Bayesian Parameter Estimation on Three Models of Influenza

Mathematical models of viral infections have been informing virology research for years. Estimating parameter values for these models can lead to understanding of biological values. This has been successful in HIV modeling for the estimation of values such as the lifetime of infected CD8 T-Cells. However, estimating these values is notoriously difficult, especially for highly complex models. We use Bayesian inference and Monte Carlo Markov Chain methods to estimate the underlying densities of the parameters (assumed to be continuous random variables) for three models of influenza. We discuss the advantages and limitations of parameter estimation using these methods. The data and influenza models used for this project are from the lab of Dr. Amber Smith in Memphis, Tennessee.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Mathematics

Department dc:contributor.department

Mathematics

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2017

Author dc:creator

• Torrence, Robert Billington

Chair dc:contributor.committeechair

• Chung, Matthias

Committee members dc:contributor.committeemember

• Borggaard, Jeffrey T.
• Smith, Amber Marie
• Ciupe, Stanca M.

• Influenza
• Bayesian Inference
• Parameter Estimation
• Mathematical Biology
• MCMC Methods
• Metropolis Algorithm

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:11582

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/77611