Category: COVID through 2022

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 11/24/2021, 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.

Case counts are rising slowly, and are rising in ever region except the Pacific states.

Tomorrow we begin our holiday season.  I’ve set up the second graph below so that I can track case counts relative to what happened during last year’s holidays.  Thanksgiving is one day earlier this year.

Up to now, I’ve been characterizing this year’s winter wave as “late”, compared to last year, likely owing to warm October weather in much of the country.  Best guess, based on mean October temperatures for the U.S., and the typical Fall month-to-month rate of temperature decrease, maybe two or three weeks late.

That may be true, but after this past week of slow growth, that’s starting to wear a little thin / stretch a little far / pick your own metaphor, as a complete description of what’s happening.

If the 2021 winter wave were a duplicate of 2020, just a couple of weeks later, we’d see the orange line above eventually paralleling the blue line.  Instead, ,that gap between last year’s line and this year’s line continues to grow.

Restated, either this year’s winter wave is much later than last year’s, or we have a generally lower rate of growth in cases this year, compared to last year.

We also have a more-or-less a complete lack of a winter wave in three states that were leaders of the winter wave in the Mountain region last year (ID, MT, WY).

Now that I look at it, the three states that led the Midwest region in last year’s winter wave have also mostly failed to get with the program this year.  Shown below.  It’s not as clear-cut as the leaders of the Mountain states — there is a slight upward trend in two of the three — but it is nevertheless a strong contrast to last year’s experience.

I’m not sure what to make of all that, yet, but given that it’s the holidays, I’ll try to end on an upbeat note.

Maybe we are due for a repeat of last year’s wave.  The same as much of Europe is suffering through right now. Just a bit later.

On the other hand, owing to our exceptionally high rate of infection in the past, and to a middle-of-the-road vaccination rate, maybe immunity in the population is now high enough to allow is to avoid another awful winter wave.

(Awful?  People forget.  For nearly the entire month of January 2021, we had an average of more than 3000 COVID-19 deaths per day.  As a result, for that period, the total U.S. death rate increased by more than one-third above the norm.)

Near as I can tell, we ought to be pretty close to “herd immunity”, for something as infectious as the Delta variant of COVID-19.  That’s defined as having enough immunity in the population (via infection or vaccination) to be able to suppress the spread of a virus with basic reproduction rate (R-nought) of 5.0.  (Where the average infected person would go on to infect five others, absent any interventions or immunity).  The table below shows 77%, where we’d need a value of 80% or more to suppress spread of a such a virus with no other measures taken (e.g., without wearing masks).

I have to make a lot of assumptions in that calculation, as it depends on a lot of things that cannot be observed.  For example, it doesn’t account well for the rate at which immunity fades over time.  It doesn’t account for the potential for re-infections (Post #1326).  And so on.

But I think this is enough to show that we’re in the ballpark.  Right now, infectiousness of Delta is rising owing to changing weather conditions.  So it would not be a surprise to have some winter wave.  But we ought to be close enough to herd immunity now that maybe we won’t have much of a winter wave.

Let me put it this way.  Let’s assume Spring is going to occur at roughly the same time this year as it did last year.  At the minimum, the heart of this year’s winter wave will be shorter than last year, reducing the total he total time we’ll spend at those very high winter infection rates.  And maybe we have enough immunity that the peak of this year’s winter wave will be lower as well.  Further reducing overall impact.

No matter how I slice it, it seems like we’re in for a “smaller” winter wave this year.  Either shorter in duration, or shorter in height, or, ideally, both.

Maybe we can take some inspiration from our northern neighbor.  For sure, if cold weather is the issue, they’ve got that covered.  And, while they have a much higher overall vaccination rate, but we beat them in terms of cumulative infections per capita.  So we’re not that different in terms of total population immunity.

And yet, compared to last year, so far, there’s not much of a winter wave happening there. Whatever it is that’s suppressing the start of the U.S. winter wave, it apparently is not limited to the U.S.

 

 

 

 

We’ve reached the part of the year where we’re only going to get glimpses of the actual U.S. trend in new COVID-19 cases.  Between now and the second week of January, we will observe a mix of:

• the true long-term trend of new case counts,
• the lack of data reporting on weekends,
• the even greater lack of data reporting on holidays,
• reduction in test-seeking (and case counts) over holidays,
• any actual impact of the holidays on new COVID-19 infections.

