Post #1299: Final COVID-19 update for the week, no change.

 

New COVID-19 cases continue to fall at a rate of about 12 percent per week.  There’s still no sign of a winter wave.  Alaska remains the only state with more than 100 new cases per 100K population per day, and it seems to be plateauing at that high rate.

Data source for this and other graphs of new case counts:  Calculated from The New York Times. (2021). Coronavirus (Covid-19) Data in the United States. Retrieved 10/16/2021, from https://github.com/nytimes/covid-19-data.”  The NY Times U.S. tracking page may be found at https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html.


Oh, Canada.

No hint of a winter wave in Canada either:

That matters because, in general, the Canadian and U.S. pandemics have been in sync with each other.  And, our winter wave started in and had its worst impact on our cold-climate states.  Together, those factors make Canada something of a canary-in-a-coal mine, vis-a-vis our winter wave.  But so far, there’s no sign of a winter wave there, either.

OTOH, Canada has done a better job of vaccination than we have.

Source:  Government of Canada.

Source:  CDC COVID data tracker, accessed 10-16-2021.

As with the U.S., Canada has not authorized COVID-19 vaccines for children under the age of 12.  So that’s an apples-to-apples comparison on the vaccination rate.  Makes me wonder what it’s like to live in a country where the population is somewhat more rational.  Or, at least, more willing to get vaccinated.


Britain started it.

The United Kingdom started its Delta wave well before the U.S.  And at this point the only thing that we can say for sure is that Britain is proof that vaccination rate isn’t the only factor in play.  They continue to have very high new case rates, despite a higher vaccination rate than the U.S.

They’ve plateaued at just under 40,000 new cases per day for the two-and-a-half months.  The United Kingdom has a population that is almost exactly one-fifth that of the U.S., so that’s the equivalent of 200,000 new cases per day in the U.S.  Or, if you reference the U.S. graph above, they are more-or-less living with a per-capita new case rate equal to the peak of the last U.S. winter wave.

In any case, the longer the U.S. situation situation persists — no sign of a winter wave — the less likely it is that we’ll see a winter wave.

Next, I’m going to re-do my search for counties with no apparent COVID-19 in circulation.  That’s my opening step in asking the question, if this isn’t heading toward a winter wave, where, exactly, is it heading?

Post #1298: William and Mary, five new student COVID cases this week.

Source:  Calculated from William and Mary COVID-19 dashboard.

Is there anything else worth noting?  I don’t think so.

With numbers this low, I’d love to start talking about the potential for false positives.  False positives in the sense of tests results showing the presence of COVID-19 in individuals who never actually had COVID.  If that were common enough, then maybe the trickle of cases currently being observed is actually zero true new cases, and we’re just seeing these results of some small false positive rate.

But I think that’s wishful thinking.  It’s far more likely that , in reality, there is still some low rate of infections circulating among the student body.

Let me work through the arithmetic on that as best I can.


False positive COVID-19 DNA (PCR) tests

It’s tough to talk about false positive DNA (PCR) tests for COVID-19, for several reasons.

First, all the pandemic-denier nuts come out in full force as soon as you raise the topic.  That tends to poison rational discussion when people start claiming that (e.g.) it’s all a hoax, it’s all false testing, and so on.

So I need to start this by averring that I am not a nut.  I’m just trying to run down the numbers.

Second, that aside, it’s difficult to get an estimate of the false-positive rate for DNA (PCR) COVID-19 tests, because, hey, how else can you be sure somebody had COVID-19?  In practice, a positive PRC test is taken as the gold standard for somebody actually having COVID-19.  How can you test the gold standard?

That said, the American College of Pathology (ACP) says that, in practice, COVID-19 DNA (PCR) tests have 98% to 99% “specificity”.  That is, there’s a roughly 1 to 2 percent false positive rate.  (Per this reference).  Other seemingly legitimate studies put the false-positive rate between 0.8% and 4.0% (per this reference, in The Lancet).

I’d call that the same ballpark.  How they know that, I haven’t a clue.  But two seemingly authoritative source more-or-less agree, that’s good enough for me.

Third, there’s a separate confounding issue of PCR tests flagging individuals who actually had COVID-19, have recovered, and yet retain fragments of (dead) viral DNA on and in their nasal membranes.  I have yet to see any quantitative estimate of that, but that is, as I understand it, part of the reason that they don’t want you to get re-tested once you’ve tested positive and have recovered from all symptoms.  Not sure if that’s contained within the false-positive rates cited above, or not.

