### Human seasonal influenza viruses

slide by Trevor Bedford
- Influenza viruses evolve to avoid human immunity
- Vaccines need frequent updates

### GISRS and GISAID -- Influenza virus surveillance

- comprehensive coverage of the world
- timely sharing of data through GISAID -- often within 2-3 weeks of sampling
- hundreds of sequences per week (in peak months)

##### → requires continuous analysis and easy dissemination

##### → interpretable and intuitive visualization

## Beyond tracking: can we predict?

### Fitness variation in rapidly adapting populations

- Speed of adaptation is logarithmic in population size
- Environment (fitness landscape), not mutation supply, determines adaptation
- 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
### Simple heuristic: Local branching index

### Validate on simulation data

- simulate evolution
- sample sequences
- reconstruct trees
- infer fitness
- predict ancestor of future
- compare to truth

RN, Russell, Shraiman, eLife, 2014
### Validation on simulated data

RN, Russell, Shraiman, eLife, 2014
### Validation on simulated data

RN, Russell, Shraiman, eLife, 2014
### Prediction of the dominating H3N2 influenza strain

- no influenza specific input
- how can the model be improved? (see model by Luksza & Laessig)
- what other context might this apply?

RN, Russell, Shraiman, eLife, 2014
### Limits of predictability

Barrat-Charlaix et al, 2020
### Limits of predictability

Barrat-Charlaix et al, 2020
### Limits of predictability

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

- Boris Shraiman
- Colin Russell
- Trevor Bedford
- Pierre Barrat
- Oskar Hallatschek

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