Novel Bayesian inference in epidemics − model assessment and integrating epidemiological and genetic data
Abstract
Work in this thesis represents advances in addressing two key challenges in epidemiological
and ecological modelling: the lack of an effective and easily deployable modelassessment
tool and a statistically sound joint inferential framework for epidemic
and evolutionary processes. Firstly, we present a novel statistical framework that
combines classical and Bayesian reasoning in testing for mis-specifications of a spatiotemporal
model by investigating the consistency of so-called latent residuals with a
known sampling distribution using a classical hypothesis test. Second, we devise a
statistically sound Bayesian framework which facilitates the integration of epidemiological
and genetic data; specifically, we demonstrate how the transmitted sequences
can be effectively imputed so that the transmission dynamics of the joint epidemic and
evolution process can be accurately recovered and also any unsampled infected hosts
can be naturally accommodated in the analysis. The new methodology we propose
are assessed using simulation studies and they are applied to two real-world epidemic
datasets which respectively describe the spread of an invasive plant and foot-andmouth
disease in the UK, which shows that they may greatly enhance our ability to
understand the transmission dynamics of disease and therefore lead to more efficient
disease management.