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

TL;DR

This paper details the development and adaptation of a Bayesian real-time model for SARS-CoV-2 in England, integrating diverse data sources to improve epidemic tracking and understanding over three years.

Contribution

It introduces a novel data integration approach, including large-scale household survey prevalence estimates, for real-time epidemic modeling during a complex pandemic.

Findings

Quantified the impact of vaccination campaigns.

Assessed the influence of data timeliness on model robustness.

Deconstructed factors affecting the reproduction number.

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.