Category: COVID through 2022

All my various postings tracking the COVID-19 pandemic through the end of 2022.

Post #807: A vastly simpler mask liner using Filtrete ™

Source for base data: 3M, ASHRAE.  See Post #593 for writeup of these and other filtration standards.

I’ve done several posts about making masks out of Filtrete ™ home air-filter material.  Each time I return to that task, I find simpler ways to work with Filtrete ™.

In this post, I’m going to show just how easy it is to take a Filtrete ™ home air filter and make some simple, flat, easy-to-handle, fiber-free pieces for use inside cloth masks.  In a nutshell, extract the Filtrete ™ fabric from the air filter and hot-glue it between two layers of the thinnest synthetic fabric you can find.

Then end-user can then cut it to size, for use as a liner for a cloth mask.  That seems to work just fine, and nothing more labor-intensive is needed.  The materials run well under $0.25 per mask liner, depending on what size filter you buy, and how large you cut your mask liners.

A few tips and tricks for doing that are given below.  Of which, the only one that might not occur to you is to use kitchen “parchment paper” as a non-stick surface as you are gluing.

Continue reading Post #807: A vastly simpler mask liner using Filtrete ™

Post #806: Virginia coronavirus trends to 9/8/2020

No material change.

Vienna (ZIP code 22180) is averaging about 2 cases a day, of late. As of 9/8/2020, based on an estimated 24,000 residents of ZIP code 22180, 17% of resident of ZIP code 22180 have been tested for coronavirus, and 1.3% of residents have been diagnosed with coronavirus. 

An unknown portion will have had it, and not been diagnosed.  Extrapolating from mid-June results of Virginia’s serology study, I’d guess that if we’re about like the rest of the state, we’d have maybe three undiagnosed cases for every one diagnosed.  If so, then about 5% of residents of 22180 have had coronavirus at this point.

Virginia (blue) remains around 1000 new cases/day.  Fairfax (orange) is around 90.

The late-reopening areas (NoVA+Richmond City, blue) continue to average just over 200 new cases a day.  The rest of the state is a little over 700 a day.

Vienna and surrounding area continue to add a few cases a day.  For the past week, Vienna (ZIP 22180) added about two cases per day.

 

Post #804: This was, unfortunately, entirely predictable if you’ve ever studied macroeconomics, and understand Keynesian theory.

Source: The Hill

I’ve been waiting to see this one, so I’m hopping on it as soon as it’s reported.  As above.  Go read the article, and see that the previous high in the trade deficit was in 2008.  During our last crisis.

Coincidence?  No.  As the night follows the day.  A predictable outcome based on standard Keynesian macroeconomic theory.

I don’t mean to keep harping on this, but if you think that President Trump’s trade policies made any sense, to anybody with a graduate degree in economics, you’re (almost entirely) mistaken.  I never even did macroeconomics professionally, and even I could see that fomenting trade wars was not a useful trade policy.  Great way to vent your anger, sure.  But nothing approaching the smart and detailed industrial policies of our Asian rivals.

It was just, well, dumb.  And if you’re surprised by that, then you’re surprised by that.  Because, probably, you’ve never looked at what a seemingly successful trade policy looks like.

And I know that your standard Republican probably spits at the word Keynesian, but that’s because nobody every bothers to look up what Keynes himself actually said.

So, a couple of quotes, to get oriented.

“Lenin is said to have declared that the best way to destroy the Capitalistic System was to debauch the currency. . . Lenin was certainly right. There is no subtler, no surer means of overturning the existing basis of society than to debauch the currency. The process engages all the hidden forces of economic law on the side of destruction, and does it in a manner which not one man in a million can diagnose.”

I’m guessing your die-hard Republicans won’t recognize that as J.M. Keynes.

Well, how about this one, then?

We are all Keynesians now.

That’s typically attributed to Milton Friedman.  Who is as close to an icon of laissez-faire free-market economics as ever existed.

And Wikipedia has it just right, associating that quote with “the reluctant embrace in a time of financial crisis of Keynesian economics by individuals … who had formerly favored less interventionist policies.”

Anyway, this isn’t rocket science.  If the US government borrows a whole bunch of money, and puts that into people’s hands, they’ll buy stuff.  See Post #783.  Much of which will come from overseas.  Hence, record-setting balance-of-trade deficit.  Matched to record-setting Federal deficit.  Yep.  Cause-and-effect.

