Real-time genomic surveillance of pathogen evolution and spread

Trevor Bedford (@trvrb)
4 Apr 2017
D-BSSE Seminar Series
ETH Zurich

Slides at bedford.io/talks/

Phylogenies

Phylogenies describe history

Phylogenies describe history

Phylogenies describe history

Phylogenies reveal process

Epidemic process

Sample some individuals

Sequence and determine phylogeny

Sequence and determine phylogeny

Localized Middle Eastern MERS-CoV phylogeny

Regional West African Ebola phylogeny

Global influenza phylogeny

Applications of evolutionary analysis for influenza vaccine strain selection and charting spread of Ebola and Zika

Influenza

Influenza virion

Population turnover (in H3N2) is extremely rapid

Clades emerge, die out and take over

Clades show rapid turnover

Dynamics driven by antigenic drift

Drift variants emerge and rapidly take over in the virus population

This causes the side effect of evading existing vaccine formulations

Drift necessitates vaccine updates

H3N2 vaccine updates occur every ~2 years

Timely surveillance and rapid analysis essential to vaccine strain selection

nextflu

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

nextflu

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

All in collaboration with Richard Neher

nextflu pipeline

1. Download all recent HA sequences from GISAID
2. Filter to remove outliers
3. Subsample across time and space
4. Align sequences
5. Build tree
6. Estimate clade frequencies
7. Infer antigenic phenotypes
8. Export for visualization

nextflu.org

Forecasting

The future is here, it's just not evenly distributed yet — William Gibson

USA music industry, 2011 dollars per capita

Influenza population turnover

Vaccine strain selection timeline

Seek to explain change in clade frequencies over 1 year

Fitness models can project clade frequencies

Clade frequencies $X$ derive from the fitnesses $f$ and frequencies $x$ of constituent viruses, such that

$$\hat{X}_v(t+\Delta t) = \sum_{i:v} x_i(t) \, \mathrm{exp}(f_i \, \Delta t)$$

This captures clonal interference between competing lineages

Predictive fitness models

A simple predictive model estimates the fitness $f$ of virus $i$ as

$$\hat{f}_i = \beta^\mathrm{ep} \, f_i^\mathrm{ep} + \beta^\mathrm{ne} \, f_i^\mathrm{ne}$$

where $f_i^\mathrm{ep}$ measures cross-immunity via substitutions at epitope sites and $f_i^\mathrm{ep}$ measures mutational load via substitutions at non-epitope sites

We implement a similar model based on two predictors

1. Clade frequency change
2. Antigenic advancement

Project frequencies forward,
growing clades have high fitness

Calculate antigenic difference from ancestor via serological data

Fitness model parameterization

Our predictive model estimates the fitness $f$ of virus $i$ as

$$\hat{f}_i = \beta^\mathrm{freq} \, f_i^\mathrm{freq} + \beta^\mathrm{HI} \, f_i^\mathrm{HI}$$

We learn coefficients and validate model based on previous 15 H3N2 seasons

Clade growth rate is well correlated (ρ = 0.66)

Growth vs decline correct in 84% of cases

Trajectories show more detailed congruence

Real-time analyses are actionable and may inform influenza vaccine strain selection

Ebola

Tracking geographic spread of the Ebola epidemic

with Gytis Dudas, Luiz Carvalho, Marc Suchard, Philippe Lemey, Andrew Rambaut
and many others

Sequencing of 1610 Ebola virus genomes collected during the 2013-2016 West African epidemic

Phylogenetic reconstruction of evolution and spread

Tracking migration events

Factors influencing migration rates

Effect of borders on migration rates

Spatial structure at the country level

Substantial mixing at the regional level

Regional outbreaks due to multiple introductions

Each introduction results in a minor outbreak

Ebola spread in West Africa followed a gravity model with moderate slowing by international borders, in which spread is driven by short-lived migratory clusters

Zika

Zika's arrival and spread in the Americas

Tracking origins of the Zika epidemic

with Nuno Faria, Nick Loman, Oli Pybus, Luiz Alcantara, Ester Sabino, Josh Quick, Allison Black, Ingra Morales, Julien Thézé, Marcio Nunes, Jacqueline de Jesus, Marta Giovanetti, Moritz Kraemer, Sarah Hill and many others

Road trip through northeast Brazil to collect samples and sequence

Case reports and diagnostics suggest initiation in northeast Brazil

Sequencing shows accumulating genetic diversity

Phylogeny infers an origin in northeast Brazil

Local spread of Zika in Florida

with Kristian Andersen, Nathan Grubaugh, Jason Ladner, Gustavo Palacios, Sharon Isern, Oli Pybus, Moritz Kraemer, Gytis Dudas, Amanda Tan, Karthik Gangavarapu, Michael Wiley, Stephen White, Julien Thézé, Scott Michael, Leah Gillis, Pardis Sabeti, and many others

Outbreak of locally-acquired infections focused in Miami-Dade county

Phylogeny shows a surprising degree of clustering

Clustering suggests fewer, longer transmission chains and higher R₀

Extrapolate R₀ to predict introduction counts driving outbreak

Flow of infected travelers greatest from Caribbean

Southern Florida has high potential for Aedes borne outbreaks

Important analyses, let's make them more rapid and more automated

Key challenges

• Timely analysis and sharing of results critical
• Dissemination must be scalable
• Integrate many data sources
• Results must be easily interpretable and queryable

nextstrain

Project to conduct real-time molecular epidemiology and evolutionary analysis of emerging epidemics

Richard Neher, Trevor Bedford, Colin Megill,
James Hadfield, Charlton Callender, Sidney Bell,
Barney Potter, Sarah Murata,

Nextstrain architecture

nextstrain.org

Rapid on-the-ground sequencing by Ian Goodfellow, Matt Cotten and colleagues

Desired analytics are pathogen specific and tied to response measures

Acknowledgements

Influenza: WHO Global Influenza Surveillance Network, GISAID, Worldwide Influenza Centre at the Francis Crick Institute, Richard Neher, Colin Russell, Boris Shraiman

Ebola: data producers, Gytis Dudas, Andrew Rambaut, Luiz Carvalho, Philippe Lemey, Marc Suchard, Andrew Tatem, Nick Loman, Ian Goodfellow, Matt Cotten, Paul Kellam, Kristian Andersen, Pardis Sabeti, many others

Zika: data producers, Nick Loman, Nuno Faria, Oliver Pybus, Josh Quick, Allison Black, Kristian Andersen, Nathan Grubaugh, Gytis Dudas, many others

Nextstrain: Richard Neher, Colin Megill, James Hadfield, Charlton Callender, Sarah Murata, Sidney Bell, Barney Potter