| Summary: | 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.
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