Post #1404: Apparently, the Governor didn’t read the law, or didn’t care.

 

In Post #1403, I noted how badly-written the Governor of Virginia’s Executive Order 2 is.  This is the order that guts all mask mandates in K-12 schools in Virginia, starting 1/24/2022.

Along with various grammatical errors, some truly awkward writing, and bizarre rationales (e.g., children’s masks collect parasites), the Governor managed to get the name of the U.S. CDC wrong.  Got it wrong, in the process of  mis-characterizing the CDC’s research on effectiveness of masks in schools.

It looked like somebody threw it together, and never bothered to check anything.  Not the grammar, not the facts, and not the logic.

Turns out, another thing they didn’t bother to check was the law of the Commonwealth of Virginia.

I’m just going to given you a reference to the reporting on this issue, because that explains it clearly.  Read it at this web page, from WJLA.

The gist of it is that the Virginia legislature passed a law last year that required schools to remain open for in-person instruction, and required them to follow the CDC’s advice on mitigating COVID-19 risks.  This was passed with bipartisan support.

On the face of it, that’s typical sound Virginia legislation.  Keep the schools open, but do it as intelligently as you can.  Burden every school district to adhere to national safety standards in this area, as promulgated by CDC.

For my last post on this topic, I looked up the current CDC guidance.  Yep, they still call for universal indoor mask use in schools.

Source:  US CDC (that’s Centers for Disease Control).

Either the Governor was unaware of the law, or chose to ignore the law, or maybe will now claim that the law doesn’t say what it plainly appears to say.

Let me lift a quote from the WJLA reporting, so you can see exactly how clearly this is stated in the law:

The bill also says school districts should "provide such in-person instruction in a manner in which it adheres, to the maximum extent practicable, to any currently applicable mitigation strategies for early childhood care and education programs and elementary and secondary schools to reduce the transmission of COVID-19 that have been provided by the federal Centers for Disease Control and Prevention."

And now, Republicans in Virginia government are claiming that the law doesn’t actually say what it appears to say.  School districts are either setting themselves up to follow the law as written, or to follow Executive Order 2, as they choose. And the net result of the Governor’s Executive Order 2 will be to sow discord and confusion, and force the issue to be settled in the courts.

Post #1400, Part 2: Endemic Omicron

 

In this post, I’m trying to guess what the world will look like after the current Omicron wave ends.

In a nutshell

  1. The consensus of informed opinion is that COVID-19 will become endemic to the U.S., just another one of the many diseases continuously circulating in the population.
  2. How that will work, exactly, nobody seems to be able to tell you.  I can’t quite get my mind around Omicron (or its successors) just fading into the background, given that it’s both extremely infectious and good at evading the immune system.
  3. Best guess, if you are smart enough to stay fully vaccinated and boostered, your overall risk from Omicron won’t be materially different from your overall risk from flu.  I do a rough cut to show that in this post, and plan to do a more systematic job of that tomorrow.
  4. Why, then, is Omicron stressing out the U.S. hospital systems in ways that the flu never does?  One key is in italics above.  That’s due to the high burden of illness among the unvaccinated.  This really is, still, mostly a pandemic of the unvaccinated.  The second factor is simply the sheer volume of weekly new Omicron cases, which I estimate to be four times the  volume of symptomatic flu cases in a typical peak week of flu season.
  5. And the upside of that is that if you are fully vaccinated, right now you are not facing risks from Omicron that hugely greater than those from flu.  In fact, most of your excess risk isn’t due to the virulence of Omicron compared to flu, it’s due to the high prevalence.  There’s just a lot of Omicron going around right now, compared to flu during flu season.  Once we get past this peak, as long as Omicron remains the dominant variant, in the long run, severe illness risk from COVID-19 risk, for the fully-protected population, should be no higher than the risk from flu.

Source:  Calculated from Virginia COVID-19 data by vaccination status, week ending 12/25/2021.


Looking past the end of the Omicron wave.

Now that Omicron is getting ready to peak in the U.S., it’s time to start thinking past the end of the Omicron wave.

If there is an end.

It appears that the overwhelming scientific consensus is that we’re stuck with COVID-19 permanently.  As in, 90% of qualified scientists thought it was going to be end up endemic here in the U.S. (Reference), just one of many diseases constantly in circulation in the population.  And that consensus dates back a year, when we were merely dealing with the native (Wuhan) strain of it, not the vastly more infectious Omicron strain.

Once upon a time, I figured the 2021/22 winter wave would be the end of the COVID-19 pandemic.  That wasn’t just wishful thinking, or mindless analogy to the 1918 flu pandemic.  My calculated guess was that by the end of this 2021/22 winter wave, nearly everyone would have been either fully vaccinated or infected.  Throw that level of immunity into your basic math for epidemics, chuck some reasonable estimate of infectiousness (“R-nought”), and presto, the pandemic should end.

That involved some wishful thinking.  But I really couldn’t contemplate the alternative.

