Tracking and predicting viral evolution, from flu to covid-19

Richard Neher
Biozentrum & SIB, University of Basel

slides at

Human seasonal influenza viruses

slide by Trevor Bedford

Positive tests for influenza in the USA by week

Data by the US CDC

Sequences record the spread of pathogens

Mutations accumulate at a rate of $10^{-5}$ per site and day!
images by Trevor Bedford

  • Influenza viruses evolve to avoid human immunity
  • Vaccines need frequent updates

Influenza B viruses have split into two lineages

Le Yan, RN, Shraiman, bioRxiv, 2018

GISRS and GISAID -- Influenza virus surveillance

  • comprehensive coverage of the world
  • timely sharing of data through GISAID -- often within 2-3 weeks of sampling
  • hundreds of sequences per week (in peak months)
→ requires continuous analysis and easy dissemination
→ interpretable and intuitive visualization

joint project with Trevor Bedford & his lab

Beyond tracking: can we predict?

Fitness variation in rapidly adapting populations

  • Speed of adaptation is logarithmic in population size
  • Environment (fitness landscape), not mutation supply, determines adaptation
  • Different models have universal emerging properties
RN, Annual Reviews, 2013; Desai & Fisher; Brunet & Derride; Kessler & Levine

Neutral/Kingman coalescent

strong selection

Bolthausen-Sznitman Coalescent

RN, Hallatschek, PNAS, 2013; see also Brunet and Derrida, PRE, 2007; Desai, Walczak, Fisher, Genetics, 2013

Burst in the tree ↔ high fitness

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

Simple heuristic: Local branching index

Validate on simulation data

  • simulate evolution
  • sample sequences
  • reconstruct trees
  • infer fitness
  • predict ancestor of future
  • compare to truth
RN, Russell, Shraiman, eLife, 2014

Validation on simulated data

RN, Russell, Shraiman, eLife, 2014

Validation on simulated data

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

Limits of predictability

Barrat-Charlaix et al, 2020

Limits of predictability

Barrat-Charlaix et al, 2020

Limits of predictability

Barrat-Charlaix et al, 2020
Data summarized by Ian MacKay
Data summarized by Ian MacKay
Data summarized by Ian MacKay
Data summarized by Ian MacKay
Data summarized by Ian MacKay
by Trevor Bedford
by Trevor Bedford

Tracking diversity and spread of SARS-CoV-2 in Nextstrain

Available data on Jan 26

Early genomes differed by only a few mutations, suggesting very recent emergence
→ the closest to "real-time" we have experienced so far...
Figure by James Hadfield/Emma Hodcroft
Mutations accumulate constantly, but most of them are irrelevant and rare.
The genome accumulates about two mutations a month...
Diversified into multiple global variants. Groups 20A/B/C have taken over.

Genomic analysis as complement to contact tracing

Swiss sequences on August 31, most sequencing by Tanja Stadler's group at ETH, D-BSSE

A European cluster in summer 2020

What's next?

What have we learned? What are the unknowns?

  • Social distancing is very effective
    → we can suppress the outbreak if we want!
  • Many Asian and European countries have largely re-opened
    → some now see consistent rise in cases
    → exponential increase
  • Test-Trace-Isolate-Quarantine is more effective and less disruptive at low case numbers
    → acceleration possible when TTIQ systems get overwhelmed
  • What role does seasonality have?
    → behavioral component: indoor vs outdoor activities
    → environmental component: less ventilation, drier air
    → interaction with other viruses

Seasonal incidence of influenza viruses

Data by the US CDC

2009 pandemic influenza -- US

2009 pandemic influenza -- UK

By Dave Farrance - wikipedia

2009 pandemic influenza -- Germany


1918 influenza --- UK

Taubenberger et al
by Trevor Bedford

Human corona viruses have pronounced seasonal prevalence (Sweden)

  • Most respiratory virus including established CoVs show seasonality
  • Little direct evidence; absolute effect of seasonality unknown
  • But expect control of the virus to be harder in winter
Neher et al

Potential transition to an endemic seasonal virus

Influenza and Theory acknowledgments

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

  • All the NICs and WHO CCs that provide influenza sequence data
  • The WHO CCs in London and Atlanta for providing titer data


Trevor Bedford and his lab -- terrific collaboration since 2014

especially James Hadfield, Emma Hodcroft, Ivan Aksamentov, Moira Zuber, and Tom Sibley

Data we analyze are contributed by scientists from all over the world

Data are shared and curated by GISAID