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)
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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, GISAID, 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/