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.
Authors
- A. Sarah Walker (ORCID: https://orcid.org/0000-0002-0412-8509)
- André Charlett (ORCID: https://orcid.org/0000-0001-7154-0432)
- Sanmitra Ghosh (ORCID: https://orcid.org/0000-0002-4879-7587)
- Daniela De Angelis (ORCID: https://orcid.org/0000-0001-6619-6112)
- Colin Starr
- Joel Kandiah (ORCID: https://orcid.org/0000-0001-8314-0620)
- Edwin van Leeuwen (ORCID: https://orcid.org/0000-0002-2383-5305)
- Thomas Finnie (ORCID: https://orcid.org/0000-0001-5962-4211)
- Koen B. Pouwels (ORCID: https://orcid.org/0000-0001-7097-8950)
- Thomas House (ORCID: https://orcid.org/0000-0001-5835-8062)
- Angelos Alexopoulos (ORCID: https://orcid.org/0000-0002-5723-6570)
- Nick Gent (ORCID: https://orcid.org/0000-0002-2605-7369)
- Paul Birrell (ORCID: https://orcid.org/0000-0001-8131-4893)
- Joshua Blake (ORCID: https://orcid.org/0000-0001-7522-3741)
- Nigel Gay
Institutions
- World Health Organization - Pakistan (PK)
- University of Cambridge (GB)
- University of Manchester (GB)
- National Institute for Health and Care Research (GB)
- University of Oxford (GB)
- MRC Biostatistics Unit (GB)
- Athens University of Economics and Business (GR)
- Manchester University (US)
Publication Details
- Journal
- Journal of the Royal Statistical Society Series A (Statistics in Society)
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1093/jrsssa/qnaf030
- Citations
- 4
- Primary Topic
- COVID-19 epidemiological studies
- Type
- article
- Field-Weighted Citation Impact
- 5.47
Funders
- Royal Perth Hospital Medical Research Foundation
- National Institute for Health and Care Research
- Engineering and Physical Sciences Research Council