Fourth, there may be a re-testing protocol for positives.  I don’t think that’s done for COVID-19, but if so — if you double-checked every positive with a second test, and required two positives in a row — that would eliminate false positives due to (e.g.) mishandling of specimens in the lab.  (It would also likely eliminate a lot of true positives, as the DNA test has a pretty substantial false negative rate.)

Finally, and relevant here, the false-positive rate depends on the pre-testing probability that disease is present.  It’s much more of an issue for screening testing — where you test everybody on a population, regardless of symptoms — than testing-for-cause (for those with symptoms or known exposure).

For that last point, I would love to have a single-sentence explanation that anybody could understand, but I don’t.  Will it suffice to say that if somebody has every symptom of COVID-19 and tests positive, there’s little reason to question the validity of the test.  By contrast, an out-of-the-blue positive result, in somebody with no symptoms and no know exposure, should be subject to a higher degree of skepticism.

That pretest-probability effect contributes to the decision not to do screening testing on populations thought to have low probability of infection (such as vaccinated students, in our case).  Aside from the cost of the test, if the actual infection rate is low enough, you reach the point where much of what you are doing is incorrectly forcing healthy people to quarantine, due to false-positive test results.

So, crudely put, false positives are mostly a problem for screening testing, in a population with a low true positive rate.

As I understand it, the only screening testing at William and Mary is weekly testing of the un-vaccinated.  Those now account for just 2% of the student population, or maybe 130 students of the roughly 6600 student residents in and around the Williamsburg campus.

For the vaccinated 98%, testing is only being done for cause.  William and Mary only tests for symptoms or known close contact with an infected individual.  That should be true even for high-risk populations such as student athletes, as the NCAA says that screening testing is not necessary for vaccinated athletes (per the “Resocialization” document on the NCAA website).  The dashboard also includes positives that reported by students who had themselves tested, without having the testing done by William and Mary.

Consistent with that, last week William and Mary performed 162 new tests, but only tested 20 new individuals.  I’m guessing that the bulk of those tests were weekly screenings for the un-vaccinated, and so did not include new (not-previously-tested) students.

If the false-positive rate really is about what the ACP said it was — maybe 1 to 2 percent — then you’d expect maybe a false positive or two per week to arise out of the screening testing.  It’s tough to say.  A lot would depend on the particulars of which test, which lab, which procedures, and so on.  Out of testing-for-cause, I’d guess the expected false positive rate would be a tiny fraction of one test per week.

In other words, no matter how I slice it, using a realistic estimate of the in-the-field false positive rate from a reputable source, I can’t come up with five false positives.

If we knew more we could probably rule it out definitively.  But given the information that is public, my conclusion is that we have to assume that COVID-19 is still, in fact, circulating at low levels within the William and Mary student population.

Post #1297: COVID-19 update to 10/14/2021, no change

 

New COVID-19 cases continue to fall slowly.  Alaska still has by far the highest new-case rate in the U.S.  We’re seeing high-but-steady rates in the upper Midwest and northern Mountain states.

In short, no change.  Still waiting for the first clear sign of a winter wave of COVID-19.

Continue reading Post #1297: COVID-19 update to 10/14/2021, no change

Post #1286: COVID-19 update to 10/13/2021, no change

The U.S. is now 47% below the 9/1/2021 peak of daily new COVID-19 cases, and new cases continue to decline at a rate of 11% per week.  Today’s count is 27.5 new cases per 100K per day, down from 28 yesterday.

Data source for this and other graphs of new case counts:  Calculated from The New York Times. (2021). Coronavirus (Covid-19) Data in the United States. Retrieved 10/13/2021, from https://github.com/nytimes/covid-19-data.”  The NY Times U.S. tracking page may be found at https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html.

No sign of a winter wave yet.  But today’s weather is newsworthy, and looks like this, per the Washington Post:

Source:  Washington Post

Just to pick a town at random, current conditions in Pocatello, ID, imply 25% indoor relative humidity at 68F/20C, for untreated indoor air.  Looks like conditions similar to that prevail across much of the northern Midwest and Mountain areas.

 

That’s well into the range that should encourage the spread of viral respiratory infections.  So, if we don’t see a significant uptick in cases within two weeks (typical lag between infection and reporting), I’m willing to call it for no winter wave this year.

Post #1284: COVID-19 update to 10/11/2021

Today’s data are a bit sketchy owing to the Columbus Day federal holiday.  About a third of states reported no data or trivial counts of new cases.  So take this for what it’s worth.

The U.S. stands at 28.6 new COVID-19 cases per 100K per day, down from 30 last Friday.  We’re now 44% below the 9/1/2021 peak.  Continue reading Post #1284: COVID-19 update to 10/11/2021

Post #1283: Final COVID-19 update for the week

 

The U.S. is now 42% below the 9/1/2021 peak of the Delta wave, down 11% in the past seven days. We now stand at an average of 30 cases per 100,000 population per day.