If you’re of a mind to ask why the Federal government put that money into people’s hands, the simple answer is, it’s the lesser of two evils.  The alternative being, likely, the next Great Depression. It’s like bailing out the S&Ls in the 1970s, or bailing out the banks in the 2008 crisis.

Beats the heck out of the alternative, which is a total economic meltdown.  Really, it’s cheaper than that alternative, all things considered.  It’s the efficient solution, once you’ve painted yourself into that corner.

That’s also a lesson of Keynesianism.  Before the Great Depression, some economists believed in Say’s Law.  That boils down to “there can never be a lack of aggregate demand”.  I.e., the economy can never fail due to people not having the ability and willingness to spend.

In the opinion of most, the Great Depression proved that Say’s Law was, well, nothing of the sort.  You can, in fact, have insufficient aggregate demand for goods and services, and have that lead to an economy-wide depression. Hence, when under fire, we’re all Keynesians now.

Next, can I talk about total employment in coal mining?  Because that’s another case of shooting fish in a barrel.

Post #803: Hahaha. No, really? So make it 11/4/2020.

Cannot be rescinded. Not even questioned.

The whole COVID-19 vaccine thing just won’t quit.  We now have the officially-appointed spokesmodel saying that a November 1 deadline for having a vaccine in place is pure coincidence, and has nothing to do with election day.

If you’re just a civilian, you may not see this the way I see it.  But, if you’ve been in the game, you realize there’s a huge amount of slack in any deadline like that. 

They have no firm estimate of fill-in-the-blank here.  For example:  Of how effective the vaccine is.  Of how capable the manufacturers are of delivering large quantities by that date.  Of how capable state public health offices are of delivering it.  Of what temperature the vaccine must be kept at.  Of whether or not people are willing to get vaccinated with it.  Of who, exactly, will be eligible to receive the first doses.

And, plausibly, of which vaccine, exactly, they’re talking about.

And yet, November 1, 2020 is set in stone.  Cannot be rescinded, not even questioned.  When, to a fairly close approximation, you could avoid any question of the appearance of impropriety by postponing release to, say, a day or so after the election?

At some level, I just get tired of asking how dumb they think we are.  If you believe the party line on this, I feel sorry for you.  And I have a bridge in Brooklyn that I’d like to sell you.

Post #802: Medicare Diagnosis Related Groups (DRGs) meet the lunatic fringe.

This post is about the claim that hospitals are over-reporting a diagnosis of COVID-19 for financial gain.  They aren’t.

But the lunatic fringe (and certain Senators, apparently) have to make this claim, in order to support the equally nonsensical claim that there have been just a few thousand COVID-19 deaths in the US (addressed here in Post #793).  For the simple reason that if you admit that we’re closing in on 400,000 hospitalizations for COVID-19, it’s hard to claim just a few thousand deaths.  And so, they need a reason to claim that the hospitalizations aren’t really COVID-19 hospitalizations.

And, frankly, you have to be willfully stupid to believe that claim about deaths, because you have to ignore all the rest of the evidence in front of you.  The high death tolls in foreign countries.  The uptick in total US deaths.  The critical shortages of ICU beds, respirators, and PPE reported in many areas.  And so on.   

But what I find fascinating about the hospital claim is that it is such carefully crafted propaganda.  All good propaganda has a grain of truth.  And that’s the case in this story being told about COVID-19 and hospitalizations.  Continue reading Post #802: Medicare Diagnosis Related Groups (DRGs) meet the lunatic fringe.

Post #800: Vaccine by November 1. Who could possibly have guessed /s

Source:  Washington Post.

It’s not like the Administration hasn’t been signalling this for days now.  And, surprise, surprise, surprise, it’s scheduled for November 1.  Isn’t there some other important event that occurs in early November?  (Edit:  I forgot to mention that November 1 is a Sunday, almost as if nobody even bothered to look at the calendar before they announced the date.)

My best guess is that they’re going to offer this “emergency” authorized vaccine to health care workers only.  Nobody’s saying that, but that’s the only thing that makes good logical sense.  And that’s what the Russians did (e.g., Post #777).  The idea being that a) it’s known to be harmless, and b) even if it’s a dud (less than 50% effective, say, and so not meeting the standard that the FDA established) you can justify using it in a high-risk population if it reduces the odds of infection even a little bit.

And now some people will be offered the vaccine, and they have to come to some conclusion:  Is this real, or is it just political theater?

