Our research group at @fredhutch, @UWMedicine and @BrotmanBaty has sequenced the viral genomes of two SARS-CoV-2 infections that were connected to the White House #COVID19 outbreak. The @nytimes reports here: nytimes.com/2020/11/01/sci…. 1/16
We enrolled two individuals with exposures linked to the White House COVID-19 outbreak into an IRB-approved research study, collected nasal swabs and sequenced the SARS-CoV-2 virus in these swabs. 2/16
Importantly, these two individuals attested that they had no direct contact with each other in the days preceding their diagnoses and are independently linked to the White House COVID-19 outbreak. 3/16
One infection, which we're calling WH1, yielded a complete genome with 14 mutations that distinguish it from the SARS-CoV-2 Wuhan reference virus. The other infection, WH2, yielded a partial genome. 4/16
The subset of sites with coverage in WH2 all agree with calls in WH1. The mutation 7936T in particular supports a match. It's present in only 0.04% of sequenced viruses. And all 5 mutations are only present together only in WH1 and WH2 out of 160,291 public viral sequences. 5/16
Together WH1 and WH2 belong to clade 20C and lineage B.1.26, which place them squarely within the genetic diversity of viruses circulating as part of the US epidemic. 6/16
We find that WH1 and WH2 are descended from viruses circulating primarily in the USA in March and April 2020 with the addition of mutations A1977G, G7936T, G14250T, T18417C, C19524T and C20402T. 7/16
The C20402T mutation is shared by 3 viruses sampled from Virginia in August ("USA/VA-DCLS-" in the above), but this lineage has 6 mutations unique to it and a molecular clock analysis places the common ancestor of this lineage and the WH lineage in April or early May. 8/16
Thus, it appears that the transmission chain leading to the White House cluster has circulated in the US for 5 or 6 months collecting an additional 5 mutations that have not been recorded in other sequenced samples. 9/16
The finding of a lineage this diverged from other sequenced viruses is rare, but not too surprising. If we look at all sequenced viruses from the US collected after August 1, we see that 5% of them are 5 or more mutations distant from other sequenced viruses. 10/16
The reason these sorts of divergent lineages exist is because although the US has sequenced and shared over 35,000 SARS-CoV-2 genomes (a remarkable achievement!), this is still less than 0.4% of the confirmed COVID-19 cases in the US. 11/16
Still, it's possible that backfill of additional sequences from viruses sampled in August or September will reveal more closely related antecedents to WH1 and WH2. 12/16
And looking forward, the relative rarity of the constellation of mutations in the WH lineage may make it possible to identify infections that possibly descend from the White House COVID-19 outbreak. 13/16
There is precedent for this in a superspreading event at the Biogen conference in Boston in February, where conference-associated mutations were later found in community cases of COVID-19 in Massachusetts (medrxiv.org/content/10.110…). 14/16
We've submitted the manuscript to medRxiv, but in the interim the (non-peer reviewed!) technical report is available as a PDF at bedford.io/pdfs/papers/be…. 15/16
Sincere thanks to co-authors of this work @JShendure, @HelenChuMD, @lea_starita, @LockwoodDNA, @debnick60, @mjonasrieder, @RykeErica and others not on Twitter. 16/16
Follow up #1: The @medrxivpreprint preprint is now up at medrxiv.org/content/10.110….
Follow up #2: We've had another pass at sequencing WH2. This time we got a complete genome with 5574X coverage. We find that WH1 and WH2 are completely identical across the full genome. This does not change any conclusions from the manuscript, but increases certainty.

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More from @trvrb

1 Nov
Separately, I wanted to address the question of "why do this?" with regards to sequencing of infections involved in the White House #COVID19 outbreak. 1/10
Although the origins of the White House outbreak were characterized as "unknowable", viral genome sequencing can offer important clues to how infections in a cluster are connected to each other and to the larger COVID-19 epidemic. 2/10
This technology is rapidly becoming a standard course of action for COVID-19 clusters of public health interest. This seems obvious, but we can use science to understand and track the spread of COVID-19. 3/10
Read 10 tweets
19 Oct
Daily #COVID19 case counts are increasing in the US and we seem to hitting a third wave (or second surge if you'd prefer). Here I wanted to look at how case counts through time correlate across different states. 1/12
I start with a simple coloring to group states in the West (in red), the Southwest (in orange), the Midwest (in green), the Southeast (in blue) and the Northeast (in purple). Color ramp borrowed from @andersonbrito_. 2/12
Using data from @COVID19Tracking, I plot daily confirmed cases for each state since March as a stacked chart. The three crests are obvious (though not clear how large the third will end up being). Different regions are contributing to each wave to different degrees. 3/12
Read 13 tweets
9 Aug
A follow up to yesterday's controversial thread on societal behavior, population immunity and Rt to specifically address issue of what fraction of the population in Florida, Texas and Arizona may have had COVID-19. 1/16
Multiple people expressed skepticism that 20% seroprevalence in Florida is reasonable. Others thought that 20% was patently impossible due to implied crude infection fatality ratio (IFR). 2/16
This thread walks through a ballpark version of implied IFR that takes into account reporting delays in Florida, Texas and Arizona in their recent epidemic surge. Data and figures that follow from @COVID19Tracking. 3/16
Read 17 tweets
7 Aug
I wanted to discuss the degree to which population immunity may be contributing to curbing #COVID19 in Florida, Arizona and Texas, where recent surges have resulted in substantial epidemics. 1/16
After increasing dramatically in June and July, daily case counts in Florida, Arizona and Texas have begun to subside. Data from @COVID19Tracking. 2/16
This corresponds to a peak Rt of between 1.2 and 1.4 in late-May / early-June and steady reductions since this point. Declining case counts correspond to Rt < 1. Rt estimates from rt.live. 3/16
Read 17 tweets
23 Jul
With @CDCgov's update to their seroprevalence results across sites in the US, it's possible to see if with increased availability in testing whether we're catching a larger fraction of infections as confirmed cases. 1/13
The @CDCgov seroprevalence survey (cdc.gov/coronavirus/20…) tests blood samples from different sites in the US for COVID-19 antibodies. With a serosurvey, it's possible to look at the path of the epidemic in a broader fashion than what's available from PCR assay results. 2/13
By taking multiple cross-sectional samples, it's possible to see the rate at which seropositivity increases. Here I'm replotting data from the CDC dashboard (cdc.gov/coronavirus/20…) to show seroprevalence at 6 sites across 2 different timepoints for each site. 3/13
Read 14 tweets
17 Jul
A follow up to the thread two weeks ago on rising case counts and "reopening", looking at continued trajectories in states with large epidemics. 1/15
When looking at epidemic dynamics, it's worth paying close attention to how the "fundamental reproductive number" changes through time, usually called Rt. This expresses how many secondary infections a primary infection leaves on average. 2/15
Approaches like epiforecasts.io/covid/ and rt.live estimate Rt from confirmed case data. Critically, due to disease incubation period, delays in seeking testing and delays in test reporting, we don't know what Rt is at the very moment. 3/15
Read 17 tweets

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