Fitness flux in SARS-CoV-2


Trevor Bedford

Fred Hutchinson Cancer Center / Howard Hughes Medical Institute
4 Dec 2025
Science Meeting
HHMI
Slides at: bedford.io/talks

Genetic relationships of globally sampled SARS-CoV-2 from 2020 to present

Rapid displacement of existing diversity by emerging variants

Mutations in S1 domain of spike protein driving displacement

SARS-CoV-2 evolution fast relative to previous endemic viruses

This talk

  • Frequency dynamics
  • Fitness flux
  • Mutational fitness effects

Frequency dynamics

Multinomial logistic regression (MLR)

Simple haploid population genetic model is equivalent to statistical multinomial logistic regression

The frequency $x_i(t)$ of variant $i$ at time $t$ is determined by its initial frequency $p_i$ along with its fitness $f_i$ following

$$x_i(t) = \frac{p_i \, \mathrm{exp}(f_i \, t)}{\sum_j p_j \, \mathrm{exp}(f_j \, t) }$$

Multinomial logistic regression fits variant frequencies well

MLR models generate accurate short-term forecasts

30 days out, countries range from 5 to 15% mean absolute error

Fitness flux

Fitness flux is the rate of change of mean population fitness

With variant frequency $x_i(t)$ and constant variant fitness $f_i$

Mean population fitness $\bar{f}(t) = \sum_i x_i(t) \, f_i$           Fitness flux $\phi(t) = \Delta \bar{f}(t) / \Delta t$

Clade-level frequency dynamics and MLR fits in sliding windows

Constant clade fitness within each window, USA data only, ignores within-clade fitness variation

SARS-CoV-2 roughly doubled in fitness every year

Line thickness is proportional to variant frequency, 40 total variants

Traveling wave as SARS-CoV-2 population increases in fitness

Slow down of fitness flux after 2022, but still rapid evolution

Many mutations of small effect create traveling fitness waves

Richard Neher and others have analytically characterized these waves

Diffusion constant $D = \mu \, \langle \delta^2 \rangle/2$, where the average $\langle \ldots \rangle$ is over the distribution of mutational effects $K(\delta)$

Fitness variance correlates well with fitness flux

This is a specific example of Fisher's fundamental theorem

"The rate of increase in fitness of any organism at any time is equal to
its genetic variance in fitness at that time," ie $$\frac{d\bar{f}}{dt} = Var(f)$$

Mutational fitness effects

Analyze MLR fitness between parent/child lineages

Expand to 367 Pango lineages with at least 1000 sequence counts in the US from 2020 to 2025

Similar concept to Obermeyer et al

Most Pango branches have 0-1 spike mutations and change log fitness by ±0.1

Spike mutations tend to increase fitness

Non-spike mutations do not impact fitness on average

Looking across the genome shows that spike is the focus for positive selection, but accessory genes have some signal

Some attenuation of fitness effects over time

Framework to compare predictors of fitness

EvEscape combines a variational autoencoder for mutation effect + antibody accessibility + biochemical dissimilarity

EvEscape does no better than counting spike mutations

CoVFit uses spike protein embeddings from ESM-2 to predict MLR fitnesses

CoVFit performs poorly outside of training window

Note also the importance of properly assessing independent parent/child lineage deltas

Estimate fitness of circulating lineages and predict their short-term frequencies
 
Predict fitness of proximal out-of-sample lineages
 
Predict emergence and spread of out-of-sample lineages

Acknowledgements

Seasonal influenza and SARS-CoV-2 genomics: Data producers from all over the world, GISAID

Nextstrain: Richard Neher, Ivan Aksamentov, John SJ Anderson, Kim Andrews, Jennifer Chang, James Hadfield, Emma Hodcroft, John Huddleston, Jover Lee, Victor Lin, Cornelius Roemer, Thomas Sibley

MLR and fitness modeling: Marlin Figgins, Eslam Abousamra, Jover Lee, James Hadfield, John Huddleston, Philippa Steinberg, Jesse Bloom, Cornelius Roemer, Richard Neher

Bedford Lab: John Huddleston,   James Hadfield,   Katie Kistler,   Jover Lee,   Marlin Figgins,   Victor Lin,   Jennifer Chang,   Nashwa Ahmed,   Cécile Tran Kiem,   Kim Andrews,   Philippa Steinberg,   Jacob Dodds,   Amin Bemanian,   Carlos Avendano,   Aayush Verma