Today is a case in point.  When I left this last week (Post #1326), there seemed to be a sharp uptick in cases.  Now I can see that was temporary.

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 11/23/2021, 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.

The trend is still up, but at a much slower rate than appeared to be the case last week.

And that’s a good way to illustrate the potential data-reporting impacts of the holidays.  Holidays?  There were no holidays last week.  How could holidays have possibly affected this apparent slowdown in new case growth?

In part, the trend reflects a true slowdown in case growth, in states such as Michigan and many of the New England states.  But it also appears slower because I goofed:  I forgot about Veterans’ Day (11/11).   The non-reporting of cases for the Veterans’ day holiday, combined with the use of a seven-day moving average, provides a boost to the apparent growth rate on 11/18.  That’s the date on which the under-reported Veterans’ Day case count finally moves outside of the seven-day window, leaving only the additional cases that were reported on the day following Veteran’s Day remaining within the seven-day window.

If you failed to follow that last bit in every detail, that’s fine.  Suffice it to say that holidays scramble the numbers.  We’re entering the holiday season.  And so the numbers are going to be scrambled for the next few weeks.

We’re in the same situation as last year, absent the hindsight that allowed us to fill in the underlying trend after-the-fact.  Below is the graph of the U.S. new case rate, from last year’s holiday season.  Imagine trying to guess what the actual trend would be, at each step of the way.  That’s where we are for the coming holidays.  The two large “dips” in the line below — known only after the fact — are the large data reporting artifacts of Thanksgiving and Christmas/New Year’s.

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The 2021 winter wave and popular press reporting.

First, I want to continue to highlight the behavior of ID, MT, WY.  These were leaders in the 2020 U.S. COVID-19 winter wave.  But this year, they appear to have peaked at the end of the summer Delta wave.  As yet, there’s no indication that they’ll have a winter wave this year.  I don’t know whether that’s a harbinger for the rest of the country, or whether they’re just a little late getting started.  Or whether it’s just something unique to those states, this year.

The South seems to be split along temperature lines.  States bordering on the Mid-Atlantic region and Midwest are showing slight upticks.  But south of Virginia on the Atlantic Coast, right on through the Gulf Coast, there’s no apparent increase in cases.  (This isn’t very different from last year, where those regions had late peaks of the winter wave.)

Finally, here’s the U.S., comparing the first and second pandemic years.  Aside from being a couple of weeks late, and missing part of the Mountain states, we still seem to be on track for a winter wave.

If you’ve stuck with this post, this far, you’ll probably understand why this graph above — the one that compares this year to the same time last year — is the only one worth looking at for the next six weeks or so.  The hope is that this year’s holiday “artifacts” are about the same as last year’s.  Which means that the only hope of making sense of this year is to compare it to the same period last year.

I’m guessing that most popular press reporting isn’t going to bother to do that.  But if you don’t see the comparison to last year, you really can’t make head-or-tail out of what the most recent trend has been.

For example, we should now expect to see a substantial decline in the new case counts, owing to the Thanksgiving holiday.  (Which never generated a “surge” in cases, despite what you may have read to the contrary (Post #1324, Winter Wave Buzzword Bingo)).

By itself, that decline is meaningless.  Only in the context of last year can we start making some judgments about where the trend is actually heading.  Look for the popular press reporting that does that, and you’ll have put your finger on the people who understand how to make sense of the numbers.  Ignore the rest.

Map courtesy of datawrapper.de

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 11/18/2021, 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.

The U.S. winter wave continues apace, except in the Mountain region.  As you can see, the northernmost mountain states now form sort of a “hole” in the northern tier of U.S. non-Pacific-coast states.

 

Last year’s winter wave was led by the Midwest and Mountain regions, both of which peaked sometime around Thanksgiving.  This year, by contrast, while the Midwest is now rising rapidly, the Mountain region seems to have stalled (circled above).

Within the Mountain region, instead of any uniform trend, it’s a mixed bag.  As shown below, three states appear to have peaked.  Those are the same states that are circled on the first map graph above.

The three northernmost Mountain states are now showing a downward trend.  Based on my counts, it doesn’t look like they’ve hit herd immunity.  So what’s going on?

Turns out, it looks like they’ve had their winter wave.  Tough to say why, exactly, but it looks like the end of their summer Delta wave simply ran into and effectively became their winter wave.  Note that there’s no end of the Delta wave for those states.  Cases continued to climb from July all the way through to early October.  And has been noted in earlier posts, Idaho already had to declare “crisis standards of care”, allowing physicians to ration hospital beds to those who seemed most likely to survive.

