The origins and potential future of SARS-CoV-2 variants of concern in the evolving COVID-19 pandemic

Abstract

One year into the global COVID-19 pandemic, the focus of attention has shifted to the emergence and spread of SARS-CoV-2 variants of concern (VOCs). After nearly a year of the pandemic with little evolutionary change affecting human health, several variants have now been shown to have substantial detrimental effects on transmission and severity of the virus. Public health officials, medical practitioners, scientists, and the broader community have since been scrambling to understand what these variants mean for diagnosis, treatment, and the control of the pandemic through nonpharmaceutical interventions and vaccines. Here we explore the evolutionary processes that are involved in the emergence of new variants, what we can expect in terms of the future emergence of VOCs, and what we can do to minimise their impact.

Figure 1

The role of heterogeneity in the probability that a variant establishes within a population. Illustrated here is a predominantly susceptible population, with an average number of ten contacts per case and no competition for susceptible hosts between the variant and non-variant. If the number of contacts per individual is Poisson distributed and there is a constant chance of infection per contact, the probability of establishment rises with the chance of infection per contact as shown by the black curve. Here, variants are not expected to persist unless the transmission probability is above 10%, as only then are cases expected to give rise to at least one new case (R_t > 1). If cases vary in their infectiousness, then variants are less likely to establish because more cases fail to have any onward transmission (blue curve, where we assume that half of the cases are three times as infective as the other half). If, however, there is variability in contact number or activity level, variants are more likely to establish because individuals with more contacts are more likely to get infected and then more likely to pass on the variant (red curve, assuming the contact distribution is negative binomial with a dispersion parameter of k = 3). Because the disease spreads more easily among the subset of active people, heterogeneity in contacts also reduces the critical transmission probability above which establishment is possible (red curve rises above zero earlier, causing R_t > 1). (Based on methods in reference.)

Figure 2

The rise in frequency of B.1.1.7 in nine regions of England. Data are weekly measures of the fraction of Pillar 2 tests that showed SGTF, taken from Public Health England Technical Briefing 5 (http://www.gov.uk/government/publications/investigation-of-novel-sars-cov-2-variant-variant-of-concern-20201201).

A simplified model of the evolution of the first step escape mutant (I^∗) using an SIR model framework. The transmission rate $\beta$ to susceptible individuals and the clearance rate $\kappa$ are assumed to be equal for both variants. Only the escape mutant is partially able to infect resistant individuals (at a rate reduced by a factor p relative to the rate of infecting susceptible individuals). The model considers only the first step in the accumulation of mutations that increase the ability to reinfect otherwise resistant individuals but could be extended to model subsequent steps by mutation.

Figure 3

Weekly new cases per 100,000 people for Ontario, Canada. Data displayed as total count, those due to non-VOC, and those due to VOC (primarily B.1.1.7, as measured by a PCR test for the N501Y mutation). Plots are moving seven-day averages, using data from https://covid19-sciencetable.ca/ontario-dashboard/. The decreasing total case count between 21 January, 2021 and early March, 2021 masked an underlying increase in the case count due to VOCs.

Figure 4

The spike in case numbers in the spring of 2021 was predicted by models of VOC dynamics. Case numbers in Canadian provinces (black circles; data up to 8 March, 2021) were fit using a dynamic modeling approach, either ignoring VOC (purple) or allowing the spread of VOC with a transmission advantage of 50% (grey). In each panel, the VOC is introduced a week before the date of the first publicly reported case in each province (vertical dashed line) with initial numbers set to match the observed VOC numbers in early March. Subsequent case numbers, which were not used in the model fits, are shown as hollow circles. The spike in cases led to various emergency restrictions (vertical solid lines), which subsequently brought cases down over the next couple of weeks. Poor model predictions in a couple of provinces are likely due to migration among provinces and/or a low sampling rate for VOC (for example, genomics now indicates that B.1.1.7 was in Manitoba at least 19 days earlier than the first reported case). (Based on model fits using the Public Health Agency of Canada/McMaster model.)

Figure 5

Selective advantage of B.1.1.7 declines with increasing social restrictions in the UK. The same data as in Figure 2 but now the selective advantage of B.1.1.7, as measured by the weekly change in log(frequency SGTF/frequency of non-SGTF), is plotted against the stringency index of restrictions during that week in the UK (taken from http://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker). All data points corresponding to a frequency of less than 10% were excluded to ensure SGTF data predominantly reflect the presence of B.1.1.7.