Viral genomes reveal patterns of the SARS-CoV-2 outbreak in Washington State

Nicola F. Müller, Cassia Wagner, Chris D. Frazar, Pavitra Roychoudhury, Jover Lee, Louise H. Moncla, Benjamin Pelle, Matthew Richardson, Erica Ryke, Hong Xie, Lasata Shrestha, Amin Addetia, Victoria M. Rachleff, Nicole A. P. Lieberman, Meei-Li Huang, Romesh Gautom, Geoff Melly, Brian Hiatt, Philip Dykema, Amanda Adler, Elisabeth Brandstetter, Peter D. Han 2, Kairsten Fay 1, Misja Ilcisin, Kirsten Lacombe, Thomas R. Sibley, Melissa Truong, Caitlin R. Wolf, Michael Boeckh, Janet A. Englund, Michael Famulare, Barry R. Lutz, Mark J. Rieder, Matthew Thompson, Jeffrey S. Duchin, Lea M. Starita, Helen Y. Chu, Jay Shendure, Keith R. Jerome, Scott Lindquist, Alexander L. Greninger, Deborah A. Nickerson, Trevor Bedford

Abstract

The rapid spread of SARS-CoV-2 has gravely impacted societies around the world. Outbreaks in different parts of the globe are shaped by repeated introductions of new lineages and subsequent local transmission of those lineages. Here, we sequenced 3940 SARS-CoV-2 viral genomes from Washington State to characterize how the spread of SARS-CoV-2 in Washington State (USA) was shaped by differences in timing of mitigation strategies across counties, as well as by repeated introductions of viral lineages into the state. Additionally, we show that the increase in frequency of a potentially more transmissible viral variant (614G) over time can potentially be explained by regional mobility differences and multiple introductions of 614G, but not the other variant (614D) into the state. At an individual level, we see evidence of higher viral loads in patients infected with the 614G variant. However, using clinical records data, we do not find any evidence that the 614G variant impacts clinical severity or patient outcomes. Overall, this suggests that with regards to D614G, the behavior of individuals has been more important in shaping the course of the pandemic than changes in the virus.

Organization

data: Contains TSV of data used for Ct and clinical analyses cleaned of patient identifiers.
analyses: Contains R markdown files with code for analyses and code to produce the cleaned dataset.
scripts: Contains python scripts used in assembling the dataset.
figures: Contains PDFs and JPGs of figures generated from the analyses.
additional-analyses: Contains additional analyses not described in the manuscript.

Citation

All code (files ending in .py or .md) is licensed under an MIT License.