SARS-CoV-2 evolution in light of Omicron
Trevor Bedford (@trvrb)
Fred Hutchinson Cancer Research Center / Howard Hughes Medical Institute
15 Mar 2022
Viruses and Vaccines Seminar Series
Brotman Baty Institute
Slides at: bedford.io/talks
1. Emergence of variants of concern
2. Assessing adaptive evolution
3. Variant transmission dynamics
4. Omicron emergence and spread
5. Future perspectives
Emergence of variants of concern
Over 9.2M SARS-CoV-2 genomes shared to GISAID and evolution tracked in real-time at nextstrain.org
Richard Neher,
Emma Hodcroft,
James Hadfield,
Thomas Sibley,
John Huddleston,
Ivan Aksamentov,
Moira Zuber,
Jover Lee,
Cassia Wagner,
Denisse Sequeira,
Cornelius Roemer,
Victor Lin,
Jennifer Chang
SARS-CoV-2 lineages establish globally in February and March
Limited early mutations like spike D614G spread globally during initial wave
Spike protein is critical for cell invasion by the virus and is the primary target for human immune response
Repeated emergence of 484K and 501Y across the world
Emergence of Alpha (B.1.1.7) in the UK
Emergence of Beta (B.1.351) in the South Africa
Emergence of Gamma (P.1) in the Brazil
Spread of VOC / VOI lineages across the world
Delta outcompeting other variants and seemed poised to sweep
Variants show excess mutations across the genome
But show most substantial excess in the S1 domain of spike
Assessing adaptive evolution
Rapid and parallel adaptive mutations in spike S1 drive clade success in SARS-CoV-2
Katie Kistler,
John Huddleston
Phylogeny of 10k genomes equitably sampled in space and time
Measure clade growth as a proxy for viral fitness
Clades with more S1 nonsynonymous mutations grow faster
S1 is quickly evolving and highly correlated with clade growth
dN/dS to root further highlights adaptive evolution
Rapid pace of adaptive evolution relative to H3N2 influenza
Mutations in S1 arising via within-host pressures result increase viral fitness and are enriched in the viral population by natural selection
Although selection has not been primarily for antigenic drift, observed level of adaptability suggests its potential
Variant transmission dynamics
Multiple approaches to modeling fitness differences between circulating variants
Multinomial logistic regression models work well on frequencies
However, frequency of a variant may rise while cases fall
SARS-CoV-2 variant dynamics across US states show consistent differences in transmission rates
Marlin Figgins
Estimation of variant-specific Rt through time using state-level data
State-level case counts are partitioned based on frequencies of sequenced cases
Differences in intrinsic Rt across variants, but all trending downwards
Consistent differences in variant-specific transmission rate across states
Future work would ideally tie together granular empirical estimates of viral fitness from frequency data together with mutational and phenotypic predictors to learn what is driving variant success
Omicron emergence and spread
Genetic relationships of SARS-CoV-2 sampled up to Mar 2022
Rapid displacement of existing diversity by Omicron
Omicron viruses possess huge excess of mutations in S1
Primary hypotheses for Omicron's origin
-
(More likely) Evolution during chronic infection in an immunocompromised individual
- Supported by large excess of S1 mutations, but paucity of non-spike mutations
- Hard to explain simultaneous BA.1, BA.2 and BA.3 sublineages otherwise
-
(Less likely) Human to animal spillover mid-2020 and spillback in late 2021
- Mutational spectra and particular mutations such as 493R and 498R suggest rodent intermediary (Wei et al)
S1 evolved at a rate of 12 amino acid changes per year in 2021
This is remarkably fast relative to seasonal influenza
Omicron spike mutations substantially drop VE against infection
Significant immune escape drove large-scale epidemics
Starting Rt approached that of the initial wave in spring 2020
Omicron attack rate
- We estimate US has seen 9.8% of the population as confirmed cases of Omicron through Mar 3, with the large majority accumulating after Dec 15
- Assuming a case detection rate of 1 in 5 infections, we estimate almost 50% of the US infected with Omicron, most in the span of ~10 weeks
Similar epidemics across US states with downturn driven by immunity
Omicron BA.2 has been steadily displacing BA.1 across the world
BA.2 shows a transmission advantage of ~30% over BA.1
Reduced intrinsic severity relative to other VOC viruses
Immunity and reduced intrinsic severity has dropped CFR
Future perspectives
SARS-CoV-2 will continue to evolve to escape population immunity, though with multiple potential avenues
With 1 observation in ~2.3 years of virus evolution, it's currently unclear how common Omicron-like events will be
This rate distribution gives a naive prediction of Omicron-like emergence events occurring in the rest of 2022
Likely scenarios over the next 12 months
- Evolution within Omicron BA.2 to further increase intrinsic transmission and to escape from Omicron-derived immunity. This scenario sees lower attack rates with 2022-2023 epidemic driven by drift + waning + seasonality.
- Another Omicron-like emergence event in which a chronic infection initiated in ~2021 incubates a new wildly divergent virus. This scenario sees high attack rates with epidemic driven by variant emergence.
Broad expectations of endemicity based on comparison with flu
- R0 of Delta/Omicron is perhaps ~6 compared to R0 of flu of ~2. At same rates of evolution and waning expect more COVID circulation.
- Before Omicron rates of evolution in SARS-CoV-2 S1 had been roughly equal to H3N2 flu. Omicron equivalent to 5 years of H3N2 evolution.
- Infection fatality rate (IFR) of COVID is roughly comparable to flu once you have prior immunity.
1, 2 and 3 suggest a virus that circulates at higher levels than flu, but individual infections aren't much more severe in terms of mortality than H3N2 influenza.
Seasonality suggests winter "COVID season", but Omicron-like events could overcome seasonality.
Key parameters are seasonal attack rate and IFR
- Assume seasonal attack rate of 40%, with Omicron-like emergence driving 80% some years.
- Assume IFR of 0.05% (compared to early pandemic IFR of 0.5%), but new variant could push this higher.
- This would give somewhere around 60K yearly deaths at endemicity, but potential for variant emergence to push this over 100K.
Acknowledgements
SARS-CoV-2 genomic epi: Data producers from all over the world, GISAID and the Nextstrain team
Bedford Lab:
John Huddleston,
James Hadfield,
Katie Kistler,
Louise Moncla,
Maya Lewinsohn,
Thomas Sibley,
Jover Lee,
Cassia Wagner,
Miguel Paredes,
Nicola Müller,
Marlin Figgins,
Denisse Sequeira,
Victor Lin,
Jennifer Chang
