# Tracking and forecasting influenza virus evolution

Trevor Bedford (@trvrb)

29 Aug 2019

Options X

Singapore

# Tracking seasonal influenza virus evolution

### Population turnover of A/H3N2 influenza is extremely rapid

#
Nextflu

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

All in collaboration with Richard Neher

#
Nextflu
→
Nextstrain

Real-time tracking of pathogen evolution

*
with
Richard Neher,
James Hadfield,
Emma Hodcroft,
Thomas Sibley,
Colin Megill,
John Huddleston,
Barney Potter,
Sidney Bell,
Louise Moncla,
Charlton Callender,
Misja Ilcisin,
Kairsten Fay,
Jover Lee
*

### Current view of H3N2 from nextstrain.org/flu

## Forecasting has been a goal for a while

### At Options IX in 2016, I stated:

We predict the 171K clade will continue to be successful (unless supplanted by a novel mutant)

### Instead, momentum behind 171K (clade 3c2.A1) waned and co-circulation has increased

## What's going on with this co-circulation?

### Looking historically shows this to be rare

### Variation in both diversity and rate of HI drift

### Possible correlation of increased diversity when drift is slow

## Risk of eventual "speciation"

# Forecasting strain turnover

*
with John Huddleston
and Richard Neher
*

### Fitness models project strain frequencies

Future frequency $x_i(t+\Delta t)$ of strain $i$ derives from strain fitness $f_i$ and present day frequency $x_i(t)$, such that

$$\hat{x}_i(t+\Delta t) = x_i(t) \, \mathrm{exp}(f_i \, \Delta t)$$

Total strain frequencies at each timepoint are normalized.
This captures clonal interference between competing lineages.

### Two inputs

- Estimate of present-day strain frequencies $x(t)$
- Estimate of present-day strain fitnesses $f$

### Strain frequency estimated via region-weighted KDE

### Strain fitness estimated from viral attributes

The fitness $f$ of strain $i$ is estimated as

$$\hat{f}_i = \beta^\mathrm{A} \, f_i^\mathrm{A} + \beta^\mathrm{B} \, f_i^\mathrm{B} + \ldots$$

where $f^A$, $f^B$, etc... are different standardized viral attributes and
$\beta^A$, $\beta^B$, etc... coefficients are trained based on historical evolution

Antigenic drift |
Intrinsic fitness |
Recent growth |

epitope mutations |
non-epitope mutations |
local branching index |

HI titers |
DMS data (via Bloom lab) |
delta frequency |

### Future population depends on frequency and fitness

### Forecast assessed based on weighted distance match to observed future population

### Forecast assessed based on weighted distance match to observed future population

### Train in 6-year sliding windows from 1995 to 2015 with most recent years held out as test

### Within-category performance favors HI drift, non-epitope fitness and local branching index

### Composite model relies on local branching index and non-epitope fitness

### Model successfully predicts clade growth

### Best pick from model is generally close to best possible retrospective pick

### Forecast from current virus population

### Since March these two clades have begun to spread throughout the world

### Predicted sequence match of circulating strains to future population

### Goal is to start rolling these forecasts out live to nextstrain.org/flu shortly

### Future work

- Better predictors for antigenic drift
- Incorporate NA evolution
- Incorporate geography
- Forecast H1N1pdm, Vic and Yam

### Acknowledgements

**Bedford Lab**:
Alli Black,
John Huddleston,
Barney Potter,
James Hadfield,
Katie Kistler,
Louise Moncla,
Maya Lewinsohn,
Thomas Sibley,
Jover Lee,
Kairsten Fay,
Misja Ilcisin

**This work**: WHO Global Influenza Surveillance Network, John Huddleston,
Richard Neher, Barney Potter, James Hadfield, Dave Wentworth, Becky Garten, Marta Łuksza,
Michael Lässig, Richard Reeve, Jackie Katz, Colin Russell, John McCauley, Rod Daniels,
Kanta Subbarao, Ian Barr, Aeron Hurt, Tomoko Kuwahara, Takato Odagiri

### Contact

- Lab website: bedford.io
- Flu tracking: nextstrain.org
- Twitter: @trvrb
- Slides: bedford.io/talks/flu-forecasting-options-2019/