But Omicron changed the math quite a bit.  Not only is it vastly more infectious than prior strains, it’s able to avoid existing immunity to a far greater degree.   Put those new parameters into the basic pandemic equation and it’s hard to see an end to the pandemic.

I don’t think you even need to bring up the unvaccinated to reach that conclusion.  (Although they they certainly aren’t helping things.)  My guess is that the slow decay of natural immunity over time would continuously generate enough new carriers to keep the disease in circulation, given how contagious it is.  Plus, we don’t have a vaccine good enough to put this particular genii back into the bottle anyway.

If the R-nought for Omicron is somewhere around 15, that means you have to stop 14 out of 15 chains of infection in order to bring this pandemic to a close.  If we take no other precautions against spread of disease, that would require that more than 93% of the population have perfect immunity to Omicron.  It’s not possible to achieve that when vaccine plus booster is only perhaps 70% effective in preventing symptomatic infection with Omicron.

But somehow, even though I believe the scientific consensus on this, I can’t quite get my mind around how “endemic COVID-19” is going to work. 

These are certainly examples of diseases that emerged in the U.S. over the past few decades and are still here.  (Emerged meaning that they weren’t here before.)  They are endemic — just part of the background of everyday life in the U.S.A. now.

AIDS.  Zika.  Multi-drug-resistant tuberculosis.  Lyme.  West Nile.  Legionnaire’s disease.  Dengue.  E. coli that can kill you.  Hantavirus.  Methicillin-resistant Staphylococcus aureus (MRSA).  And so on.

The trouble is, Omicron is qualitatively different from any of those diseases listed above.  And it is different from common highly-contagious diseases that we currently control with long-lasting vaccines, such as the numerous formerly-common diseases of childhood (measles, mumps, rubella, varicella, and so on).

And Omicron is qualitatively different from flu, in that it’s vastly more infectious.  A typical estimated R-nought for seasonal flu is somewhere around 1.5.  For Omicron, it’s about ten times that.

As a result, I can’t find any obvious model for how “endemic Omicron” would play out.   I can’t quite wrap my head around how the world will look with a disease that is:

  • currently quite common.
  • Airborne, so requires no vector and requires no physical contact for infection.
  • About as contagious as a disease can be (I did not come across any diseases with  estimated R-nought materially higher than 15, which is best-guess for Omicron).
  • Sometimes causes acute illness and death (more on that below).
  • Still frequently undergoing major mutations.
  • Able to bypass immunity developed from prior infections with other strains.
  • And for which vaccine-induced immunity fades with half a year.

I don’t think there’s another disease in existence today that matches those characteristics.  And so, I’m having a hard time figuring out how we could possibly have a stable, background pool of that, constantly circulating at low levels in the population.  Something about that description of an endemic disease just doesn’t quite line up with Omicron’s ability for explosive growth due to its high R-nought (infectiousness), combined with its ability to evade much of the immune system.


What happens immediately after the Omicron peak?

We can look at South Africa to see that they’ve had a fairly long “tail” to their Omicron wave.  They peaked around 12/17/2021 — just about the time the U.S. got started.  Cases fell rapidly for about two weeks.  And then the rate of decline slowed.  Four weeks after peak, they’ve still got about 25% of their peak case rate.

The U.K. appears to be following roughly the same trajectory so far.  They are less than two full weeks after their peak, and cases have fallen from about two-thirds, from 200K per day at peak to 70K per day.

If the U.S. were to follow the same trajectory, and if we’re hit our peak this week (say Wednesday 1/19/2022) at around 250 cases per 100K per day, we’ll still be looking at:

  • 80 new cases / 100K / day for around February 1.
  • 60 new cases / 100K / day around February 15.

Just for comparison, in 2021, during the mid-summer lull, we had almost two months when the new case rate never exceeded one-tenth of that.  Those were the months when (e.g.) I went back to going to the gym, and so on, due to the low risk of infection.  Months where I would say we could approach normalcy.

The point is, if you won’t feel safe until there’s relatively little virus in circulation — say as little as there was last summer — you’re going to have to keep your guard up for some months yet.

I realize that I keep talking about the peak of the Omicron wave as something to look forward to.  But, in reality, it’s just another way of saying that this is as bad as it gets.  If we follow the South African trajectory, there will still be plenty of opportunity for infection for at least a month after the peak.


And in the long run? There are too many unknowns right now.

Let me just pretend for the time being that Omicron is the final and most successful mutation of COVID-19.  And so, as the winner of that competition, that’s the one we have to live with.

If I had to pick out the single largest unknown in how “endemic Omicron” works out, it would be whether Omicron can readily re-infect people after an Omicron infection.  It’s already well-established that it has a high re-infection rate among those who have recovered from some other strain of COVID-19.  British research seemed to show that prior infection provided almost no protection against Omicron (reference).  And we’re now seeing the same sort of high reinfection rates that were first observed in South Africa.  Below is a graph from Missouri showing that almost eight percent of recent cases are reinfections.

