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. 2021 May 26;13(595):eabf0202.
doi: 10.1126/scitranslmed.abf0202. Epub 2021 May 3.

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

Nicola F Müller #  1 , Cassia Wagner #  2   3 , Chris D Frazar #  3 , Pavitra Roychoudhury #  2   4 , Jover Lee  2 , Louise H Moncla  2 , Benjamin Pelle  3 , Matthew Richardson  3 , Erica Ryke  3 , Hong Xie  4 , Lasata Shrestha  4 , Amin Addetia  4 , Victoria M Rachleff  2   4 , Nicole A P Lieberman  4 , Meei-Li Huang  4 , Romesh Gautom  5 , Geoff Melly  5 , Brian Hiatt  5 , Philip Dykema  5 , Amanda Adler  6 , Elisabeth Brandstetter  7 , Peter D Han  3 , Kairsten Fay  2 , Misja Ilcisin  2 , Kirsten Lacombe  6 , Thomas R Sibley  2 , Melissa Truong  3 , Caitlin R Wolf  7 , Michael Boeckh  2   7   8 , Janet A Englund  6   9 , Michael Famulare  10 , Barry R Lutz  8   11 , Mark J Rieder  8 , Matthew Thompson  12 , Jeffrey S Duchin  7   13 , Lea M Starita  3   8 , Helen Y Chu  7   8 , Jay Shendure  3   8   14 , Keith R Jerome  2   4 , Scott Lindquist  5 , Alexander L Greninger  2   4 , Deborah A Nickerson  3   8 , Trevor Bedford  1   3   8
Affiliations

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

Nicola F Müller et al. Sci Transl Med. .

Abstract

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has gravely affected societies around the world. Outbreaks in different parts of the globe have been shaped by repeated introductions of new viral lineages and subsequent local transmission of those lineages. Here, we sequenced 3940 SARS-CoV-2 viral genomes from Washington State (USA) to characterize how the spread of SARS-CoV-2 in Washington State in early 2020 was shaped by differences in timing of mitigation strategies across counties and by repeated introductions of viral lineages into the state. In addition, 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 observed evidence of higher viral loads in patients infected with the 614G variant. However, using clinical records data, we did not find any evidence that the 614G variant affects clinical severity or patient outcomes. Overall, this suggests that with regard to D614G, the behavior of individuals has been more important in shaping the course of the pandemic in Washington State than this variant of the virus.

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Figures

Fig. 1
Fig. 1. SARS-CoV-2 phylogeny highlighting D614G split and cases through time in Washington State.
(A) Phylogenetic tree of 13,900 sequences from Washington State and around the world. Tips are colored on the basis of sampling location. This is a time-calibrated phylogeny with time shown in the x axis. The split between 614D sequences (blue) and 614G (orange) sequences is shown as a bar to the right of the phylogeny. (B to E) Confirmed cases and genetic makeup of SARS-CoV-2 across Washington State and individual counties. The green line shows a 7-day moving average of daily confirmed cases. The bar plots show weekly sequenced cases in our dataset. Cases due to the 614D variant are shown in blue, and cases due to the 614G variant are shown in orange. w/o, without.
Fig. 2
Fig. 2. Regional dynamics of SARS-CoV-2 in Washington State inferred from confirmed cases and pathogen genomes.
(A) Estimates of effective population sizes for the outbreak in Washington State (green interval) as well as for 614D (blue interval) and 614G (orange interval) individually as compared to confirmed cases in the state (gray bars). The inner band denotes the 50% highest posterior density (HPD) interval and the outer band denotes the 95% HPD interval. (B) Re estimates using a birth-death approach for the same groups as in (A). The Re estimates are compared to Google workplace mobility data for King, Pierce, Skagit, and Snohomish Counties shown as black solid and dashed lines. Workplace mobility is represented as a 7-day moving average.
Fig. 3
Fig. 3. Phylogenetic estimate of the percentage of introductions of the overall cases.
Percentages were estimated as the relative contribution of introductions to the overall number of infections using the multitree coalescent. Percentages are shown for the 2020 outbreak in Washington State (green interval) as well as for 614D (blue interval) and 614G (orange interval). The inner area denotes the 50% HPD interval and the outer area denotes the 95% HPD interval.
Fig. 4
Fig. 4. Factors affecting viral load and disease severity at an individual level.
(A) Comparison between cycle threshold (Ct) values for viruses with 614G and 614D variants. (B) GLM analysis of Ct values using spike variant, age, and days since symptom onset as predictors. (C) Odds ratio of being hospitalized given infection with SARS-CoV-2. Error bars show 95% CI, corrected for multiple hypothesis testing using a Bonferroni correction. (D) Estimates of the average chance that a patient from a given group was infected with a virus from the 614D clade. The error bars denote the SE of the average chance that a patient from a group was infected with a virus from the 614D clade.
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Update of

  • Viral genomes reveal patterns of the SARS-CoV-2 outbreak in Washington State.
    Müller NF, Wagner C, Frazar CD, Roychoudhury P, Lee J, Moncla LH, Pelle B, Richardson M, Ryke E, Xie H, Shrestha L, Addetia A, Rachleff VM, Lieberman NAP, Huang ML, Gautom R, Melly G, Hiatt B, Dykema P, Adler A, Brandstetter E, Han PD, Fay K, Llcisin M, Lacombe K, Sibley TR, Truong M, Wolf CR, Boeckh M, Englund JA, Famulare M, Lutz BR, Rieder MJ, Thompson M, Duchin JS, Starita LM, Chu HY, Shendure J, Jerome KR, Lindquist S, Greninger AL, Nickerson DA, Bedford T. Müller NF, et al. medRxiv [Preprint]. 2020 Sep 30:2020.09.30.20204230. doi: 10.1101/2020.09.30.20204230. medRxiv. 2020. Update in: Sci Transl Med. 2021 May 26;13(595):eabf0202. doi: 10.1126/scitranslmed.abf0202. PMID: 33024981 Free PMC article. Updated. Preprint.

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