TY - JOUR
T1 - Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models
AU - The COVID-19 Genomics UK (COG-UK) Consortium
AU - Hinch, Robert
AU - Panovska-Griffiths, Jasmina
AU - Probert, William J M
AU - Ferretti, Luca
AU - Wymant, Chris
AU - Di Lauro, Francesco
AU - Baya, Nikolas
AU - Ghafari, Mahan
AU - Abeler-Dörner, Lucie
AU - Fraser, Christophe
AU - Bashton, Matthew
AU - Smith, Darren
AU - Nelson, Andrew
AU - Young, Gregory R.
AU - McCann, Clare
N1 - Matthew Bashton, Andrew Nelson, Clare McCann, Greg Young and Darren Smith are members of the COVID-19 Genomics UK consortium.
PY - 2022/10/3
Y1 - 2022/10/3
N2 - 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'.
AB - 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'.
KW - COVID-19/epidemiology
KW - Communicable Disease Control
KW - Humans
KW - SARS-CoV-2/genetics
KW - Seasons
U2 - 10.1098/rsta.2021.0304
DO - 10.1098/rsta.2021.0304
M3 - Article
C2 - 35965459
VL - 380
JO - Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
JF - Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
SN - 0962-8428
IS - 2233
M1 - 20210304
ER -