Trevor Bedford (@trvrb)

3 Mar 2016

Infectious Disease Epidemiology Seminar

Harvard School of Public Health

Haeckel 1879

Darwin 1859

- Antigenic drift
- Geographic circulation

with Andrew Rambaut, Marc Suchard and others

Bedford et al 2014. Integrating influenza antigenic dynamics

with molecular evolution. eLife.

with Colin Russell, Philippe Lemey and many others

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

All in collaboration with Richard Neher

- 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

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

— William Gibson

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

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

- Clade frequency change
- Antigenic advancement

growing clades have high fitness

drifted clades have high fitness

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

Model | Ep coefficient | HI coefficient | Freq error | Growth corr |
---|---|---|---|---|

Epitope only | 2.36 | -- | 0.10 | 0.57 |

HI only | -- | 2.05 | 0.08 | 0.63 |

Epitope + HI | -0.11 | 2.15 | 0.08 | 0.67 |

- 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

Rambaut 2015

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

WHO Global Influenza Surveillance Network, Richard Neher (Max Planck Tübingen), Andrew Rambaut (University of Edinburgh), Colin Russell (Cambridge University), Philipe Lemey (KU Leuven), Marc Suchard (UCLA), Steven Riley (Imperial College), Gytis Dudas (University of Edinburgh).

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