Real-time tracking of virus evolution


Trevor Bedford (@trvrb)
23 May 2016
MIDAS Network Meeting
Washington, DC

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

Vaccine strain selection timeline

Fitness models can project strain dynamics


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

Predicted clade frequency trajectories show congruence

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

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

Tracking epidemic spread in real-time:

ebola.nextstrain.org

Rapid on-the-ground sequencing by Ian Goodfellow, Matt Cotten and colleagues













Deployment of MinION sequencing to Guinea by Nick Loman, Josh Quick, Lauren Cowley and colleagues

Zika

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

Spread eastward through the South Pacific

Single arrival into the Americas in early 2014

Working on analysis of ongoing evolution:

nextstrain.org/zika/

Discussion points for breakout session

Discussion points


  • Mechanisms to promote data sharing (genetic data, epi data) and proper scientific attribution
  • Key insights from phylodynamic analyses for understanding outbreak spread and aiding epidemiological investigation
  • Interface of modeling results with public health policy

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, Matt Cotten, Paul Kellam, Danny Park, Kristian Andersen, Pardis Sabeti

Zika: data producers, Nick Loman, Nuno Faria, Oliver Pybus, Andrew Rambaut, Richard Neher, Charlton Callender

   

Contact


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

Discussion points for breakout session

Options for genetic data sharing

Incentives for genetic data sharing


Statements of support from major journals and from the WHO

Confusion over behavioral norms


Mlakar et al. NEJM                                           Faria et al. Science