Let me walk though my analysis of it. Continue reading Post #800: Vaccine by November 1. Who could possibly have guessed /s

Post #799: Some simple arithmetic on herd immunity

This is just another basic math problem. Because people seem to keep forgetting the diagram above.

The point is, if you were to spread COVID-19 in Virginia as fast as possible, 13 times faster than it’s spreading now in Virginia, vastly faster than we have seen in any other state, subject only to the constraint that you don’t run out of ICU beds, the simple arithmetic says it would take a year and three months to achieve herd immunity in Virginia. 

Which, I hope goes without saying, is far beyond when we can plausibly expect to have a vaccine.

This is really just a way of re-emphasizing the related calculation I did back in Post #681 (4/4/2020).  That’s a different and more realistic calculation, and there I still came up with something like two years.

“Herd immunity” seems to be getting a lot of attention from some White House advisors.  My question is, can anyone in the White House be bothered to do even the simplest bit of grade-school arithmetic, before we end up that that as our main approach to dealing with the pandemic.

Details follow.

Let’s say that you were bound and determined to pursue a strategy of “herd immunity” here in  Virginia.  But in a somewhat humane way.  You’d arrange to have COVID-19 rip through the population as fast as possible.  Subject to just one constraint:  Not running out of any critical hospital resource.

I’ll call that the “no corpses in reefers” clause.

How long would it take?

If you look at Virginia hospital data, it appears that the ICU beds are the binding constraint.  Including newly-added surge capacity, there are just 3500 empty ICU beds.  (And if you don’t include surge capacity, it’s far fewer).

Let me make the heroic assumption that those are all staffed beds.  Because I suspect that staffing is itself more of a constraint than the beds are.  But let me just assume that away.

Right now, at our current rate of 12 new cases/ 100K population/ day, there are 266 COVID-19 patients in ICU beds.

Roughly how much higher could that daily infection rate go before we’d fill all the empty ICU beds?  Doing the long division, if ICU beds are the constraint, we could, in theory, have 13 times the current infection rate (3500 available beds/266 used at the current infection rate) before we’d keep all those ICU beds filled with COVID-19 patients.

That would mean a diagnosed infection rate of 156 / 100K / day.  (13 times our current 12 / 100k/ day).  Assuming no other constraints.  And ignoring the fact that this is off-the-charts.  Many other states have come close to hitting their hospital limits at a tiny fraction of that rate.

But, for the sake of argument, let’s just assume that “capacity” in Virigina is reached at 13 times the current rate, or 156 new cases/ 100K population / day.  And that, somehow, we could goad the population into getting infected at that rate.

Question:  How long would it take use to achieve “herd immunity”, defined has having 70% or more recovered from COVID-19?  Under that ludicrously unrealistically high rate of infection.  But ignoring the fact that there are probably several un-diagnosed cases from every one that is diagnosed.

Answer:  (70% x 100,000 / 156 = ) 448 days, or about a year and three months from now.  Well beyond anybody’s expectation of when we’ll have vaccines available.

The point is, unless you want to stack bodies up in trailers, “herd immunity” isn’t a strategy as much as it is a fantasy.  Even pedal-to-the-metal, it would take longer than a strategy of distributing a vaccine.

But what’s most annoying is that it’s a fantasy pushed by people who cannot be bothered to do simple grade-school arithmetic.  Before attempting to shape US health care policy.

Back when I worked for a US legislative-branch health policy agency (MedPAC), a colleague of mine had a name for things like this:  Policy malpractice.  If a physician screws something up in a negligent manner, you can sue them.  But if a federal official promotes negligently promotes a health care policy that’s obviously unworkable, you just have to live with it.  Or die from it.  It’s a shame that you can’t sue them for policy malpractice.

Post #798: Dr. Fauci and statistical power

See a caveat about the very short trials at the end of this posting.  They exaggerate exactly how short this trials could be, because this table is based on “normal approximation” to the actual probability distribution.

Yesterday, Dr. Anthony Fauci correctly stated the one and only way that vaccine clinical trials can end early.  (Assuming that that the FDA was serious when it said that any vaccine approved for US use must pass all phases of its clinical trials.)

If the results are extremely good (or extremely bad), they could legitimately end the clinical trail before it was scheduled to end.  Because, if that happens, they would legitimately and accurately be able to pass judgment on the vaccine, given the available data.  And so, if the results are extremely good, they could approve the vaccine ahead of schedule.

