Contact reduction (spending time in-person with fewer people) is an extremely smart method of avoiding infection. You'll see this in the next two tables. One focuses on schools/meetings. The other is more generic. Let's see which one gets shared more.
4/
This table shows how the risk of interacting with someone with COVID increases as the size of a classroom or meeting increases, as of Nov 20.
With 10 people, there's a 15% chance someone has COVID. With 50 people, there's a 55% chance someone has COVID.
5/
This table has the same data but a more generic title. It should help people think about the typical number of people they interact with in a day.
In interacting with 10 people, there's a 15% chance someone has COVID. 50 people = 55% chance. Plane, restaurant, or theater with 100 people = 80% chance someone there has COVID.
Again, same table as Tweet #5, just framed with a different title.
6/
Many families will gather for Thanksgiving in 3 days (Nov 23). Here's how COVID risk increases with the number of social interactions.
In a family gathering of 5, there's an 8% chance someone has COVID. A big gathering of 10 = 16% chance. Two family dinners each with 10 people (approx 20 people total) = 29% chance someone has COVID. Packed restaurant of 100 = 82% chance someone has COVID. Flight of 200 = 97% chance someone has COVID.
7/
Many families will gather for Christmas and other celebrations next month. Here's how COVID risk increases with the number of social interactions.
In a family gathering of 5, there's an 15% chance someone has COVID. A big gathering of 10 = 27% chance. Two family dinners each with 10 people (approx 20 people total) = 47% chance someone has COVID. Packed restaurant of 100 = 96% chance someone has COVID. Flight of 200 = 99.8% chance someone has COVID.
These numbers are speculative at 1-month out. The model will refine estimates as we get closer and closer. Stay tuned.
8/
Here's the full PMC Dashboard for Nov 20.
You can read the full report here:
Thanks everybody for sharing across platforms and your feedback on things like axis labels, making the percentages meaningful, etc. Much appreciated. The "C" in PMC is for collaborative.
We're entering the 8th pandemic wave, likely surging to >2% infectious (>1 million cases/day) in a month.
Today's numbers:
๐น 1.41% (1 in 71) are infectious
๐น >670,000 C0VID cases/day
๐น>34,000 #LongCovid cases/day
1/
Note that the different forecasting models show high convergence.
December 11 by the Numbers:
๐น 2.25% (1 in 44) likely to be infectious
๐น >1 million anticipated C0VID cases/day
๐น>50,000 resulting #LongCovid cases/day
2/
Zooming out to the full #pandemic, there is no debate we're in an 8th U.S. C0VID wave, likely entering a "surge" in my view. That's not a word I take lightly.
There's more transmission than during 54% of pandemic days.
#MaskUp #VaxUp ๐ท๐
Today's Numbers:
๐น 1.27% (1 in 78) are infectious
๐น >600,000 C0VID cases/day
๐น>30,000 #LongCovid cases/day
We will pass the late-summer wave's peak in just over a month.
1/
The different forecasting models reach a strikingly similar conclusion about where we'll be in a month: very bad.
November 27 by the Numbers:
๐น 1.76% (1 in 57) are infectious
๐น >800,000 C0VID cases/day
๐น>40,000 #LongCovid cases/day
2/
Forecasting nuance:
Alt Model #1 (turtle) thinks the current real-time numbers are an underestimate, and it ignores the most recent week's data. Alt Model #2 (cheetah) accounts for recent errors in the real-time numbers; with low error, it maps on very closely to the real-time (red) line. The black line shows the composite used for reporting estimates. Note, everything converges in 4 weeks.
Zooming out to the full pandemic, you can see that we are entering the 8th wave.
Today, there is more transmission than during 50.6% of pandemic days. It's a coin toss as to whether any particular day of the pandemic has had more or less transmission than today. ๐ท๐ 3/
New #LongCOVID article out today in one of the top science journals, @Nature.
The most striking finding to me was that more frequent vaccination reduced the risk of yearlong LC from the 4.3-5.2% range (in their sample, which is low) to just 0.38%. That's a >10x reduction.
1/
Their sample is at the low end of #LongCOVID estimates overall, so for a fairer comparison, I could imagine 20% of the under-vaccinated group with LC & about 2% in the more frequently vaxxed group.
We could quibble on the base rates, but the risk reduction is the key stat.
2/
I always advocate for multilayered mitigation because a 0.4-2% chance of a new severe disability is considerable for individuals and populations. Effects are cumulative. Also, vaccines wane, and evading variants emerge unpredictably. Forward transmission is common. Etc.
3/
PMC C0VID-19 Tracker, Oct 23, 2023
"As Good As It Gets"
U.S. #wastewater levels are higher than during 44% of the pandemic:
๐น 1.05% (1 in 95) are infectious
๐น >500,000 C0VID cases/day
๐น>25,000 #LongCovid cases/day
Fall cases bottom out in 2 days or so. 1/
Zooming out from the 6-month view to the full pandemic, note that fall transmission is bottoming out at a high rate.
The U.S. 8th wave this winter will start to pick up soon, at a fast clip, and transmission will accelerate in December.
2/
With 1.05% of the U.S. population actively infectious (Oct 23), larger group activities continue to be increasingly risky.
Expect similar numbers the next two weeks. Then, activities will get much more dangerous.
Biobot (blue) versus Verily (black) #wastewater data.
You'll see Verily data suggest the most recent wave (#7) has had considerably more transmission than Delta (#3). And that last winter (#6) was similar (or worse!) than the prior winter's BA.1 surge (#4).
Who wins?
1/
Here are the correlations among Biobot levels, Verily levels, & IHME true cases for the 1st of each month from Jan '21 to Apr '23.
Biobot correlates r=.94 (freakish) with IHME. Verily only correlates r=.67.
Either Biobot is much better, or Verily knows something we don't. 2/
The CDC awarded Biobot's contract to Verily.
Once Verily brings on Biobot's former CDC-contracted wastewater sites, that should help. Case estimation will be easier if they fold in the historic data to more accurately represent the nation.