The upshot is that instead of a distinct winter wave with a sharp peak late in November, their summer Delta wave just continued, with the result of a broad peak spanning most of September and October.  If I had to guess, I’d guess they’re done for the year.

The rest of the Mountain states, by contrast, had a low peak in early September, consistent with the end of the summer Delta wave.  And they’ve (mostly) started on a new Winter wave.  Those states don’t appear to be done yet, for 2021.

Time and again, the pandemic has run right up to the point where hospitals are full, and then stopped.  It’s as if there’s only a finite subset of the population at risk of infection at any one time.  And once they’ve been infected, the pandemic runs out of steam for a while.

And so, in those three northernmost Mountain states, it surely looks as if the summer Delta wave worked its way through all the infection-eligible population.   For all intents and purposes, it looks like the pandemic has simply run out of steam, in those three states, for now.  And so, there won’t be a  winter wave cresting around Thanksgiving.  Instead, they had two months of high case loads under Delta, leading to (among other things) crisis standards of hospital care in Idaho.

Maybe that bodes well for the entire U.S.  Maybe we’re finally reaching the point where there’s just some modest subset of the population left to be infected.  And so instead of a rip-roaring winter wave, as we had last year, maybe we’ll all go the way of WY, MT, and ID.  Maybe the people still capable of being infected will end up so thin on the ground that we’ll have just a modest peak this winter.

 

Looks like the U.S. is stuck on repeat, in terms of a COVID-19 winter wave.  I suppose that in addition to the case counts, this probably means a replay of much of the nonsense reporting we saw last winter.

In the interest of efficiency,  in this post, let me try to handle some of the known nonsense in bulk, rather than piecemeal.  We may see novel forms of it evolve.  But at least I can efficiently dispose of the stuff that’s a straight-up repeat from last year.

To be clear, this post is all about things that are wrong.  But that nevertheless make great click-bait, or allow people to feel all righteous about something, or otherwise seem to stick around despite clear evidence that they are incorrect.

This underscores yet again the fundamental problem with America today:

1. Every citizen feels the unalienable right to have a strongly held and firmly expressed opinion.
2. Nobody feels even the tiniest obligation to do their homework.

Hence, this list of pre-debunked items from last winter.  I’ve already done the homework so you don’t have to.

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1:  Post-holiday surge.

So far, we have never had a post-holiday “surge” in cases in the U.S.  Never.  Not even once.  Not post-Thanksgiving.  Not post-Christmas. Not post-Superbowl, Memorial Day, 4th of July, Labor Day. Not not post-fill-in-the-blank.  Never happened.  Never never never never never.

And yet, you will almost surely hear solemn and dire warnings about the expected post-holiday surge in cases this winter. 

When you hear that, bear two things in mind.

1)  Nobody except me has ever bothered to define what “a surge” means.  A “post-holiday surge” is a coordinated increase in daily new COVID-19 cases, above the pre-existing trend, occurring in all or most states, roughly two weeks following the holiday.  (That last clause is consistent with the average lag between date of infection and date of case reporting.)

2)  Consistent with that, I seem to be the only person who has actually bothered to look at the data, after the fact, to see whether or not a surge actually occurred as predicted.  Among the reasons that you’ll never see that in the newspapers is that you have to wait two weeks to see whether or not a surge occurs.  That’s far longer than the attention span of the American people and their news media.

The data-driven part of this is not exactly rocket science.  U.S. reported COVID-19 cases fall on the holidays because state health departments are closed. Then (most of) the backlog of cases gets reported, and then cases return to their prior trend.  Not just once.  But over and over, holiday after holiday.

We have never had new COVID-19 cases surge, or jump, or suddenly rise after a holiday, so some level beyond the pre-holiday trend.  Never.

Below, you can see that I looked for those post-holiday surges, really hard.  Rather than walk you through it, I’ll refer you to many prior posts that go through this in detail for Thanksgiving, Christmas, and most specifically, for the Superbowl.

• Post #915, still no post-Thanksgiving surge.
• Post #916, what about that huge post-Canadian-Thanksgiving surge?
• Post #919, still waiting to see that surge.
• Post #922, Thanksgiving, calling it for no surge.
• Post #929, a footnote to the Thanksgiving non-surge.
• Post #948, no surge for Christmas or New Year’s, either.
• Post #1029, Post-Superbowl non-explosion of cases.