That’s totally legit.  And there is significant precedent for it.  Clinical trials have been stopped before, and drugs given approval, if they are shown to be clearly effective against some life-threatening disease.  In some sense, that’s every drug manufacturer’s dream.  Google the phrase drug trial end early and you’ll see an entire scholarly literature on this topic.

I’m putting a marker down, in the form of this posting, for several reasons.

  1. It was refreshing to see a US public official get the math right.  (Note that he said very good or very bad, which is the fully accurate statement.)
  2. Ditto, making a clear and unambiguous statement of fact.
  3. It will be interesting to see what actually happens, as election day approaches.
  4. I get the sense that we’re finally coming around to the Russian view of vaccine implementation (Post #773), only our bureaucracy can’t admit to that.
  5. I like math, and figured I’d use this as an opportunity to do some back-of-the-envelope statistical power calculations that I had been meaning to do anyway.

In other words, with point 5, I’d like to make some reasonable guess as to just how good the vaccine would have to be, to allow trials to end appreciably earlier than scheduled.  Just so I have some sense of whether they are making things up or not, if (when?) they cut the Moderna (US) vaccine trial short later this year.

And I’ve now done that, shown at the top of this post.  Fauci is absolutely right.  If these vaccines are effective, it won’t take very long to demonstrate that.  That’s what I find, calculating it from the ground up.

And so, if the vaccines are effective, and they cut the trials short, that’s not really a shortcut.  That’s legit.  And that’s not and excuse for not getting vaccinated.

And as an odd side note, the need for a statistical test effectively bars marginally-effective vaccines from being marketed.  (Assuming they do their statistical tests legitimately.)  If a vaccine is only 60% effective, you can eventually show that it’s “statistically significantly different” from the FDA 50% threshold.  But with any luck the pandemic will be over by the time you do that.

Details follow.

Challenge trials are the joker in the deck

You may have missed this little news item that came out a couple of weeks ago.  It said that the Federal government was brewing up batches of COVID-19 virus to use in “possible” human challenge trials.

Challenge trails:  When you absolutely, positively need to know right now.  If you’re really in a hurry to see if something works or not, you vaccinate, dose your subjects with the infectious agent, and see what happens.  You “challenge” them with heavy exposure to the disease, typically one that would otherwise guarantee infection in an un-vaccinated individual.

With that approach, there’s none of this waiting around for nature to take its course.  That’s more of a slam-bang, count the bodies and be done with it approach.  At some significant risk to the participants. (Although, the writeup above suggests that it would still take months to set up and run, which I find hard to believe if it were done on an emergency basis.)

Challenge trials are not a new idea.  They’re not even a new idea for testing COVID-19 vaccines.  Even some of Our Statesmen in Congress figured this out, months ago.

But, interestingly, Fauci appears to dismiss that possibility completely.  Meanwhile, “government scientists” (not otherwise identified in the news reporting) are creating the batches of COVID-19 that will only be needed if there are challenge trials.  I wonder which one is right.  And I wonder who the unnamed “government scientists” are.

But for now, I’m dismissing that possibility.  If they were doing to do challenge trials, they should have done them months ago.

Approximate statistical power calculation

In Post #774, I showed some of basic arithmetic that explains why it is so hard to test a COVID-19 vaccine.

  • It’s a deadly disease, so you can’t just take a few hundred people, give half a placebo, and expose them to COVID-19.  (That’s a challenge trial, as discussed above.)
  • Instead, you have to take tens of thousands of people, give half of them a placebo, and wait to see who gets infected in the normal course of business.
  • Given the low rate of new infections, it takes considerable person-months of time to accumulate enough infections to provide usable data.

I did the arithmetic in that prior post.  The US vaccine is aiming for a 30,000 person clinical trial.   For the sake of argument, let’s assume that they instantly enrolled all 30,000 people.  If they split that 50/50 (vaccine/placebo, good enough to make this point), at Virginia’s current infection rate (12 new infections per 100,000 population per day), you’d only accumulate about 50 infections per month in the placebo group. 

But that might vary quite a bit, just by chance.  Might be 20, might be 100.  And if the vaccine works, you’d accumulate fewer than 50/month, on average, for the vaccine group.  But that also would vary quite a bit, just by chance.  And then, based on the small difference between those small counts, you’d have to decide how well you think the vaccine is working.

That is why the people conducting vaccine clinical trials:

  • Chase down COVID-19 hotspots and recruit people there.  That gives them more infections per person-month of exposure.
  • Use huge samples (30,000 persons).  That gives them more stable numbers (more “statistical power”) for a given infection rate.