Data source for this and other graphs of new case counts:  Calculated from The New York Times. (2021). Coronavirus (Covid-19) Data in the United States. Retrieved 10/9/2021, from https://github.com/nytimes/covid-19-data.”  The NY Times U.S. tracking page may be found at https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html.

We’re still waiting for the winter wave to start.  If history is going to repeat itself, then the highlighted lines above should begin to turn sharply upward next week.  That’s my diagnostic for whether or not we’ll have a winter wave.


The most mis-represented number in the game right now.

That’s the fraction of infections that are breakthrough infections.  “Breakthrough” being the term-of-art for infections among individuals who are vaccinated.

Let me start with a little clarity.  Virginia updated its COVID-19 numbers “by vaccination status”, to the week ending 10/2/2021.  They look like this:

Source:  Calculated from Virginia Department of Health COVID-19 dashboard data.

Now, before we go a step further, let’s note the obvious.  Note the GREAT BIG SECTION OF THE PIE that is attributable to those who are NOT VACCINATED.

Obvious, right?  Last week, in Virginia, 82% of infections were among un-vaccinated individuals.

A less dramatic but more informative way to present the data is to show both the fraction of the population, and the fraction of infections.  This way, the intelligent reader can correctly infer that INFECTIONS ARE GROSSLY AND DISPROPORTIONATELY CONCENTRATED AMONG THE UN-VACCINATED.

Source:  Calculated from Virginia Department of Health COVID-19 dashboard data.

Again, not exactly rocket science to deliver a clear and unambiguous message.

The problem is, this is straight-up dog-bites-man messaging.  Vaccines are supposed to prevent infection, and they do.  Ho hum.  That’s hardly news.

And, to be clear, this is more-or-less the way the state-level numbers look everywhere.  And that’s because, once again, the COVID-19 vaccines work fairly well, regardless of location.

So not only is it boring, it’s the same everywhere you look.

In the modern world, that’s insufficient click-bait.  That doesn’t bring the eyeballs that fuel the advertising revenues that make the news industry run.

And so, inevitably, headlines for half the articles about breakthrough infections have to make it seem as if vaccines don’t work.  The stories themselves always seem to present the facts.  But the headlines come out of a different universe entirely.

I don’t know if the editors who create those headlines really are sympathetic to the nut-o-verse of anti-vaccine forces, and want to give them some (non-factual) basis to sustain their beliefs.  Or, maybe they’re just scrambling for advertising dollars, and figure the contrarian headlines suggesting that vaccines don’t work will help that.  Or maybe it just tickles their fancy to fashion a headline that’s so clearly contrary to the content of the story.

There is a serious public health issue here, beyond the rate of vaccination.  Immunity fades over time, and booster shots will become increasingly necessary if you want to maintain an optimal level of protection.

But, as is clear from the simple graphs above, that’s a far cry from saying that the current COVID-19 vaccines don’t work.  In my opinion, every state health department ought to be producing their own version of the first graph above.

In a world where we all get our information from the internet, sometimes you really need to slap people in the face with the simple, obvious, and correct story.  Because you can be sure that, intentionally or otherwise, somebody else is out there doing their best to muddy up the waters.

Post #1281: COVID-19 trend to 10/7/2021.

 

Source for featured image:  groundhog.org.

The U.S. stands at 30.6 new COVID-19 cases per 100,000 population per day.

That’s down 40% since the 9/1/2021 peak of the Delta wave, and down 11% over the past seven days.

That’s about the extent of what you’ll see in mainstream coverage of this.

And while that’s true, it’s not the entire story.  Three regions are still at the peak of their respective Delta waves.  Those are the ones to watch now. Continue reading Post #1281: COVID-19 trend to 10/7/2021.

Post #1280: COVID-19 trend to 10/6/2021, Virginia K-12 school opening analysis, no change.

 

Currently, the U.S. as a whole is seeing 31 new COVID-19 cases / 100K / day.

Daily new COVID-19 cases for the The U.S. are now 40% lower than at the 9/1/2021 peak of the Delta wave.

That 40% decline since 9/1/2021 is made up of:

  • Three regions (South Atlantic, South Central, Pacific) where daily new cases are down by about half.
  • Three regions (Northeast, Midwest, Mountain) where daily new cases are roughly unchanged.

Continue reading Post #1280: COVID-19 trend to 10/6/2021, Virginia K-12 school opening analysis, no change.