Preprint: COVID-19 screening and surveillance are critical, but molecular tests haven't come close to meeting needs, and temperature checks fail. We modeled the epidemiological impacts of using loss of smell as a screening symptom. Here's what we found. 1/ medrxiv.org/content/10.110…
Loss of smell is an interesting screening symptom because it's highly specific to COVID, precedes most other overt symptoms, and typically lasts ~1 week. Critically, its prevalence goes from ~45% when self-reported up to ~80% when a test is used. 2/
Contrast this with fever: ~20% prevalence, not specific to COVID, and lasts 1.5 days on average. So why do we still screen for fever? You can look for it in seconds with a contactless thermometer.

Could rapid, contactless, cheap tests for anosmia, impact transmission? 3/
Our main finding is that screening for olfactory dysfunction could break enough transmission chains to decrease R and mitigate outbreaks. (Fig R0=1.6)

Just like our work on PCR/antigen screening, impact increases with test frequency (see thread).

In the paper, we examine whether our results hold for different assumptions about (1) the timing of olfactory dysfunction's onset, (2) its duration, and (3) its prevalence. We find that biweekly OD screening is similar or superior to weekly PCR, unless OD onset time is late. 5/
Just as @StephenKissler et al did for the early stages of viral load trajectories in the @NBA study, prospective longitudinal screening for anosmia would be of GREAT benefit to tighten up estimates of the critical parameter of anosmia's typical onset time.
We also considered whether olfaction tests could be used at point-of-entry (e.g. airport, restaurant). 3-5% of people have non-COVID anosmia, so we modeled a rapid antigen test to avoid turning away false positives. This 2-shot screening cost ~$1/person and was >70% effective. 7/
The tech that could realize our modeling looks like an index card with 5 scratch/sniff panels on it. A QR code opens a multiple choice quiz on your phone, which tests your ability to discern the smells.

It takes 60 seconds, costs $0.50, and these are easy to print at scale. 7/
Low costs mean that repeated olfactory screening could be highly cost effective, even w/ followup PCR. This plot shows epidemic mitigation simulations & estimated costs.

Even if PCR costs only $20/test, we estimate OD screening would work better & be an order of mag cheaper. 8/
Importantly, OD testing is scalable. People can self-test, there is no need for a central laboratory, and a single commercial printing facility could produce tens of millions of OD tests daily—staggering outputs when total U.S. testing is ~2 million/day, per @COVID19Tracking 9/
Still, modeling studies have limitations. Our model assumes that tests would remain sensitive over regular use—that people wouldn't adapt or memorize patterns. We also assumed no one would intentionally fail a test. Humans are complicated, and these are strong assumptions. 10/
We also assumed that a positive test would lead to isolation, but this is unlikely to be valid for essential workers, those who cannot afford to isolate, or those who just refuse.

A comprehensive testing plan means making sure anyone who wants to isolate can do so safely! 11/
Finally, let me disclose a COI here because open science is important. Toomre (2nd author), is the founder of an olfactory test company, and has filed an EUA for applications to COVID-19. Still, all of our modeling assumptions rest firmly in the established literature. 12/
With coauthors Roy Parker & Derek Toomre, we hope that olfactory dysfunction testing may be more broadly considered for COVID-19 screening & mitigation.

Our modeling strongly suggests that we should not continue to ignore this symptom of COVID-19.

medrxiv.org/content/10.110…
Finally, this is a preprint, and feedback on the paper would be valuable. There's no point in this work if it's not useful. Our email addresses are on the front page of the paper.

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More from @DanLarremore

28 Oct
Slovakia (pop 5.5M) is attempting a mass COVID-19 screening campaign using rapid antigen tests. The public health community is going to learn a lot. Here's what I'm looking for...
1/

spectator.sme.sk/c/22519165/cor…
Slovakia, like Europe, is experiencing a rapid acceleration of infections & deaths, and is starting to use curfews & lockdowns.

A pilot phase tested 140K people with rapid antigen tests, found 5.5K positives (4%).

They'll test the nation over next 2 weekends! Good idea?
2/
First, there are reasonable critiques of rapid Ag tests related to their sensitivity—do they miss too many infections?—and their specificity—do they falsely tell uninfected people that they're positive?

Re sensitivity: every broken transmission chain is a victory, BUT...
3/
Read 9 tweets
24 Oct
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
Read 12 tweets
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

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