Source:  Heath.MO.gov

But nobody knows (yet) whether Omicron can reinfect people readily after a prior Omicron infection.  (Or, if so, I haven’t seen it.)  If Omicron can readily reinfect individuals following a prior Omicron infection, then the population will never achieve much in the way of immunity to Omicron.  We might develop immunity to severe disease, but not immunity preventing any infection.

The second big unknown is how effective the new Omicron-specific vaccines will be.  One is already in production and is slated to be available in March (per this reporting).  I have not seen any data on how much more effective the new vaccine is.  (And, per this reporting, manufacturers are reluctant to jump in for fear that the vaccine will be made obsolete by yet another mutation of COVID).

Let me sum it up to this point.

  • The scientific consensus is that COVID-19 will become endemic.  That is, it will always be circulating at low levels in the population.
  • How that’s going to happen, nobody can tell you.
  • I’m skeptical that we’ll reach some nice, stable background rate.  I think the combination of airborne + extremely infectious + high levels of immune escape just begs to result in outbreaks.
  • Nobody can even start to guess what the long run will look like until we have some handle on whether an Omicron infection confers significant immunity against Omicron, and on how effective the new Omicron-specific vaccines will be.

 


Comparison of risks between Omicron and flu

Since nobody can tell you what “endemic Omicron” will look like, let me turn it the other way around.  How different are the risks now posed by Omicron and by common seasonal flu? 

I’m not ready to put up the numbers on this one yet, so this is just a teaser for a more complete analysis.  I hope to do a more refined set of numbers as the third and final post in this series.

I already looked at this issue crudely in Post #1364At that point, with that crude comparison, I could already see that the numbers were in the same ballpark.

Now I want to take the most recent U.S. data and ask a very specific question:  How different are the risks to a person concerned enough to get fully vaccinated?  So I’d like to know the risks faced by an individual who gets an annual flu shot (for flu), and an individual who is vaccinated and boostered (for Omicron).

That turns out to be a fairly involved task, because most of the data we have for either disease is for the population as a whole.  So in my final post in this series, I’m going to take the raw numbers and try to “back solve” for the risk faced by the prudent and fully-vaccinated individual.

But I can already tell you that the answers are shaping up to offer some pleasant surprises.  Mainly, as far as I can tell, the case mortality rate for Omicron, for a fully-vaccinated individual, now appears to be roughly the same as for seasonal flu.

Let me do the quick-and-dirty cut of the numbers here, to show you were I’m headed.

Start from the CDC’s estimates of the illness burden of flu, on this CDC web page.  Here, I’ve just ignored the statistical uncertainty (the 95% confidence intervals) and taken the median of values for the past ten US flu seasons.

In a typical year, in the U.S., 1.4% of persons with a symptomatic case of the flu end up in the hospital, and 0.13% die.  And there are about 30M symptomatic cases.  So those are the benchmarks for something we can routinely live with.

The question I want to ask and answer is, how does that case mortality rate compare to the average fully-vaccinated and boostered individual with Omicron?  That’s going to take some back-solving from the observed data.

But just crudely, let me pull out some mortality data from Virginia, putting a two-week lag between case counts and death counts to account for the median time from infection to death.  Here I’m looking at Virginia data broken out by vaccination status, on this web page.

For the past three weeks, the highest mortality rate observed for fully-vaccinated individuals in Virginia was 0.12 per 100K population, for the week ending 1/1/2022.  (Earlier weeks show substantially higher rates, but that’s reaching back into the Delta era.)  Going back two weeks, to the week ending 12/18/2021, the fully-vaccinated population contracted Omicron infections at the rate of 111 per 100K population.  Therefore, my two-week-lag case mortality rate for the fully vaccinated population of Virginia is (0.12/111 =) 0.11%.

Compare to the 0.13% from the table above, for flu.  It’s really not that different.

That’s one week of data, that doesn’t account for flu vaccination, and so on and so forth.  On the other hand, “fully vaccinated” is a mix of those who only have two shots, and those who have also gotten a booster shot.

So it’s a rough cut.  But I think this demonstrates that, once infected, Omicron’s risk for a fully-vaccinated person is probably just about on par with the risk from seasonal flu.

Why does the overall severity of illness from Omicron appear much worse that from flu?  Aside from a larger number of total cases, it’s due entirely to the vaccine-stubborn population.  If you’re smart enough to get vaccinated and boostered, the only excess risk you face from Omicron relative to flu arises because there’s such a high Omicron infection rate right now.  And not because the average case of Omicron has higher severity of illness than the average cost of flu, for the fully-vaccinated population.

Source:  Calculated from Virginia COVID-19 data by vaccination status.

Addendum:  But are there really vastly more new COVID-19 cases each week than there are weekly flu cases in a typical year? Interestingly, the answer is no, there are not.  More, yes.  Vastly more, no.

