  A central pillar of the UK's response to the SARS-CoV-2 pandemic was the
provision of up-to-the moment nowcasts and short term projections to monitor
current trends in transmission and associated healthcare burden. Here we
present a detailed deconstruction of one of the 'real-time' models that was key
contributor to this response, focussing on the model adaptations required over
three pandemic years characterised by the imposition of lockdowns, mass
vaccination campaigns and the emergence of new pandemic strains. The Bayesian
model integrates an array of surveillance and other data sources including a
novel approach to incorporating prevalence estimates from an unprecedented
large-scale household survey. We present a full range of estimates of the
epidemic history and the changing severity of the infection, quantify the
impact of the vaccination programme and deconstruct contributing factors to the
reproduction number. We further investigate the sensitivity of model-derived
insights to the availability and timeliness of prevalence data, identifying its
importance to the production of robust estimates.
