Real-time tracking of virus evolution


Trevor Bedford (@trvrb)
19 Apr 2016
IDM Symposium
Institute for Disease Modeling

Slides at bedford.io/talks/

Sequencing to reconstruct pathogen spread

Epidemic process

Sample some individuals

Sequence and determine phylogeny

Sequence and determine phylogeny

Localized Middle Eastern MERS-CoV phylogeny

Regional West African Ebola phylogeny

Global influenza phylogeny

Applications of evolutionary analysis for vaccine strain selection and charting outbreak spread

Influenza

Influenza H3N2 vaccine updates

H3N2 phylogeny showing antigenic drift

H3N2 phylogeny showing antigenic drift

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

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

We implement a model based on two predictors


  1. Clade frequency change
  2. Antigenic advancement

Clade frequency trajectories show congruence

Clade growth rate is well correlated (ρ = 0.66)

Growth vs decline correct in 84% of cases

Real-time analyses are actionable and may inform influenza vaccine strain selection

Outbreak analysis

Ebola

Early sequencing showed single origin of epidemic

Continued spread through Dec 2014

At epidemic height, geographic spread of particular interest

Rambaut 2015

Later on, tracking transmission clusters of primary importance

Evolutionary analyses helped to establish the degree of adaptive evolution occurring

Phylogeographic analyses reveal detailed patterns of spatial movement

Dudas et al 2016

Important analyses, let's make them more rapid and more automated

Tracking epidemic spread in real-time:

ebola.nextstrain.org

Zika

Virus endemic to Africa, emergence in Southeast Asia in the last century

Spread eastward through the South Pacific

Isolated epidemics in the South Pacific

Single arrival into the Americas in early 2014

Working on analysis of ongoing evolution:

nextstrain.org/zika/

Moving forward, genetically-informed outbreak response requires:


  • Rapid sharing of sequence data, genetic context critical
  • Technologies to rapidly conduct phylogenetic inference
  • Technologies to explore genetic relationships and inform epidemiological investigation

Future work

Acknowledgements


Influenza: WHO Global Influenza Surveillance Network, Worldwide Influenza Centre at the Francis Crick Institute, Richard Neher, Colin Russell, Andrew Rambaut

Ebola: data producers, Gytis Dudas, Andrew Rambaut, Philipe Lemey, Richard Neher, Nick Loman, Ian Goodfellow, Paul Kellam, Danny Park, Kristian Andersen, Pardis Sabeti

Zika: data producers, Nuno Faria, Andrew Rambaut, Richard Neher, Charlton Callender

 

Contact


  • Website: bedford.io
  • Twitter: @trvrb
  • Slides: bedford.io/talks/real-time-tracking-idmod/