Using open data to track and predict infectious disease


Richard Neher
Biozentrum & SIB, University of Basel


slides at neherlab.org/201910_RIVM.html

Sequences record the spread of pathogens

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

Influenza virus genome - 8 segments

Zika virus genome $\sim 10000$ bases

Ebola virus genome $\sim 20000$ bases

Many RNA viruses pick up one mutation every 2-4 weeks!

Frequent mutations imply...

  • most viruses in an outbreak/season differ from each other
  • transmission chains are can be inferred
  • transmission can be ruled out!
  • geographic spread can be reconstructed
  • drug resistance surveillance
  • specific mutations might mediate antigenic mismatch


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

Vaccine strain selection schedule

Klingen and McHardy, Trends in Microbiology

GISRS -- 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

nextflu.org

joint project with Trevor Bedford & his lab

Visualization features of nextstrain

  • Regular and time scaled phylogenies
  • Mutations are mapped to the tree
  • Filtering to time interval, region, country, authors, ...
  • Zoom into clades
  • Information on specific viruses
  • Color by amino acid or nucleotide
  • Frequency trajectories of clades and mutations
  • Color by antigenic advance, predictive scores, etc
Hadfield et al, 2018

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

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

Our current prediction...

Enterovirus D68 -- with Robert Dyrdak, Emma Hodcroft & Jan Albert

  • Non-polio enterovirus
  • Almost everybody has antibodies against EV-D68
  • Large outbreak in 2014 with severe neurological symptoms in
    young children (acute flaccid myelitis)
  • Another outbreak in 2016
  • Outbreaks tend to start in late summer/fall
  • Several reports of EV-D68 outbreaks last fall
    (201 AFM cases in the US in 2018)

EV-D68 whole genome deep sequencing project across Europe

Geographic and demographic distribution EV-D68

Acknowledgments

  • Trevor Bedford
  • Pavel Sagulenko
  • James Hadfield
  • Emma Hodcroft
  • Tom Sibley
  • and others

Influenza and Theory acknowledgments

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

Acknowledgments -- Enterovirus

  • Robert Dyrdak
  • Jan Albert
  • Lina Thebo
  • Emma Hodcroft
  • Bert Niesters (Groningen)
  • Randy Poelman (Groningen)
  • Elke Wollants (Leuven)
  • Adrian Egli (Basel)
  • Andrés Antón Pagarolas (Barcelona)