Right now, at the peak of the Omicron wave, the U.S. is identifying roughly 5.5 million new COVID-19 infections per week. You’d have to guess that for every identified case, there’s another one that was not formally identified.  So that would yield about 11M total new COVID-19 cases each week, in the U.S.

By contrast, the CDC estimates (above) about 30M flu cases in a typical year.  By looking at the weekly data for a typical year (I choose 2017-2018), the peak weeks of flu season typically account for 9 percent of the year’s cases.  Doing the math, in a typical peak flu week, the U.S. gets roughly 2.7M symptomatic flu cases.

The upshot of that our all-time peak Omicron week generates only about 4 times as many cases as our typical peak flu week. 

Post #1403, Why can’t Virginia be more like Florida?

 

I knew it was too good to last.  Republican mask nuttiness has come to Virginia

Our new Governor has not only rescinded a state-wide mask mandate for K-12 schools, he has barred any school district or school or school teacher from enforcing any sort of mask requirement.  Executive Order 2 (.pdf) takes effect on 1/24/2022.  At that point, there is no longer any state mandate, and any parent can demand that his or her child be allowed to attend any K-12 school without wearing a mask. Continue reading Post #1403, Why can’t Virginia be more like Florida?

Post #1400, Part 1: Omicron and luck

 

This is the first of what I expect to be three posts, trying to look past the peak of the Omicron wave.

These next posts aren’t going to very cheery, so let me gratuitously toss in this graph of how well the U.K. is recovering from its Omicron wave.  In the past two weeks, they’ve gone from 200K cases per day to 80K.

Source:  Google.

There is a light at the end of the tunnel.  We might even see the same sort of rapid decline in cases here in the U.S., once we’re past our peak.

That’s enough good cheer for the time being.  Now it’s back to business.


Will we ever admit how lucky we were, with Omicron?

We dodged a bullet with Omicron.  I’m wondering whether anybody of importance is going to admit that.  And, maybe even have some intelligent discussion about what that means going forward.

Omicron produced much less severe illness, on average, than the prior strain (Delta).  But that was entirely a matter of luck.  If the roll of the genetic dice had turned out differently, we’d be filling mass graves now instead of sending our kids back to school and trying to get on with life.

Why?  As I understand the theory of it, ability to spread is more-or-less the only significant determinant of which variant of COVID becomes dominant.  This is almost by definition. The virus succeeds by spreading.  The better it is at infecting people, the more successful it is.

  • The Alpha (British) variant was about 1.6 times as infectious as the native (Wuhan) strain.
  • The Delta variant was again about 1.6 times as infectious as Alpha.
  • Omicron is maybe 3 times as infectious as Delta.

All other characteristics of a new successful variant are essentially chosen at random.  They are whatever-happened-to-occur on the virus whose mutations made it the most infectious of its generation.  They are the random hitchhikers on whichever ride is fastest.

I want to emphasize that what I just said isn’t just the opinion of some random blogger.  It’s  mainstream scientific thinking on how viruses evolve.  The popular notion that diseases must  get “weaker” as they evolve dates back to the 1800s, and has been “soundly debunked”, per this reporting, (emphasis mine):

As evidence mounts that the omicron variant is less deadly than prior COVID-19 strains, one oft-cited explanation is that viruses always evolve to become less virulent over time.

The problem, experts say, is that this theory has been soundly debunked.

Or, if you prefer a quote from an actual science publication, try this one, (emphasis mine):

“There’s this assumption that something more transmissible becomes less virulent. I don’t think that’s the position we should take,” says Balloux. Variants including Alpha, Beta and Delta have been linked to heightened rates of hospitalization and death — potentially because they grow to such high levels in people’s airways. The assertion that viruses evolve to become milder “is a bit of a myth”, says Rambaut. “The reality is far more complex.”

The upshot is that evolution breeds successful new COVID-19 variants based on their ability to spread, but the virulence of a successful variant is totally random.  As long as most of those who are infected can walk around for a few days infecting others, what happens after that is irrelevant.  Absent an Ebola-like mortality rate, there’s no strong evolutionary pressure on virulence one way or the other.


What if we’d had a different roll of the dice?

Consider where we’d be if Omicron had merely had the same average severity of illness as Delta.   Again, just by chance.

In the U.S., we’ve reached the point where daily new Omicron cases are five times the level seen at the peak of the Delta wave:

If Omicron had the same case hospitalization rate and ICU use rate as Delta, and our behavior did not change, we would have already filled about three-quarters of all U.S. hospital beds with Omicron patients.  More to the point, we’d have filled 150% of U.S. ICU beds with COVID-19 cases.  If we had combined Omicron’s case count with Delta’s severity, we’d have run out of ICU beds a couple of weeks ago.

Source:  Calculated from US DHHS unified hospital dataset.

To a close approximation, the only reason that didn’t happen is chance.  Just plain dumb luck.  That’s all that stood between having a somewhat stressed-out cadre of U.S. ICU nurses, and mass graves for all the COVID-19 cases that needed an ICU bed but couldn’t get it.


