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Efficient Bayesian parameter inference for COVID-19 transmission models

Thesis type
(Project) M.Sc.
Date created
2020-12-17
Authors/Contributors
Author: Doig, Renny
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
Identifier
etd21179
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Wang, Liangliang
Language
English
Download file Size
input_data\21081\etd21179.pdf 650.21 KB

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