Skip navigation
  • Home
  • Browse
    • Communities
      & Collections
    • Browse Items by:
    • Publication Date
    • Author
    • Title
    • Subject
    • Department
  • Sign on to:
    • My MacSphere
    • Receive email
      updates
    • Edit Profile


McMaster University Home Page
  1. MacSphere
  2. Open Access Dissertations and Theses Community
  3. Open Access Dissertations and Theses
Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/24945
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorBolker, Ben-
dc.contributor.advisorDushoff, Jonathan-
dc.contributor.authorLi, Michael-
dc.date.accessioned2019-10-03T19:42:58Z-
dc.date.available2019-10-03T19:42:58Z-
dc.date.issued2019-
dc.identifier.urihttp://hdl.handle.net/11375/24945-
dc.description.abstractMathematical and statistical models are widely used in studying infectious disease. They provide important insights – including mechanisms of the spread of infectious disease, forecast epidemic size and duration, and effects of intervention strategies – which are useful in studying and combating infectious disease. Over the last couple of decades, modeling techniques have advanced tremendously due to improvements in computational power, data availability, and data accessibility; this enables researchers to use various modeling approaches to capture more realistic aspects of infectious disease epidemics. Despite having flexible modeling techniques, these approaches use different modeling assumptions to incorporate information and propagate uncertainty, often arriving at inconsistent conclusions. My work focuses on exploring and improving methods for modeling the spread of infectious disease; in particular, exploring the state of the art techniques for disease modeling in real epidemic outbreaks and simulation settings. Motivated by a synthetic forecasting challenge inspired by the 2014 West African Ebola outbreak, I compared simple Markov chain Monte Carlo approaches to simulated epidemics (Chapter 2). Using high-resolution data from an ongoing rabies contact- tracing study, I apply robust techniques to reassess global historical risk estimates of canine rabies (Chapter 3), and show that disease trait correlations bias generation time estimates, with implications for conclusions about control (Chapter 4). In Chapter 5, I developed a method to improve modeling trait relationships while incorporating phylogenetic relationships by reformulating phylogenetic mixed models to improve flexibility and speed.en_US
dc.language.isoenen_US
dc.subjectinfectious diseaseen_US
dc.subjectrabiesen_US
dc.subjectmathematical modelen_US
dc.subjectepidemiologyen_US
dc.titleMETHODS FOR MODELING THE SPREAD OF INFECTIOUS DISEASEen_US
dc.typeThesisen_US
dc.contributor.departmentBiologyen_US
dc.description.degreetypeDissertationen_US
dc.description.degreeDoctor of Philosophy (PhD)en_US
Appears in Collections:Open Access Dissertations and Theses

Files in This Item:
File Description SizeFormat 
Li_Michael_2019Sept_PhD.pdf
Open Access
3.91 MBAdobe PDFView/Open
Show simple item record Statistics


Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.

Sherman Centre for Digital Scholarship     McMaster University Libraries
©2022 McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4L8 | 905-525-9140 | Contact Us | Terms of Use & Privacy Policy | Feedback

Report Accessibility Issue