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

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’.

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

“…They include well known graphic types such as scatter plots [ 8 ] (fig. 4); choropleth maps [ 12 ] (fig. 2), [ 15 ] (fig.…”

Statistical and agent-based modelling of the transmissibility of different SARS-CoV-2 variants in England and impact of different interventions

“…Within the model, we incorporated a dynamic transmission probability to be reflective of changes in the social mobility within different layers of society, updated weekly ( figure 2 ). Overall levels were reflective of reported Google mobility changes [ 29 ], but scaled at different times to reflect changes such as school holidays, national lockdowns and more gradual social mobility increase during the phased reopening from March 2021 as in other studies [ 30 ]. These were necessary since the mobility changes in the Google reports stratify society in different ways to how we stratify society in the layers depicted in figure 2 .…”

Technical challenges of modelling real-life epidemics and examples of overcoming these

“…Sanz-Leon et al [ 7 ] investigate vaccine hesitancy and the variant in Australia. Panovska-Griffiths et al [ 5 ] and Hinch et al [ 6 ] are focused on the English COVID-19 epidemics and both estimate the transmissibility of variants and evaluate interventions, with the former combining the ABM with a statistical regression and making a policy contribution with the results, while the latter is taking a geospatial approach.…”

“…Agent-based models (ABMs) figure prominently in this issue with [5][6][7][8] all using them to explore different questions. All of these involve fitting to real data, of course, and all consider the effect of different variants of SARS-CoV-2.…”

“…Sanz-Leon et al [7] investigate vaccine hesitancy and the δ variant in Australia. Panovska-Griffiths et al [5] and Hinch et al [6] are focused on the English COVID-19 epidemics and both estimate the transmissibility of variants and evaluate interventions, with the former combining the ABM with a statistical regression and making a policy contribution with the results, while the latter is taking a geospatial approach. improved methods [3][4][5][6][7][8] fitting data [3,[9][10][11] agent-based models [5,9,10,12,13] model relationships [5,6,14,15] estimating R (t) [8,11,13,16] methodology recommendations [17,18] variants […”