Advanced bayesian modelling for the analysis of outbreaks and shifting epidemiological dynamics

The emergence, spread, and establishment of an infectious disease within a population brings about a plethora of challenges for public health organisations, whose aim is to reduce disease burden while having access to limited information. In this thesis, we develop statistical models and analyses to support public health response, addressing uncertainties that are inherent to epidemics. The work is divided into two parts, focusing on the last century’s biggest pandemics. In the first part, we focus on the emergence of novel pathogens and variants of concern, with applications to SARS-CoV-2. Firstly, we develop an adjustment to early reproduction number estimates, when generations of infections have not been reported. Our adjustment is shown to reduce early biases in simulation studies. Secondly, we develop a multi-strain Bayesian model to describe fluctuations in hospital fatality rates in Brazil following the emergence of the Gamma variant. By synthesising data from separate sources, we estimate the proportion of patients with either variant in hospitals across Brazil, and quantify the impact of healthcare pressures, variant, and location effects. In the second part of the thesis, we describe changes in transmission dynamics and burden of HIV, using data from the Rakai Community Cohort Study. In the first project, we develop a phylogenetic pipeline to estimate HIV time since infection from viral sequences, and develop statistical models to refine estimates by incorporating testing histories and known transmission network. By dating transmissions, we are able to describe changes in transmission patterns, highlighting shifts in the age-profile of the sources. Finally, we provide detailed descriptions of shifts in the age and gender compositions of the burden of HIV and viraemia in Uganda. We obtain estimates at the age level by developing non-parametric models sharing information across age groups. We conclude by proposing novel metrics to inform prevention strategies.