Threats from within and without
Phylogenetic insights into virus transmission between communities and species
JT McCrone
May 16, 2024
Global Infectious Disease Seminar Series
Wisconsin National Primate Research Center
Virus replication is error prone
Virus evolution records transmission
Time trees
Assessing temporal signal
SARS-CoV-2
Sampling duration matters
Ebola dynamics in the unknown reservoir
Ebola outbreaks 1976-2022
- (-) sense RNA virus ~18 Kb genome
- 17 independent spillover events from a poorly characterized reservoir
- 1.2e-3 sub/site/year in humans
- Anti-Ebola antibodies and viral RNA have been detected in multiple bat species
Temporal signal in Ebola 1976-2014
Evolutionary dynamics 1976-2014
Temporal signal in Ebola after 2014
Rate heterogeneity in humans
Keita, A. K, et al., 2021
A latent branch-rate model
evolutionary rate = 0| μ
latent $ \overset{\mbox{$\lambda$}}{\Leftrightarrow}$ replicating
Latent branches date back to 1980s
Geographic implications of latency
Conclusion: Ebola dynamics in the reservoir
- Active transmission from 1970-2014 seeding latent infections
- Recent outbreaks (2014-present) stem from reactivated infections
- Long-lived reservoir host
- Dynamic spill-over threat
- Unknown mechanism for latency
From 17 to 17M genomes?
The unique challenges of large densely sampled outbreaks
- Data is too large for a "storybook" approach
- Common approaches are intractable
- Poorly defined structure within clades
Opportunities
- Well defined large-scale clades
- Many short internal branches
cov2tree.org
Bayesian analyses scale poorly
- Tree-space is vast (more trees than particles in the universe)
- Explore the space in small steps
- Each step is computationally expensive
Limit our search to reasonable trees
Constrain our search with a semi-resolved tree
Bayesian analyses scale poorly
- Tree-space is vast (more trees than particles in the universe)
- Explore the space in small steps
- Each step is computationally expensive
Increased efficiency with simplified likelihood
Tractable phylogenetics for the pandemic
Computational time
Memory usage
Bayesian analyses scale poorly
- Tree-space is vast (more trees than particles in the universe)
- Explore the space in small steps
- Each step is computationally expensive
The rise of Delta
Objectives
- Characterize the role of targeted travel restrictions
- Describe the internal spread of Delta in England
- Determine the impact of local mixing patterns on the rate of spread
Data
- >90K genomes (~50% England)
- Normalized travel intensities from aggregated mobile phone data
McCrone et al., 2022
Delta importations dynamics
McCrone et al., 2022
Within-country spread
McCrone et al., 2022
Increased mobility ⇒ faster Delta sweep
McCrone et al., 2022
Delta in the UK
- Importations correlate with relative importation intensity
- Importation dynamics were disrupted by travel restrictions
- Travel restrictions were not effective in curtailing the spread of Delta in England
- Heterogeneous dispersal patterns within the UK driven by North-West outbreak
- Local mobility a key factor in Delta's growth rate
McCrone et al., 2022
Future directions for large, viral datasets
Thank you
University of Edinburgh
- Andrew Rambaut
- Verity Hill
- Ifeayni Omah
- Ben Jackson
- Áine O'Toole
- Rachel Colquhoun
- Emily Scher
- Shawn Yu
Oxford
- Louis du Plessis
- Alex Zarebski
- Oliver Pybus
- Moritz Kramer
- Sumali Bajaj
- Rosario Dawson
- Chris Ruis
- Guy Baele -KU Leuven
Fred Hutch
- Eric Matsen
- Joseph Brew
