Efficient Bayesian parameter inference for COVID-19 transmission models

Author: 
Date created: 
2020-12-17
Identifier: 
etd21179
Keywords: 
COVID-19
Bayesian inference
Sequential Monte Carlo
Transmission models
Model comparison
Abstract: 

Many transmission models have been proposed and adapted to reflect changes in policy for mitigating the spread of COVID-19. Often these models are applied without any formal comparison with previously existing models. In this project, we use an annealed sequential Monte Carlo (ASMC) algorithm to estimate parameters of these transmission models. We also use Bayesian model selection to provide a framework through which the relative performance of transmission models can be compared in a statistically rigorous manner. The ASMC algorithm provides an unbiased estimate of the marginal likelihood which can be computed at no additional computational cost. This offers a significant computational advantage over MCMC methods which require expensive post hoc computation to estimate the marginal likelihood. We find that ASMC can produce results that are comparable to MCMC in a fraction of the time.

Document type: 
Graduating extended essay / Research project
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Supervisor(s): 
Liangliang Wang
Department: 
Science: Department of Statistics and Actuarial Science
Thesis type: 
(Project) M.Sc.
Statistics: