Global circulation and evolution of influenza virus


Trevor Bedford (@trvrb)
5 Mar 2018
NIHE/OUCRU Workshop on Influenza Epidemiology and Evolution in Vietnam

Population turnover (in H3N2) is extremely rapid

Clades emerge, die out and take over

Clades show rapid turnover

Dynamics driven by antigenic drift

Drift necessitates vaccine updates

H3N2 vaccine updates occur every ~2 years

Timely surveillance and rapid analysis essential to vaccine strain selection

nextflu

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

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 clade frequencies
  7. Infer antigenic phenotypes
  8. Export for visualization

Up-to-date analysis publicly available at:

nextflu.org

Competing clades central to influenza evolutionary dynamics

Clades grow and decline through time

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

Łuksza and Lässig 2014. Nature.

Construct a fitness model based on:


  1. Amino acid changes at epitope sites
  2. Antigenic novelty based on HI
  3. Rapid phylogenetic growth

Clade growth rate is well predicted (ρ = 0.66)

Trajectories show more detailed congruence

Trajectories show more detailed congruence

Geographic circulation drives evolutionary outcomes

Clades commonly show region-specific circulation patterns

Phylogeny of H3 with geographic history

Bedford et al. 2015. Nature.

Infer geographic transition matrix

Bedford et al. 2015. Nature.

Geographic location of phylogeny trunk

Bedford et al. 2015. Nature.

Region-specific ancestry

Bedford et al. 2015. Nature.

Which regions are best predictive of future outcomes?

Correlation between clade frequencies

Correlations across regions

Correlations across regions with 6-month lag

Correlations across regions with 12-month lag

Goal is to include migration forcings in predictive fitness model

Acknowledgements

Bedford Lab: Alli Black, Sidney Bell, Gytis Dudas, John Huddleston,
Barney Potter, James Hadfield, Louise Moncla

Influenza: WHO Global Influenza Surveillance Network, GISAID, Richard Neher, Colin Russell, Andrew Rambaut, Gytis Dudas, Dave Wentworth, Becky Garten, Marta Łuksza, Michael Lässig

Nextflu/Nextstrain: Richard Neher, James Hadfield, Colin Megill, Sidney Bell, Barney Potter, John Huddleston, Charlton Callender, Emma Hodcroft