Improving seasonal influenza vaccines by predicting virus evolution

Richard Neher
Biozentrum, University of Basel

slides at

Evolution of RNA viruses

HI virus, source wikipedia
  • Constant struggle to adapt to changing environments and host immunity
  • Mutation rates of about 0.00001/site and replication
  • Large viral populations explore every single mutation every day
  • Most mutations are detrimental, but some persist
  • Mutations can act like an approximate clock
Phylogenetic analysis
Phylogenetic analysis

Sequences record the spread of pathogens

The resolution is limited by the number of mutations!
images by Trevor Bedford

Human seasonal influenza viruses

slide by Trevor Bedford

  • Influenza virus evolves to avoid human immunity
  • Vaccines need frequent updates

joint work with Trevor Bedford & his lab

based on sequences and phenotype provided by the WHO CCs and NICs

Beyond tracking: can we predict?

Competition between different viral variants

RN, Annual Reviews, 2013; Desai & Fisher; Brunet & Derride; Kessler & Levine

Predicting evolution

Given the branching pattern:

  • can we predict fitness?
  • pick the closest relative of the future?
RN, Russell, Shraiman, eLife, 2014

Fitness inference from trees

$$P(\mathbf{x}|T) = \frac{1}{Z(T)} p_0(x_0) \prod_{i=0}^{n_{int}} g(x_{i_1}, t_{i_1}| x_i, t_i)g(x_{i_2}, t_{i_2}| x_i, t_i)$$
RN, Russell, Shraiman, eLife, 2014

Prediction of the dominating H3N2 influenza strain

  • no influenza specific input
  • how can the model be improved? (see model by Luksza & Laessig)
  • what other context might this apply?
RN, Russell, Shraiman, eLife, 2014

  • integrate data from many different sources
  • analyze those data in near real time
  • disseminate results in an intuitive yet informative way
  • provide actionable insights

Influenza and Theory acknowledgments

  • Boris Shraiman
  • Colin Russell
  • Trevor Bedford
  • Oskar Hallatschek

  • Trevor Bedford
  • Colin Megill
  • Pavel Sagulenko
  • Sidney Bell
  • James Hadfield
  • Wei Ding