagion spread
title_full NeuralPALS - learning exposure functions and infection probabilities for contagion spread
title_fullStr NeuralPALS - learning exposure functions and infection probabilities for contagion spread
title_full_unstemmed NeuralPALS - learning exposure functions and infection probabilities for contagion spread
title_short NeuralPALS - learning exposure functions and infection probabilities for contagion spread
title_sort neuralpals learning exposure functions and infection probabilities for contagion spread
topic Electrical Engineering and Computer Science.
url https://hdl.handle.net/1721.1/122999
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