Trevor Bedford (@trvrb)

25 May 2017

Immunology and Evolution of Influenza Symposium

Emory University

Real-time updates as new information rolls in

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

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 clade frequencies
- Infer antigenic phenotypes
- Export for visualization

Huddleston 2016

Huddleston 2016

"The future is here, it's just not evenly

— William Gibson

distributed yet"

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

- Amino acid changes at epitope sites
- Antigenic novelty based on HI
- Rapid phylogenetic growth

drifted clades have high fitness

growing clades have high fitness

where the fitness $f$ of virus $i$ is estimated as

$$\hat{f}_i = \beta^\mathrm{HI} \, f_i^\mathrm{HI} + \beta^\mathrm{freq} \, f_i^\mathrm{freq}$$

where $f_i^\mathrm{HI}$ measures antigenic drift via HI and $f_i^\mathrm{freq}$ measures clade growth/decline

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 |

Emerging clades are difficult to forecast: little antigenic data and little evidence of "past performance"

Models work well for clades at >10%, but less well for clades <5%

Models can project forward from circulating strains, but cannot foresee the appearance of new mutations

Intrinsically limits the timescale of forecasting to ~1 year

Requires rapid shipping of samples, rapid sequencing and rapid antigenic characterization

- Extend to other seasonal viruses
- Forecast NA evolution
- Integrate neutralization (FRA) assay data
- Model effects of egg adaptation
- Incorporate an explicit geographic model

Analysis: Richard Neher, Colin Russell, Charlton Callender, John Huddleston, Gytis Dudas, Colin Megill, Andrew Rambaut, Charles Cheung, Marc Suchard, Steven Riley, Philippe Lemey, Boris Shraiman, Marta Łuksza, Michael Lässig

GISRS: Ian Barr, Shobha Broor, Mandeep Chadha, Nancy Cox, Rod Daniels, Becky Garten, Palani Gunasekaran, Aeron Hurt, Anne Kelso, Jackie Katz, Nicola Lewis, Xiyan Li, John McCauley, Takato Odagiri, Varsha Potdar, Yuelong Shu, Eugene Skepner, Masato Tashiro, Dayan Wang, Dave Wentworth, Xiyan Xu