Usage
  • 16 views
  • 102 downloads

Modeling the Transmission of Tuberculosis in Long-Term Care Facilities using a Network Model

  • Author / Creator
    Muscat, Alison
  • Tuberculosis (TB) infection in the elderly is frequently misdiagnosed. The resulting treatment delay may increase TB transmission which is higher in long-term care (LTC) facilities. The CDC's recommendations to prevent and control TB in LTC facilities include TB education and better initial screening methods on entry into the facility. However, TB education programs might not always be given priority and comparing screening methods experimentally is often not feasible. To address these problems, we develop a general conceptual SEIR network model for LTC facilities and present a case study of a specific outbreak that occurred in a nursing home in Arkansas. We investigate the impact of reducing diagnosis delay on the Arkansas outbreak and evaluate potential screening programs for that setting. Our results quantify the effectiveness of reducing diagnosis delay, justifying a good TB education program. We also suggest multiple screening programs that were found to produce equivalent results.

  • Subjects / Keywords
  • Graduation date
    2012-09
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3431H
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Mathematical and Statistical Sciences
  • Specialization
    • Applied Mathematics
  • Supervisor / co-supervisor and their department(s)
    • Li, Michael (Mathematical and Statistical Sciences)
  • Examining committee members and their departments
    • Muldowney, James (Mathematical and Statistical Sciences)
    • Wang, Hao (Mathematical and Statistical Sciences)
    • Long, Richard (Medicine)