Evolutionary dynamics of SARSCoV2
Trevor Bedford
Fred Hutchinson Cancer Center / Howard Hughes Medical Institute
24 Jun 2023
John J. Holland Lecture
Symposium on Understanding How Viruses Spread and Evolve
ASV Annual Meeting
Slides at: bedford.io/talks
Population immunity has driven a dramatic shift in death toll
But this immunity is now driving evolution of the virus
In which new variants emerge that escape from existing population immunity and spread rapidly
Novel variants sweep globally in months rather than years
SARSCoV2
Influenza H3N2
We really should be at something like Psi at this point
1. SARSCoV2 genome evolution
2. Variant frequency dynamics
3. Evolution driving epidemics
4. Forecasting
5. Continued evolution
SARSCoV2 genome evolution
Phylogeny of 2k viruses sampled globally in June and July 2022
Measure clade growth as a proxy for viral fitness
Across 2020 to 2023, clades with more S1 mutations grow faster
Rate of amino acid evolution and correlation between mutation and clade growth strongest in S1
Strength of adaptive evolution consistently high through time
Mutations at spike S1 propel escape from population immunity
These mutations are accumulating just as quickly postOmicron
And are accruing much more rapidly than other endemic viruses
Variant frequency dynamics
Population genetic expectation of variant frequency under selection
$x' = \frac{x \, (1+s)}{x \, (1+s) + (1x)}$ for frequency $x$ over one generation with selective advantage $s$
$x(t) = \frac{x_0 \, (1+s)^t}{x_0 \, (1+s)^t + (1x_0)}$ for initial frequency $x_0$ over $t$ generations
Trajectories are linear once logit transformed via $\mathrm{log}(\frac{x}{1  x})$
Variants show consistent frequency dynamics in logit space
Variants show consistent frequency dynamics in logit space
Multinomial logistic regression
Multinomial logistic regression across $n$ variants models the probability of a virus sampled at time $t$ belonging to
variant $i$ as
$$\mathrm{Pr}(X = i) = x_i(t) = \frac{p_i \, \mathrm{exp}(f_i \, t)}{\sum_{1 \le j \le n} p_j \, \mathrm{exp}(f_j \, t) }$$
with $2n$ parameters consisting of $p_i$ the frequency of variant $i$ at initial timepoint
and $f_i$ the growth rate or fitness of variant $i$.
Variant frequencies across countries from Feb 2022 to present
We find that recent variants like XBB.1.5 are ~300% fitter than original Omicron BA.1
Evolution driving epidemics
Many fewer reported cases in England postOmicron
ONS Infection Survey provides rare source of ground truth
Roughly 1 in 3 infections detected in 2021, while 1 in 40 in 2023
Partitioning ONS incidence based on sequencing data shows variantdriven epidemics
~110% population attack rate from March 2022 to March 2023
~27k deaths in ~61M infections yields IFR of 0.04%
Assessing MLR models for shortterm frequency forecasting
MLR models generate accurate shortterm forecasts
Now have clade and lineage forecasts continuously updated
Multinomial logistic regression should work well for SARSCoV2 prediction, except new variants have been emerging
fast enough that the prediction horizon is really quite short
Could we predict the spread of new mutations using DMS data?
Escape from antibodies that potently neutralize BA.2
Can calculate escape of arbitrary RBD against antibodies known to neutralize BA.2
Strong correlation between DMS immune escape and lineagelevel MLR growth advantage
How likely are further "Omicronlike" events?
With 1 observation in 3.6 years of virus evolution, it's currently unclear how common Omicronlike events will be
This rate distribution gives a naive prediction of ~23% probability of an Omicronlike event occurring in the next 12 months
1 in 4 seems high, but "cryptic lineages" are commonly being found in wastewater
Even without further "saltational" events, ongoing evolution is rapid and so far shows little evidence of slowing
This pace of evolution contributes to large burden of disease at endemicity

Assume yearly attack rate of ~50% (even without the potential for Omicronlike emergence this may be an underestimate)

Assume IFR of 0.05% (similar to influenza, compare to early pandemic IFR of 0.5%)

This would give somewhere around 80K yearly deaths in the US at endemicity

For comparison, this year the US has seen 40k COVID19 deaths from Jan 1 to Jun 10, which would extrapolate to 90k deaths in 2023
Research priorities to deal with this rapid evolution

Continued genomic surveillance and evolutionary forecasting

Development of new antivirals

Vaccination strategies to combat antigenic evolution and OAS
 Universal vaccines?
 Cocktail vaccines?
 Masking epitope sites to reduce imprinted response?
Acknowledgements
John J. Holland Lectureship
SARSCoV2 genomic epi: Data producers from all over the world, GISAID and the Nextstrain team
Bedford Lab:
John Huddleston,
James Hadfield,
Katie Kistler,
Thomas Sibley,
Jover Lee,
Cassia Wagner,
Miguel Paredes,
Nicola Müller,
Marlin Figgins,
Victor Lin,
Jennifer Chang,
Allison Li,
Eslam Abousamra,
Donna Modrell,
Nashwa Ahmed,
Cécile Tran Kiem