But immune escape isn’t random at all.

I want to make just one more grim little point about COVID-19 variants.

The ability of a virus to spread occurs against a background of the existing immunity within the population.  If you’ll read the article cited above, there’s some hint that it is not merely by chance that Omicron is good at re-infecting those who had prior variants, and not merely by chance that Omicron is good at evading immunity established by existing vaccines, which themselves targeted those prior variants.  Those “immune escape” characteristics of Omicron are plausibly (though not definitively) a product of evolutionary pressures.

Just for a moment, consider where Omicron evolved:  South Africa.  In the province where Omicron first emerged, roughly 70% of the entire population had antibodies against the prior strains of COVID-19 (reference).  Omicron emerged in an environment that virtually required that the next winning COVID-19 variant be able to get past immunity to prior COVID-19 strains.

To be clear, that point isn’t just random fear-mongering.  Viral evolution to escape the immune system is part of mainstream scientific thinking.  Scientists were busily predicting the ways that COVID might achieve immune escape long before Omicron was on the scene (reference).

In South Africa, at some point in their Omicron wave, their government noted that about 8 percent of their Omicron cases were re-infections.  That was, at that time, unusual enough to merit notice.

And in the U.S.?  Near as I can tell, it’s starting to look the same.  The only state I know that had the foresight to track reinfections routinely is Missouri.  As of a couple of days ago, nearly 8 percent of infections in Missouri were re-infections (below).  That’s a radical departure from earlier periods, and so presumably that’s due to Omicron.

Source:  Heath.MO.gov

I want to put a little addendum on this, because the nut-o-verse has this fixed idea that “natural immunity” from infection is superior to what you can get from a vaccine.  So I want to be clear that these are re-infections, not breakthrough infections (infections of vaccinated individuals).  These are people who had recovered from some prior strain of COVID-19, and so had all the “natural immunity” that can provide.  And yet, they were infected all over again Omicron.

In any case, the striking re-infection rate that was noted in South Africa seems to be occurring in the U.S. as well.  And that’s probably not random at all.

On the plus side, I gather that, as with breakthrough infections of vaccinated individuals, re-infections tend to be milder than average.  Even if the virus can evade some parts of your immune system, other parts of your immune system remain primed to fight it.  As a result, a lower portion of individuals with breakthrough infection or re-infection end up with severe cases.


Summary of Part 1

To me, this good news / bad news story — Omicron’s combination of low severity and high infectiousness — reminds me of those times when NASA tells us that Earth just had a near-miss with some heretofore unknown killer asteroid.  I guess we’re supposed to feel good about that, compared to the alternative.  But a rational person can’t help but ask, “what about the next one”?

And that’s where I’ll end Part I

Post #1402: COVID-19 trend to 1/14/2022, nearing the U.S. peak.

 

The U.S. stands at 249 new COVID-19 cases per 100K per day, virtually unchanged from yesterday.  Cases are now up just 21% in the past seven days, and the national curve clearly shows the inflection points suggesting that a peak is near.

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 1/14/2022, 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.

But wait!, you might say.  With manufacturers now selling between 100M and 300M home test kits per month (Post #1397), how can you be sure this peak is real?  Maybe we’re just seeing a drop in the official counts because home testing has become so common?

One thing suggesting that this peak is real is the sequencing.  Regions are moving through their respective Omicron waves in an orderly progression.  New York was the first to be hit, and now it’s the first to peak.   Regions that started later are still in their rapid-growth phase.  That U.S. curve reflects an average of areas that are already past their peak (Northeast region), all the way to areas where growth is still near-vertical (Pacific region)

A second thing suggesting that the peak is real is the parallel behavior of new COVID-19 hospitalizations.  They haven’t quite peaked yet, but they appear to be close.

Source:  Calculated from U.S. DHHS unified hospital dataset.

And, it’s a good thing we’re nearly at the peak.  In more than half the states, COVID-19 patients are taking up more than 30% of ICU beds.  That’s what the right-hand end of the green line below is showing.

That said, there’s still no sign of the intense stress on hospital ICU beds that marked the last two major waves.  The yellow line (states with an average of 40% or more of ICU beds filled with COVID-19 patients) and red line (50% or more of ICU beds) are both flat.   So there are a lot of COVID-19 hospitalizations, but unlike prior waves, they are spread across the entire U.S. at the same time.

Post #1401: COVID-19 trend to 1/13/2022: Maybe a week from the U.S. peak?

 

By my count, the U.S. now stands at 247 new COVID-19 cases per 100K per day, up less than 3% from yesterday, and up 33% over the past seven days.

Beneath the U.S. average is a spectrum of growth rates, from outright declines in new cases in the Northeast, to near-vertical growth in new cases in the Pacific region.  Regions are peaking in more-or-less the order that they started, which has been the norm for prior COVID-19 waves in the U.S. Continue reading Post #1401: COVID-19 trend to 1/13/2022: Maybe a week from the U.S. peak?

