Here is my summary of an exciting new @NBA + longitudinal COVID testing paper.

Writing a thread about COVID and the NBA has been on my bucket list for some time, so today I decided to box out some time and give it a shot. 1/n

medrxiv.org/content/10.110…
Most of what we know about viral dynamics during SARS-CoV-2 infections comes from samples taken *after* symptom onset. From symptoms onward, viral loads slowly fadeaway.

What do viral loads look like between exposure and symptoms? 2/n
In this study, researchers in the NBA bubble recruited players, coaches, vendors, and others to sign up for a longitudinal study with regular COVID testing.

In other words, the researcher ran a classic pick-enroll-screen in the NBA bubble. 3/n
Critically, this study was done prior to the release of the long-awaited vaccine. So there was time left on the shot clock. 4/n
Longitudinal testing identified infections early, and then monitored the unfolding trajectories: Ct counts drop and then rebound, corresponding to what the researchers call the “proliferation” and “clearance” phases. We know comparatively little about the proliferation phase! 5/n
After fitting a simple model to data from 46 individuals, the researchers found that symptomatic & asymptomatic people show similar viral dynamics during the proliferation phase when the infection is heating up, but different clearance times after the pivot has passed. 6/n
I want to call a timeout here to note that the proliferation phase was found to last 0.7 to 4.7 days, averaging 3. So, the window of time during which a PCR outperforms less sensitive alternatives is around a day.

What does this mean for testing strategies? 7/n
First, during man-to-man contact tracing following exposure, PCR can dunk on less sensitive tests by allowing *slightly* earlier detection during a likely proliferation phase.

But folks should quarantine post-exposure anyway. Travel would be a moving violation. 8/n
On the other hand, during repeated screening tests of asymptomatics, which sets up a zone defense, the brief window during which PCR detects early is less important than increasing the accessibility and frequency of screening to clog the paint. 9/n

nejm.org/doi/full/10.10…
The @NBA demonstrated an effective strategy of repeated testing in the bubble, but I hope the lesson generalizes:

Frequent testing creates a lockdown defense that can literally be a defense against lockdowns! 10/n
And one more key point. This study goes on to show that by interpreting the Ct values from *two* consecutive tests, it’s possible to predict whether an infection is in the proliferation or clearance phase! Some had speculated this could be possible—and this study did it! 11/n
Here’s a link to a proper thread on the paper by one of its authors, the wonderful @yhgrad.

Basketball puns aside, this is such an interesting, clear, and well written paper. I'm a fan.

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More from @DanLarremore

1 Jul
How does effective viral surveillance change when (1) some people refuse to participate, and (2) sample collection errors lead to lower sensitivity, indep. of a test's limit of detection? Questions raised by @jhuber @awyllie13 & others after I posted this preprint last week.👇 1/
I love twitter+preprints precisely because of this community. In the updated preprint, we've corrected a couple typos, and created a new supplement, "Adjustments for false negatives and test refusal" which I'll quickly summarize below. 2/ medrxiv.org/content/10.110…
Previously, we estimated the impact of a policy on R by measuring the "infectiousness" the testing, relative to no testing. The formula's values correspond to the heights of bars in plots like this one. f0 is the leftmost hatched bar. ftest is the total height of a policy bar. 3/
Read 8 tweets
25 Jun
Preprint: Viral surveillance testing is crucial, but not all surveillance strategies are equal. We modeled the impacts of test frequency, assay limit of detection, test turnaround time, measuring impact on individuals & epidemics. Here's what we found. 1/ medrxiv.org/content/10.110…
The first finding is that limit of detection matters less than we thought. There is only short (1/2 day) window when qPCR is superior during the exp growth phase. We showed this in a simple viral load model, but any model with exp growth between Ct40 and Ct33 would confirm. 2/
So only a high-frequency testing scheme will take advantage of that short window. However, high-frequency testing schemes will have a high impact on the reproductive number, *regardless* of test LOD. ➡️ Ruling out higher LOD tests for surveillance purposes would be a mistake. 3/
Read 18 tweets
18 Jun
My colleagues and I are formally seeking a retraction of the recently published “Identifying airborne transmission as the dominant route for the spread of COVID-19.” The full text of our letter to the PNAS editorial board can be found here. 1/
metrics.stanford.edu/PNAS%20retract…
It is important that science, especially now, be as rigorous and methodologically sound as possible. However, this paper suffers from numerous and fundamental errors that undermine the foundation of its conclusions. The paper is linked here. 2/ pnas.org/content/early/…
Masks help in the fight against COVID-19. Our call for one study to be retracted should not detract from that important message. Indeed, a recently published meta-analysis showed that mask use (N-95 esp), could result in large risk reduction. 3/
thelancet.com/journals/lance…
Read 6 tweets
20 Apr
Sensitivity & specificity affect the inferences that we can draw from seroprevalence studies & inform the number of samples we need for statistical confidence. To help, we built two calculators. Calculator 1: survey data, se, sp → prevalence posterior. larremorelab.github.io/covid-calculat…
But let's also remember: sensitivity & specificity are *estimated from data*. That means that they, too, need statistical treatment. So for Calculator 2: survey data AND raw assay calibration data → posteriors for prevalence, sensitivity, and specificity. larremorelab.github.io/covid-calculat…
Calculator 2 is important because it shows that the way we calibrate our tests is as important as the survey data we collect. You can incorporate all sources of uncertainty together, learning about prevalence, but also about uncertainty in sensitivity & specificity. 🤔🤓😎
Read 5 tweets
16 Apr
Earlier today, we put out a preprint that asked: how do we design and analyze SARS-CoV-2 seroprevalence surveys? @yhgrad wrote a lovely explainer thread, linked here. ] I want to highlight some #stats and #networks results. 1/
First, the basics. This paper's 1st result is like a statistical inference midterm problem: If you observe n+ positive tests, n- negative tests, and you know the sensitivity/specificity of your test, what is the posterior prob. of actual positives? Solution: Bayes' rule. ✅
Now a practical problem: The posterior looks like a binomial posterior, but due to sensitivity/specificity, we end up with incomplete beta functions & small things raised to n+ and n- powers & can't invert the CDF. Solution: take logs and use accept-reject algm to sample.✅ 3/
Read 16 tweets

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