Real-time modelling of the SARS-CoV-2 pandemic in England 2020–2023: a challenging data integration

Abstract 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 a key contributor to this response, focussing on the model adaptations required over 3 pandemic years characterized 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 incorporate 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.