Post #1399: COVID-19, no trend at 1/12/2022

 

For the second day in a row, total U.S. new COVID-19 case counts did not materially increase.  We now stand at 241 new cases / 100K / day, roughly the same as two days ago.  For the past seven days, the case count rose just 35%.

Yesterday’s pause was clearly mostly an artifact of data reporting.  Today, by contrast, I can point to a handful of high-population states — starting with New York and Illinois — whose true declining new case counts contributed to the stable U.S. average.

Cases are still rising in most of the country, for sure.  But a handful of places — NY, NJ, DC and now a few others — seem to be turning the corner.

Combine this with a couple of days of non-rising new hospitalizations (Post #1398), and even if this isn’t quite the peak, it’s a nice change of pace.

We’re due for it.  This is the week that the winter wave peaked last year.  And we’re now nearly four weeks into our wave, whereas both Great Britain and Canada appear to have peaked at just over three weeks.

Edit:  It now appears that the NY Times may be suggesting that we are at or near the peak, per this reference.

Continue reading Post #1399: COVID-19, no trend at 1/12/2022

Post #1398: Still looks like COVID-19 hospitalizations are flattening, despite rising case counts

 

I know you’re probably still hearing that things are wildly out of control in terms of COVID-19 hospitalizations.

All I can do is show you what’s on the data file the Feds use to track that. Continue reading Post #1398: Still looks like COVID-19 hospitalizations are flattening, despite rising case counts

Post #1397: Is home testing suppressing COVID-19 case counts?

The issue for this post is whether widespread use of home COVID-19 tests is materially reducing the official counts of COVID-19 cases.  My wife has been asking me this question for weeks.  Yesterday I got an email from a reader with the same question.  This post is about scraping together whatever I can, to try to put some bounds on the extent to which home testing is perturbing the numbers.

Bottom line:  Between September and October 2021, the gap between the official count of cases and estimates of the “true” count (based on evidence of prior infection) grew considerably.  At that time, home COVID-19 tests were probably being sold at the rate of 100M per month.  Those sales actually exceed the roughly 45M per month PCR tests being reported to the U.S. CDC.  But test sales are not the same as test use, and nobody knows the extent to which those 100M at-home tests have been used, or are merely being held by consumers.  For sure, this issue will become more important as the Federal government aims to get at-home COVID test distribution up to 300M per month by February 2022.

Lots of details follow.


There’s already a lot of slack in the official counts of COVID-19 cases.

First, you have to realize that the official COVID-19 numbers grossly under-count total cases anyway.  I’ll demonstrate that below.  So, from the get-go, you have to take the official case counts with a large grain of salt.

You know that this degree of under-count may well vary across states, over time, and across countries.  It will vary based on both the availability of tests and population’s willingness and ability to get tested.  If there is a shortage of tests — as there was in the U.S., early in the pandemic, due to the failure of the CDC’s first test for COVID-19 — then the under-count will be higher.

Further, the degree of under-count of the true number of infections might vary across COVID-19 variants.  Plausibly, most of the under-count is persons with asymptomatic cases.  If different COVID variants generate different proportions of asymptomatic-to-symptomatic cases, that should affect the under-count.  And if COVID becomes concentrated in a population with a high proportion of asymptomatic cases (e.g., children), that will also affect the under-count.

The best you can hope for is that, for some reasonable periods of time, the degree of under-count remains relatively constant, for whatever you are trying to look at.  That way, even if it’s off, as long as it is consistently off, you can make some reasonably valid comparisons over time.

The issue with the growth of home testing is whether or not it has introduce an increasing “wedge” between the true count of infections and the official count.  If so — if the gap between actual infections and the official numbers is growing rapidly due to unreported results of home tests — that will distort metrics based on the official count of cases.  That includes not just total infections, but also measures of severity such as case hospitalization rate and case mortality rate.

In short, we know the official counts are an under-count.  The question is whether the size of that under-count is rapidly increasing due to widespread available of home COVID-19 testing.


How do we know there’s an under-count?

We know the official COVID-19 case counts are an under-count based on the CDC’s national lab seroprevalence survey.  There, they use blood drawn for other purposes (e.g., routine blood panels) and test it for antibodies to COVID-19.  Presence of antibodies demonstrates a prior or current COVID-19 infection.  You can then compare the estimated fraction of the population with antibodies, to the reported official number of COVID-19 cases, to determine some measure of the under-count in the official numbers.

Last time I checked — roughly August 2021 — there was just shy of a 50% under-count, based on the seroprevalence survey data.  At that time, there appeared to be about 1.9 actual COVID-19 infections for every one shown in the official case counts.

Seroprevalence surveys are not perfect.  Putting aside the issue of whether or not the blood samples are representative of the population, those immunoassays have limited sensitivity, and immunity fades over time.  Both of those factors suggest that, if anything, the true number of COVID-19 infections has been even higher than the seroprevalence surveys suggest.

