## Real-time evolutionary forecasting for influenza vaccine strain selection

Trevor Bedford (@trvrb)

2 Dec 2015

Epidemics^{5}

Clearwater Beach, FL

### Slides at bedford.io/talks/

### Influenza H3N2 vaccine updates

### H3N2 phylogeny showing antigenic drift

### H3N2 phylogeny showing antigenic drift

### Drift variants rapidly take over the virus population

### Timely surveillance and rapid analysis essential to understand ongoing influenza evolution

##
nextflu

Project to provide a real-time view of the evolving influenza population

All in collaboration with Richard Neher

###
nextflu
pipeline

- Download all recent HA sequences from GISAID
- Filter to remove outliers
- Subsample across time and space
- Align sequences
- Build tree
- Estimate frequencies
- Export for visualization

### Up-to-date analysis publicly available at:

The future is here, it's just not evenly distributed yet

— William Gibson

### USA music industry, 2011 dollars per capita

### Influenza population turnover

### Vaccine strain selection timeline

### Seek to explain change in clade frequencies over 1 year

### Fitness models can project clade frequencies

Clade frequencies $X$ derive from the fitnesses $f$ and frequencies $x$ of constituent viruses, such that

$$\hat{X}_v(t+\Delta t) = \sum_{i:v} x_i(t) \, \mathrm{exp}(f_i \, \Delta t)$$

This captures clonal interference between competing lineages

### Predictive fitness models

A simple predictive model estimates the fitness $f$ of virus $i$ as

$$\hat{f}_i = \beta^\mathrm{ep} \, f_i^\mathrm{ep} + \beta^\mathrm{ne} \, f_i^\mathrm{ne}$$

where $f_i^\mathrm{ep}$ measures cross-immunity via substitutions at epitope sites and $f_i^\mathrm{ep}$ measures mutational load via substitutions at non-epitope sites

### We implement a similar model based on two predictors

- Clade frequency change
- Antigenic advancement

### Project frequencies forward,

growing clades have high fitness

### Calculate HI drop from ancestor,

drifted clades have high fitness

### Fitness model parameterization

Our predictive model estimates the fitness $f$ of virus $i$ as

$$\hat{f}_i = \beta^\mathrm{freq} \, f_i^\mathrm{freq} + \beta^\mathrm{HI} \, f_i^\mathrm{HI}$$

We learn coefficients and validate model based on previous 15 H3N2 seasons

### Clade growth and decline is well predicted

### Correct in 81% of cases

### Further work on predictive modeling

- Integrate data predictors and data sources, e.g. plan to investigate a geographic predictor
- Possible to build predictive models for H1N1 and B and to forecast NA evolution

### Evolutionary analyses can inform influenza vaccine strain selection

Analyses must be rapid and widely available

Predictive models can flag clades for experimental follow-up and creation of vaccine candidates

### Acknowledgements

WHO Global Influenza Surveillance Network, GISAID,
Richard Neher, Andrew Rambaut,
John McCauley, Rod Daniels, Ian Barr, Masato Tashiro,
Colin Russell, Sebastian Maurer-Stroh,
Michael Lässig, Marta Łuksza

### Contact

- Website: bedford.io
- Twitter: @trvrb
- Slides: bedford.io/talks/real-time-forecasting-epidemics/