Evolutionary dynamics of SARS-CoV-2

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

We really should be at something like Psi at this point

SARS-CoV-2 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

Strength of adaptive evolution consistently high through time

Mutations at spike S1 propel escape from population immunity

These mutations are accumulating just as quickly post-Omicron

And are accruing much more rapidly than other endemic viruses

Variant frequency dynamics

Population genetic expectation of variant frequency under selection

$x' = \frac{x \, (1+s)}{x \, (1+s) + (1-x)}$ for frequency $x$ over one generation with selective advantage $s$

$x(t) = \frac{x_0 \, (1+s)^t}{x_0 \, (1+s)^t + (1-x_0)}$ 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 post-Omicron

ONS Infection Survey provides rare source of ground truth

Roughly 1 in 3 infections detected in 2021, while 1 in 40 in 2023

~110% population attack rate from March 2022 to March 2023

~27k deaths in ~61M infections yields IFR of 0.04%

Forecasting

Assessing MLR models for short-term frequency forecasting

MLR models generate accurate short-term forecasts

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 lineage-level MLR growth advantage

Continued evolution

How likely are further "Omicron-like" events?

With 1 observation in 3.6 years of virus evolution, it's currently unclear how common Omicron-like events will be

This rate distribution gives a naive prediction of ~23% probability of an Omicron-like 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

Acknowledgements

John J. Holland Lectureship

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,   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