Unfortunately, CDC changed methods in September 2021.  Per their website:

Note that in response to recent data, 23 jurisdictions in the nationwide antibody seroprevalence survey switched to an assay with increased sensitivity to detect past infection in September 2021, which could impact trends.

The upshot of that is that in comparing data prior to September 2021 to the present, I can’t tell what part of that increase is real — a true increase in the undercount of infections — and what part is due to the use of a more sensitive test in the seroprevalence survey.  That leaves me with exactly two datapoints, September and October 2021, as shown below:

Source:  CDC seroprevalence survey web page accessed 1/12/2022

I’ll point out that this was a fairly stable time for COVID-19 in the U.S.  All the cases were the Delta variant, throughout the period.  The Delta wave peaked around 9/1/2021, and the winter wave had just started prior to Thanksgiving 2021.

It’s always a risk to make a lot out of two data points.  That said, per the CDC’s analysis, the purely statistical uncertainty in their estimates is quite small.  The “95% confidence interval” for October looks to be plus-or-minus one percent of the estimate.  There may be structural (non-statistical) errors — e.g., maybe their new, more-sensitive test was not fully phased-on at the start of September.   There’s no way for the outside observer to know that.

All I can say is, taken at face value, as of October of last year, there was an increasing gap between the number of infections estimated from blood antibodies in a sample of persons, and the official count of persons who had tested positive for COVID-19.

The increasing gap between actual infections and the official count could arise from any number of sources.  We can’t rule out the rise of home testing as one of them.  Yet.


The number of OTC COVID-19 test sales is larger than the number of PCR tests reported to CDC.

Fully realizing that a test sold is not the same as a test used, I have to start with some credible estimate of the rate at which at-home COVID-19 tests are being produced and sold.  The bottom line — below — is that, right now, those home tests are probably being produced and shipped at a rate in excess of 100M tests per month.

As a benchmark, there are about 45M COVID-19 PCR tests reported to the U.S. CDC per month.  (That’s my calculation, based on data from the CDC COVID data tracker).  So that’s test that were actually performed, but would not include any results from antigen tests that might be reported to the CDC.

What I want to know, for starters, is how many cheap, quick (no lab involved), at-home, no-prescription (over-the-counter or OTC) tests get sold every month.  Fully realizing that “sold” is not the same as “used”, so that’s not strictly comparable to the number of PCR tests reported to the CDC each month.

This assumes that any test that has to be sent to a lab, or is done by a medical provider, or is done at some official testing site, should have been counted in the official statistics.  This also assumes that expensive home tests would see relatively little use.

The information is piecemeal.

As of this 1/9/2022 reporting, there were 11 at-home OTC test kits approved by the FDA. (This somewhat older reporting also lists 11 tests, but it’s a slightly different list of 11).  The list keeps expanding.  Many were only approved lately for at-home (OTC) use.

Of those, per the same reporting, the following tests do not require you to mail your sample in to a lab, and are cheap:

  • Binax Now, $20 for 2.
  • Quickvue, $23 for 2.
  • Flowflex, $10 each.
  • Ihealth, $23 for 2.
  • Intelliswab, $23 for 2.
  • On/Go, $25 for 2.
  • BD Veritor, $34 for 2.

There are in addition, a few new ones that are not yet on the market, I think:

  • Celltrion Diatrust, 10/21/2021 OTC authorization.
  • SD Biosensor, 1/5/2022 OTC authorization
  • Siemens Clinitest, 12/29/2021 OTC authorization

(The last three are from various DHHS press releases).

That may not catch them all, but it’s enough to get started.  Abbott Binax Now and Quidel Quickvue were the first two rapid test approved for OTC sales, per this 12/21/2021 reporting from Vox.

The Abbott Binax Now test was first shipped to retail outlets in April 2021, per this press release from Abbott.  Their plan at that point was to produce “tens of millions per month”.  I believe that was among the first approved for OTC use.  The Abbott test was reported to account for three-quarters of all OTC test sales, per this 12/21/2021 reporting from Vox.

The Quickvue test from Quidel test sold at a rate of greater than 20M tests per month in the 4th quarter of 2021 (Reference).

Putting those two together, the two market leaders probably sold in excess of 80M tests per month in the final quarter of 2021.  (Assuming that the 75% Abbott market share estimate is correct).

This estimate is a good match for a Federal press release from October 2021.  At that time, the Federal government was expecting to increase U.S. OTC rapid test capacity from a current 100M a month to 200M test per month by the end of 2021, rising to 300M per month by February 2022.  (Source).

That increase is part of a concerted push by Federal authorities to get rapid home test to market quickly.  At least, that’s according to those same authorities.  At the minimum, they threw $70M into a fund to streamline the authorization process.  Separately, the Federal government is issuing hundreds of millions of dollars of contracts for purchase of home tests.

In summary:

  • Each month, the CDC records the result of about 45M PCR tests.  Those are tests that were actually used.
  • Each month, recently, U.S consumers have bought on-order-of 100M OTC COVID-19 tests.

