### Human seasonal influenza viruses

slide by Trevor Bedford
### Genomic analysis to reconstruct pathogen spread and evolution

**Link genotypic and phenotypic changes**: immune escape, drug resistance, host adaptation

- Influenza viruses evolve to avoid human immunity
- Vaccines need frequent updates

### Vaccine strain selection schedule

Klingen and McHardy, Trends in Microbiology
## Beyond tracking: can we predict?

## Can we pick a "winner"?

### Traditional approach: evolutionary race of viruses

- Speed of adaptation is logarithmic in population size
- Single mutations are easy to find, many such mutation are needed for success
- Different models have universal emerging properties

RN, Annual Reviews, 2013; Desai & Fisher; Brunet & Derride; Kessler & Levine

#### Neutral/Kingman coalescent

#### strong selection

#### Bolthausen-Sznitman Coalescent

RN, Hallatschek, PNAS, 2013; see also Brunet and Derrida, PRE, 2007; Desai, Walczak, Fisher, Genetics, 2013
## Burst in the tree ↔ high fitness

### Predicting evolution

### Given the branching pattern:

- can we predict fitness?
- pick the closest relative of the future?

RN, Russell, Shraiman, eLife, 2014
### Fitness inference from trees

$$P(\mathbf{x}|T) = \frac{1}{Z(T)} p_0(x_0) \prod_{i=0}^{n_{int}} g(x_{i_1}, t_{i_1}| x_i, t_i)g(x_{i_2}, t_{i_2}| x_i, t_i)$$

RN, Russell, Shraiman, eLife, 2014
### Predicting an optimal representative

RN, Russell, Shraiman, eLife, 2014
### Moderate prediction success

- But is a random strain for previous years a sensible null?
- Does this work for the right reason?

RN, Russell, Shraiman, eLife, 2014
### Predicting the distribution in sequence space

Huddleston et al, eLife, 2020
### Follow up investigations show very modest predictability

- Optimal transport distances between prediction and observation -- less than one amino acid gained

Huddleston et al, eLife, 2020
### Do A/H3N2 mutations have inertia?

Barrat-Charlaix et al, 2020
### Complicated "Ecology" of host and pathogen

- Current approaches focus on the virus population
- Models are predicated on identifying a 'winner'
- Instead, dynamics might be slaved to host immunity:

→ exposure history and waning determine population immunity, viruses fill whatever niche there is

- For prediction, host data would be at least as important as viral dynamics
- Prediction horizon is limited to within season dynamics that equilibrates the host-pathogen immunity landscape
- Viral adaptations are niche specific and loose their benefit after equilibration.

Barrat-Charlaix et al, 2020
### Influenza and Theory acknowledgments

- Boris Shraiman
- Colin Russell
- Trevor Bedford
- Pierre Barrat
- John Huddleston

- All the NICs and WHO CCs that provide influenza sequence data
- The WHO CCs in London and Atlanta for providing titer data

### Acknowledgments

#### Trevor Bedford and his lab -- terrific collaboration since 2014

#### especially James Hadfield, Emma Hodcroft, Ivan Aksamentov, Cornelius Roemer, Moira Zuber, and John Huddleston

#### Data we analyze are contributed by scientists from all over the world

#### Data are shared and curated by GISAID

####

### Fixation probability is quasi-neutral

##### A/H3N2 influenza

##### Simulations with increasing interference

Barrat-Charlaix et al, 2020