And Columbus Day.  Labor Day.  Labor Day again.  You get the drift, I hope.

Near as I can tell, all of this “post-holiday surge” nonsense dates back to a single epidemiologist who incorrectly asserted that there had been a huge surge in cases following Canadian Thanksgiving.   Why?  Because case counts were higher the week after Thanksgiving than the week before it.  But a) it takes two weeks for new infections to enter the data and b) that same statement had been true for every week so far that winter, up to that point, in Canada.  (Because they were in their winter wave, and cases were increasing week-after-week.)

No matter how obviously stupid and illogical that “surge” conclusion was, that has stuck.  And every holiday since, you’ve heard all the talking heads tut-tutting about the dangers of holidays and the inevitable post-holiday surge in new cases.

Accordingly, my favorite post-holiday-surge graphic is this one, suitable for the many Americans who know nothing about Canadian culture:

Posted on December 13, 2020

What we actually get with major holidays, first and foremost, is a steep reduction in reported cases because state governments shut down.  And, oddly, we also get small, permanent reductions in actual total cases.  Seems as if a lot of people just don’t bother to get tested over a holiday.

This is followed by a return to the prior trend, as all that gets sorted out.  What we never get is a surge in cases, above prior trend, two weeks after the holiday.

Finally, if you want to see what a surge actually looks like, try Post #920, Now That’s a Surge.  That’s just a summary of the past two decades’ worth of U.S. flu seasons.

If you wonder why we never get a surge, consider how one-sided the pundits’ discussion of the holidays is.  Two things happen on major holidays.  One, work stops.  Or at least, slows down for most of us.  Two, families gather.  All you ever hear about from the talking heads is the second aspect.  But, arguably,  the reduced risk from time off work may more-than-offset the increased risk from family gatherings.

 

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2:  Massive increase in air travel for the holidays.

Ah, just see Post #939.  Here’s the graph of actual TSA passenger screenings at U.S. airports.  Spot the huge holiday-related surges, please:

This is the fourth quarter of the year, and the blue line is 2020.  The thin wavy line is the actual  daily data, and the thicker line is a seven-day moving average.  So, yes, there are more airline passengers in the holiday season.  But the increase is only about 100,000 per day above the baseline.  Against a U.S. population of 330,000,000, any modest rate of infections occurring on those flights is just rounding error.  I went through all that arithmetic in Post #939.

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3:  Predictions of smooth sailing from now on.

So far, official predictions of the course of the pandemic have had poor accuracy.  I’m hardly the first person to note that.  Or to have shown that with some degree of formality.

In the first year of the pandemic, when you’re still trying to figure things out, that’s forgivable, I guess.  But now?  When we have more than a year’s worth of experience?  When a few simple factors such as indoor relative humidity appear to explain the Southern summer wave and Northern winter wave?  When state after state goes right to the point where hospital beds are completely filled, but rarely goes much beyond that?

By now, it seems like we pretty much ought to have a fairly good grip on the likely ups and downs.

Arguably, some of the worst predictions have come from CDC, from the consortium of modelers that they tasked to provide some sort of official prediction.  Let me just repeat the last one, which is that there would be no winter wave this year.  That prediction was made in September.

Source:  Cited just above

These predictions suffer from the same basic problem as “the surge”.  Our collective attention span is so short that no news agency can be bothered to look back and check the accuracy of the forecast.  And so, assess the forecaster’s history of inaccuracy.  Before presenting the next forecast.

As a result, the same forecasters who repeatedly provided poor predictions in the past remain free to provide equally poor predictions going forward.  And whenever those predictions match something that somebody wants to hear, you’ll see them in the popular media.  And the long track record of being incorrect will just be swept under the rug.

The CDC-organized group that made the projection above, in early September, is slated to redo their projections again in December.  Without a doubt, given the CDC imprimatur, whatever they say will be duly reported in the news.  And equally without doubt, their incorrect prior predictions will simply be ignored.

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4:  Florida has fewer cases than fill-in-the-blank now, thus proving fill-in-the-second-blank.

I went through one of these in Post #1319.  The current Fox push seemed to be “thus proving that masks don’t work”. I’m sure at some point that second blank will morph into “vaccines”, or maybe “Biden”.  Whatever fits the need of the moment.

All of that is aimed at people who are too stupid and lazy to take literally one minute to look at the actual data.

