Learning the magnitude and duration of influence of infections

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Mu, Emily,M. Eng.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• John Guttag.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123046

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123046