The final thing you need to know is that the FDA set a floor of 50% effectiveness, which they defined as either preventing infection or reducing severity of infection in half of cases.  A vaccine must be proven to do at least that well before they’ll approve it.

As an aside, that “or reducing severity of infection” may end up being weasel-wording of the highest order.  I know how to count infected versus not.  That’s black and white.  But as far as I can tell,  there’s literally no one legitimate way to calculate that second part about having reduced the severity of infection in X% of infected cases.   Manufacturers will have to establish an arbitrary scale of severity (e.g., death = 1, hospitalized on vent = 2, hospitalized no vent = 3, symptoms but not hospitalized = 4, asymptomatic case = 5).  And then perform some arithmetic on the counts of cases in each category, and from that conclude that they reduced the severity of infection in X% of cases.  Near as I can figure, that last part is going to be some form of hand-waving.  It will be some arbitrary method, applied to that arbitrary scale.  That opens the opportunities for presenting your data in the most favorable light possible.  To put it nicely.

In any event, the FDA 50% floor gives us the “target” for any statistical test.  The clinical trial is going to come up with an estimate of effectiveness, and an estimate of how uncertain your are about that number, the so-called “95% confidence interval”.  In everyday press, you’d see that expressed as a plus-or-minus range.  (E.g., the estimate is 60% effective, plus or minus 3%.  That would mean that, based on the data, you’re 95% sure it lies between 57% and 63%.  And if you repeated that clinical trial many times, only one time in 20 would you see numbers like that, but the true (real) effectiveness of the vaccine was somewhere outside of that range.)

There’s also a little statistical cheat you can try to use here, called a “one tailed test”.  It would be entirely inappropriate in this case (because a priori, you don’t know if you’re going to beat that 50% threshold or not), and I assume that legitimate scientists at FDA would not accept that.  So I’m basing this on a standard two-tailed test.

With all that, and putting aside the part about reducing severity, it’s easy enough to mock up the resulting statistical test.  To see just how much data you would need, in terms of person-days of exposure, to be able to exclude 50% effectiveness from the 95% confidence interval.  Assuming you were able to perform you test in a place with, say 20 infections / 100K persons/ day.

So that’s my rough back-of-the-envelope word problem.  Suppose you have a clinical trial with:

  • 15,000 enrolled
  • in an area with 20 infections/100K/day
  • split 50/50 into vaccine and placebo groups.  (The 50/50 split isn’t optimal, but this is just a rough calculation).

For a given true vaccine effectiveness of Y%, roughly how many days does that trial have to go on before you can exclude 50% effectiveness from the 95% confidence interval around your estimate of Y?  In other words, how long until the vaccine passes the test?

Details that nobody cares about but me:  I’m also going to use normal theory approximations here, because I know how to set up the problem in a spreadsheet that way.  I don’t think that matters, as long as I have well over 30 infections in both placebo and vaccination groups. And I’m modeling the FDA threshold as a known number, when in fact, it’s going to be half the placebo group number, which is itself uncertain.   I’m also ignoring the fact that the test is expressed as a ratio, which can complicate the statistics when both numerator and denominator are estimates (not known numbers). Finally, I’m ignoring that the trial doesn’t really start until the second month, because the US vaccine requires two doses, one month apart.

I built up this calculation using the standard formula for the variance of a binomial (yes/no) variable.  Then I use the normal approximation and, in effect, calculate a set of standard t-tests.  As mentioned above, that’s one of many things that’s not precisely correct about this calculation.  But it should be close enough.

Results are given below.  It’s no surprise that the better the vaccine, the less time it takes to prove it.  (To exclude 50% effectiveness from the 95% confidence interval).  What is surprising, to me, is how much overkill a 30,000 person trial is, if you expect the vaccine to be at all effective.  I think this may explain why other vaccines, such as the British (Jenner Institute/Astrazeneca) vaccine are using more like 10,000 in their clinical trials.

As an extras for experts, some of those very short intervals would provide too few infections to allow legitimate use of the “normal approximation” that I used here.  So those almost certainly overstate how short the trials would have to be, at the very bottom of the table.  The rule of thumb is, you’d want to see at least 30 infections in at least one of the groups.  At the placebo rate, then, that sets a floor of about three weeks to satisfy that additional constraint.  But the gist of this is still correct.  You only need a half-year of clinical trials if your vaccine doesn’t do much.