So there are certainly enough OTC tests out there to be able to perturb the official data.

The big unknowns are the rate at which consumers are using those tests, and the rate at which they are coming back with positive results.  That is, how many positives are we missing due to the presence of cheap and widely available home tests.


Is there any information on actual use of home COVID-19 tests.

There are a couple of issues here, but the bottom line is no:  As far as I can tell, nobody can tell you the fraction of those home test kits that has been used.

The only way you are going to be able to estimate this is with some sort of large-scale survey.  How else are you going to know whether or not they used the purchased tests.  The question is, has such a survey been done, and have the results been made public?  No.  Or, if so, I can’t find it.

Beyond that, I don’t think it’s worth flogging the nuances of the question.  The nuances being that your behavior toward cheap at-home testing might differ from your behavior toward more formal testing approaches.

If anything, that’s more-or-less the point of the current Federal initiative pushing at-home testing.  They want people to test more often.


Summary:

For sure, Americans are buying a lot of OTC home COVID tests.  Best guess, right now, sales of those test are two to three times the volume of PCR (DNA) tests that are being reported to CDC monthly.

For sure, the shortfall between the official counts of infections and the estimate of the true number of infections via seroprevalence testing grew in October 2021.  It’s plausible, but premature, to say that that increasing gap is due to home testing.

The big unknown is the rate at which consumers are actually using those tests and finding positive results.  Near as I can tell, the only way to know that is to ask them, via a large-scale survey.  So far, I haven’t found any evidence that anyone has conducted such a survey.

The bottom line is that it’s plausible, but not proven, that the widespread availability of cheap OTC tests is suppressing the official count of COVID-19 cases.

Post #1396: COVID-19 trend to 1/11/2022. Slower growth.

The seven-day moving average of cases fell today.  For the U.S. as a whole, and in many regions and states.  The U.S. stands at 234 new COVID-19 cases per 100K population per day, up just 39% in the last seven days.  And down from a revised 239 from yesterday.

The question of the day is: Are these turnarounds in the trend lines real (and so expected to continue), or just an artifact of holiday reporting (and so, just a one-off jiggle in the line)?

Unfortunately, my answer is that this one-day decline is mostly a data reporting artifact.  New case counts may be slowing down, but there has been no true decline in new cases yet.

All this means is that the actual rate of new case growth is lower than what has been shown for the past few days.  But new cases are (probably) still increasing, and will continue to increase for a few days yet.

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 1/8/2022, 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.


Drill-down

I would love to belabor this, but the issue is simple. Every day, I show you the seven-day moving average.  Every day, that seven-day “window” slides forward.   The oldest day drops out, the newest day drops in, and you get a new average.

States sometimes skip data reporting for a few days.  Most do, on the weekends.  Most will on holidays.  Following those non-reporting days, you’ll get a big “lump” of data as they catch up with the backlog of cases that haven’t been reported.

Source:  Google.  Notes are mine.

As long as those “lumps” come at the same time every week, that’s not a problem.  But if you end up with two lumps in a single seven-day period, then the average will jump up.  In effect, the state will have crammed 9 days’ worth of cases into a seven-day window.

And so, the question is, did we see a decline today because some great “lump” of data dropped out of the seven-day window?  That’s what I would call a data reporting artifact.  Or did we see it because we added relatively few new cases today?

For the U.S. as a whole, the data reporting does not look that unusual.  The block that passed out of the seven-day moving average today (Tuesday 1/4/2022) was not unremarkable.  And the day that got added today (Tuesday 1/11/2022) was a bit lower.  And so the average fell.

But if you look at some individual states, you can see that there is a data reporting artifact for several of them.  And, unfortunately, there’s a whopping great one for Florida, which is one of our most populous states.

I spotted one for Rhode Island yesterday.  They had clearly delayed their “lump” by one day last week, and so gave an absurdly high new case rate for the seven days ending 1/10/2022.  That number has now dropped down.  Like so:

Here’s how it looks day-by-day, below.   They skipped reporting an extra day last week, and that dumped a lot of cases into yesterday’s average.  Yesterday’s seven-day window contained not one but two lumps of data.  But not today’s average.

Source:  Google

Rhode Island is too small to matter much in the U.S. average.  But, unfortunately, Florida does matter.  And Florida did the same thing.  Like so:

Source:  Google

And then, if I look a little harder at all the downturns, I find the same for Illinois, Georgia, South Carolina, and several others.  More than enough to have had an impact on the U.S. averages.

The upshot is that the 39% increase over the past seven days is more-or-less correct.  The endpoint of the line is now in the right place, and case growth is slowing.   But the finding of an absolute decline in cases today is an artifact of many states taking a reporting holiday on Monday 1/3/2022, with no matching data reporting holiday on Monday 1/10/2022.

If I had to sketch in my best estimate of the true trend, by eye, here it is.  We’ve had a significant slowing in the rate of growth, but by eye, we’re not at the peak yet.