Genomic tracking of SARS-CoV-2 evolution and spread


Trevor Bedford (@trvrb)
8 May 2020
Biology of Genomes 2020
Cold Spring Harbor
Slides at:

Significant fog of war. Genomic approaches offer orthogonal data source to understand the pandemic.

Epidemic process

Sample some individuals

Sequence and determine phylogeny

Sequence and determine phylogeny


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

with Richard Neher, James Hadfield, Emma Hodcroft, Thomas Sibley, John Huddleston, Louise Moncla, Cassia Wagner, Miguel Parades, Misja Ilcisin, Kairsten Fay, Jover Lee, Allison Black, Colin Megill, Sidney Bell, Barney Potter, Charlton Callender

Nextstrain architecture

All code open source at

Two central aims: (1) rapid and flexible phylodynamic analysis and
(2) interactive visualization

Rapid build pipeline for 3000 SARS-CoV-2 genomes (timings are for a laptop)

  • Align with MAFFT (~20 min)
  • Build ML tree with IQTREE (~40 min)
  • Temporally resolve tree and geographic ancestry with TreeTime (~50 min)
  • Total pipeline (~2 hr)

Current data flow for SARS-CoV-2

  1. Labs contribute directly to GISAID (now have >17k full genomes)
  2. Nextstrain pulls a complete dataset from GISAID every 60 minutes
  3. This triggers an automatic rebuild on Amazon Web Services
  4. We manually update new lat/longs, etc...
  5. We push this build online to and tweet the update from @nextstrain

We do about one update per 12 hours via Seattle and Basel. We were regularly getting 200k visitors per day to the site, now down to 50k.

Dec/Jan: Emergence of SARS-CoV-2 from Wuhan in ~Nov 2019

Jan/Feb: Spread within China and seeding elsewhere

Feb/Mar: Epidemic spread within North America and Europe

Mar/Apr: Continued growth, but decreasing transmission with social distancing measures

Epidemic in the USA was introduced from China in late Jan and from Europe during Feb

Once in the US, virus spread rapidly

Single introduction ~Feb 1 quickly shows up throughout the country

Sequencing immediately useful for epidemiological understanding, but selection and functional impacts should also be studied

Significant interest in spike mutation D614G

This mutation occurred in the initial European introduction

D614G is prevalent throughout Europe and mixed in US and Australia

D614G is increasing in frequency across states in US and Australia

D614G is increasing in frequency across states in US and Australia

The success of D614G can be explained by either:

  • D614G is more transmissible and has higher $R_0$
  • founder effects and epidemiological confounding

Additional evidence from Ct values of clinical specimens

Moving forward

  • Genomic approaches immediately useful to surveillance, particularly to distinguish endogenous spread from importations
  • Longer term tracking of antigenic drift for vaccine strain updating
  • Our best way out of this mess is with test-trace-isolate


Genomic epi: Data producers from all over the world, GISAID and the Nextstrain team