Learning the magnitude and duration of influence of infections
Name
1127911615-MIT.pdf
Size
650.32 KB
Format
Adobe PDF
Checksum (MD5)
641906ea3ab015962f042820c76dbffb
Author(s)
Mu, Emily,M. Eng.Massachusetts Institute of Technology.
Advisor(s)
John Guttag.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Clostridioides difficile infections (CDIs) impose a substantial burden on the healthcare system leading to poor health out comes, mortality and costs to the heath-care system estimated at greater than $5 billion. One of the reasons why CDIs are hard to control is the contribution of individual infections to the risk of transmission is not well understood. In this paper, we propose modeling incident infections using a Hawkes process, which is a self-exciting stochastic process, encoding the intuition that new infections trigger further infections. Using data from a large urban hospital, we demonstrate that our approach reveals different patterns of infection spread across patient care units. These insights can be used to guide unit-specific interventions aimed at interrupting transmission.
Description
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 57-60).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
Persistent DSpace Link
