Real-time evolutionary forecasting for influenza vaccine strain selection


Trevor Bedford (@trvrb)
2 Dec 2015
Epidemics5
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

  1. Download all recent HA sequences from GISAID
  2. Filter to remove outliers
  3. Subsample across time and space
  4. Align sequences
  5. Build tree
  6. Estimate frequencies
  7. Export for visualization

Up-to-date analysis publicly available at:

nextflu.org

Forecasting

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


  1. Clade frequency change
  2. 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

Prototype up at

dev.nextflu.org

Further work on predictive modeling


  1. Integrate data predictors and data sources, e.g. plan to investigate a geographic predictor
  2. 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, 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/