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

Paul J Birrell; Joshua Blake; Joel Kandiah; Angelos Alexopoulos; Edwin van Leeuwen; Koen Pouwels; Sanmitra Ghosh; Colin Starr; Ann Sarah Walker; Thomas A House; Nigel Gay; Thomas Finnie; Nick Gent; André Charlett; Daniela De Angelis

arXiv · 2408.04178

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

Paul J Birrell·Joshua Blake·Joel Kandiah·Angelos Alexopoulos·Edwin van Leeuwen·Koen Pouwels·Sanmitra Ghosh·Colin Starr·Ann Sarah Walker·Thomas A House·Nigel Gay·Thomas Finnie·Nick Gent·André Charlett·Daniela De Angelis

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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 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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Paul J Birrell, Joshua Blake, Joel Kandiah, Angelos Alexopoulos, Edwin van Leeuwen, Koen Pouwels, Sanmitra Ghosh, Colin Starr, Ann Sarah Walker, Thomas A House, Nigel Gay, Thomas Finnie, Nick Gent, André Charlett, Daniela De Angelis. 2024-08-08. Real-time modelling of the SARS-CoV-2 pandemic in England 2020-2023: a challenging data integration. https://arxiv.org/abs/2408.04178

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