Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models

Hinch, Robert, Panovska-Griffiths, Jasmina, Probert, William J. M., Ferretti, Luca, Wymant, Chris, Di Lauro, Francesco, Baya, Nikolas, Ghafari, Mahan, Abeler-Dörner, Lucie, Fraser, Christophe, Bashton, Matthew, Smith, Darren, Nelson, Andrew, Young, Greg, McCann, Clare and The COVID-19 Genomics UK (COG-UK) Consortium, (2022) Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models. Philosophical Transactions of the Royal Society A: Mathematical Physical and Engineering Sciences, 380 (2233). p. 20210304. ISSN 1364-503X

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Abstract

The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

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