Long overdue 🧵 on a COVID study we preprinted last year, now out in JID. Co-led by @nialljlennon @gabriel_stacey @eric_lander, enabled by @BroadGenomics & @MassDPH

Key finding: shed viral load is independent of symptoms at time of testing (pre-vax)


Back in Apr 2020, COVID overwhelmed our hospital in MA (I was on service & will never forget).

It also swept thru nursing homes, so @MassDPH tested ALL residents+staff of SNFs for COVID (back when tests were rare: big job w help from MA Nat'l Guard).
B/c of @broadinstitute's remarkable early scale-up of testing, >70% of these (>30,000 over 2+ mo) were run @BroadGenomics. @nialljlennon & @gabriel_stacey recognized an opportunity to study viral loads on their standardized platform; @MassDPH had queried symptoms (yes/no).
So, we could ask how a binary assessment of symptom status at the time of testing correlated w/ viral load.

We found no relationship at all.

People without symptoms were just as likely to be shedding 1 billion copies per mL of viral RNA as people with them.
That is the main takeaway from our paper: even w/o symptoms, ppl can shed tons of virus. This was pre-vaccines, pre-Delta or Alpha. But already we knew that asymptomatic or presymptomatic transmission was a huge problem: one of the biggest challenges to containing COVID imo.
(Variability has been a huge challenge for COVID too… bad enough to kill millions & collapse health care systems; variable enough that it's way more common to know someone who had a mild case: everyone knows an Uncle Bob who's smoked for 50 yrs but did fine… but I digress)
Anyway, the data held more surprises. We had heard anecdotally that Ct had been rising (VL dropping) over time as the initial pandemic wave receded. @AnnWoolleyMD helped by looking at BWH Ct data that confirmed this.
So we dug deeper. And while there was no difference in viral loads as the local epidemic peaked (Apr 2020), a difference emerged over time as it waned: later in our study (Jun 2020), those without symptoms had lower viral loads than those with symptoms.
What gives? Why would viral load's relationship to symptoms change w month? Turns out it relates to epidemic dynamics, & to trajectories of viral shedding (peaks early, declines slowly) & symptoms (start early in illness, if they happen at all). I tried to diagram that here.
As case counts rise, most cases are recent, so regardless of symptoms, most folks have high VLs. Later, as cases recede, cases happened longer ago on avg. But those w symptoms are selected to be early in illness, when VLs are higher; not so for those w/o symptoms.
I again tried to diagram that here.
Sx = red, no Sx = red or blue
top = rising cases (early in study)
bottom = declining cases (later in study)

Those w/o symptoms can accumulate in the "clearance" phase, with low VL, causing the "pileup" seen at high Ct in the histogram.
If this sounds familiar, maybe it's b/c co-author @michaelmina_lab proved it more rigorously w/ @jameshay218: the shape of a Ct histogram (from a random cross-section of people - this is key) can predict epidemic dynamics in a single measurement.

- no longitudinal f/u = can't tell asympt from presympt
- severe cases excluded (b/c they'd have been in hospital, not SNF)
- Ct ~ viral RNA copies (b/c of standard curve), but ≠ infectious virus; still, we think Ct informs risks of spread (esp pre-vaccine)
There's more nuance in the article, but I learned a lot from this study - it shaped how I think abt SARS-CoV-2 shedding & spread - so I thought I'd share.

Thx to co-authors tagged above + @HeidiRehm, Sandy Smole, & Deb Hung (tweetless), & JID editors @IDSAInfo @OUPMedicine
I'll add: the non-intuitive finding that epidemic phase affects measured VL affects analysis of variant & post-vax VLs.

New variants, by def'n, are increasing in freq as they emerge = measured VL will artifactually be higher (even if "true" VL trajectory unchanged).
Similarly, since #VaccinesWork, infections become less frequent after vaccination; so measured VL will artifactually be lower (cases less likely to be recent).

Variants or vax may have TRUE effects on VL, aside from this artifact. We just have to be careful how we measure.
In assessing measured VL, timing of test (& thus the reason it was sought) is crucial for interpreting results.

Best case is full longitudinal trajectories! But that's hard to come by, unless you're @yhgrad studying the NBA:

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More from @roby_bhatt

13 Oct
Nice interplay of threads from experts on a key question: what will the future of COVID bring? Nobody knows for sure, but @trvrb and @WhitneyEpi discuss some key factors on the viral and human/societal sides, respectively. Both 🧵's worth a read in full, but my tl;drs follow. 1/5
2/5 1st, @trvrb says:
- SARS-CoV-2 is here to stay
- more contagious at baseline than flu = prob larger annual infection footprint
- key unknowns that'll affect impact: rates of waning immunity & antigenic drift; severity of post-immunity infections (IFR)

3/5 @WhitneyEpi adds that human behaviors matter too for annual impact:
- will we modify behavior, esp during surges? how? how much?
- will we test? how much? in Sx or aSx?
- will we improve ventilation?
- will we boost vaccines? how often?

Read 5 tweets
1 Oct
Eyre et al (medrxiv.org/content/10.110…) is a tour-de-force. Bravo!
- traced 150k contacts from 100k cases (! - NHS👏🏽)
- Pfizer vax'd index cases had ~5x (Alpha) or 3x (Delta) lower odds of spreading (this is beyond protection by preventing the index case to begin with)
🧵 1/6
- this despite similar cycle thresholds (Ct) in vax'd vs unvax'd index Delta cases, as others have shown
- Pfizer vax'd contacts had 16x (Alpha) or 10x (Delta) lower odds of being infected
- Ct is not infectivity
- Ct is not infectivity
- Ct is not infectivity
- #VaccinesWork
- they also saw waning, c/w other studies (incl imo the best studies, the post-hoc crossover analyses of mRNA RCTs). Here, they found 1.2x increased odds of transmission "for each doubling of weeks since 14 days after 2nd vax in index cases" (?), & 1.4x increase in contacts.
Read 11 tweets
29 Dec 20
At the risk of shouting into the void, 🧵 on papers from 2020 that most changed how I think abt COVID, as an ID physician-scientist. 280-char summaries + URLs for each. Thx to all authors & apologies for any omissions; this list is unofficial, personal, idiosyncratic, & LONG. 1/
2/ Early summary of 72,314 (hospitalized) cases from Chinese CDC, broken down by mild vs severe vs critical, early hint at CFR (overestimated b/c mild cases undersampled), & sharply age-dependent mortality. Also, risk to HCWs. Fig 1 (epidemic curve) key.

3/ Another inpt obs cohort study from China 🙏🏽. Fig 2 shows prognostic biomarkers (lymphs, D-dimer, IL-6 – not CRP, despite my false memory). Watched (helplessly) a lot of these rise in worsening patients. Still wonder why we ordered so many, so often.

Read 38 tweets
6 Aug 20
NEJM editorial argues that low sensitivity tests are problematic

If the alternative is a perfect test, sure. But tests are still too scarce. We need more & faster tests; can sacrifice Se w/ clear communication

@michaelmina_lab @DanLarremore @RWalensky 🧵
From the editorial: "A big concern has been test availability, but test accuracy may prove a larger long-term problem."

I disagree w this framing: the two features are in direct tension. If we hold out for "perfectly" sensitive tests, we resign ourselves to less testing.
Great modeling studies from folks I tagged shows frequency/TAT are MUCH more impt than sensitivity:


Even w/o repeat tests, even one 70% Se test catches far more than no test at all. (& those it misses probably shed less virus)
Read 9 tweets

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