All you have to do is Google “COVID Florida”, and Google will provide you with a graph of new COVID-19 cases in Florida over time.  Barring that, Googling “NY Times Florida COVID” will give you a graph of COVID-19 cases in Florida over time.  Either of which could then be compared to (say) California.  In either case, you’d see that most of the time, Florida has exceeded California in terms of daily new case count.

It’s not as if we lack for ready sources of the data, available with absolutely minimal effort.

It’s just straight up Rule 1 and Rule 2 above:

1. Every citizen feels the unalienable right to have a strongly held and firmly expressed opinion.
2. Nobody feels even the tiniest obligation to do their homework.

Normally, I would add a third rule:

3:  And nobody cares whether or not they are right on the facts.

But Rule 3 is a misstatement.  Near as I can tell, it’s far worse:  Nobody wants to see facts that might contradict a firmly-held belief.  It might make then have to think, and thinking is hard.

And  so, as outlined in Post #1319, any set of statistics that varies over time or in a cross-section becomes a ready propaganda tool.  Wait until the numbers are leaning your way, add whatever semi-attached figure you wish, and loudly proclaim that the data make your point.  When they lean the other way, say nothing.  And you can reliably count on the laziness and stupidity of your audience to do the rest.

It’s cheap, simple, and effective.  Of course it’s going to be re-used this winter.  To stop it, people would have to wise up.  And I predict that is unlikely to happen, based on our experience to date.

Source:  Colorado Department of Health, accessed 11/11/2021

“Vaccine hesitancy” gets my vote for the most misleading phrase in the popular press of late.

I don’t perceive the least bit of hesitancy or ambiguity among those who refuse to get a COVID-19 vaccine.  How does “hell no, I won’t get vaccinated under any circumstances” get characterized as “hesitancy”?

I blame liberals.  Liberals remain the naïve children of the Enlightenment.  Despite strong evidence to the contrary, they cling to this crazy belief that people are, at heart, fundamentally good and reasonably rational.  That they will, by and large, make informed choices for the benefit of themselves and society at large.  Within that fictional world-view, those who turn down a free, more-or-less harmless shot that reduces odds of an early death and could end the pandemic for society at large — those people must merely have different values and beliefs.  Perhaps they’ve assessed the facts differently, and have some reasonable doubts or fears regarding the efficacy of the vaccine or likelihood of true side effects.  And so they hesitate to get vaccinated.

In this liberal fantasy world, the unvaccinated are just unenlightened, or have made alternative, rational choices. They hesitate to get vaccinated.  Perhaps they can be led to see the light with proper guidance and education.

But back in the real world, nope, they’re just stupid and stubborn.  No amount of appeal to reason can fix that.  So if you want to get the entire population vaccinated, you’re going to have to resort to some level of coercion.

It’s actually completely unfair to blame “liberals” for this wishy-washy misleading term for the unvaccinated.  The true story is that “vaccine hesitancy” has been a term-of-art in public health circles for decades.  The earliest Wikipedia page on it (under the current Wikipedia page on vaccine hesitancy) dates back to 2007.  A quick search using date ranges on Google shows that the phrase appears to have emerged in the 1990s (and/or any references to it prior to 1990 aren’t captured by a Google search).

The true story isn’t that “vaccine hesitancy” is some polite new buzz-phrase, cooked up by the liberal media.  It’s just a mis-application of a bit of standard public health jargon. 

That said, surely we need something that’s a more accurate description of the current circumstances.  “Hesitancy” just doesn’t cut it.

At the very least, it should be “vaccine refusal”.  Just to make it clear that these people have been given a choice, and they’ve turned it down flat.  And that they own the consequences of their decisions.

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Colorado declares crisis standards of care for hospitals.  Nobody notices.

But why bother with the whole vaccine thing, at this point?  I mean, everything’s OK, right?  More-or-less?

Well, no.  That brings me to crisis standards of care.  That’s the formal, legal declaration  by the governor of a state that, owing to a shortage of hospital beds, physicians may triage patients/allocate hospital care based not on need, but on likelihood of survival.  It provides the legal cover for physicians to allow individuals to die for lack of hospital care, because there simply aren’t enough hospital beds (or ICU beds or respirators or whatever) to meet current demand.

The potential for governors to invoke crisis standards of care has been on the books for years.  It’s a reasonable and rational part of medicare emergency preparedness.  It went hand-in-hand with pre-established CDC rules for the substitution of sub-standard PPE for normal hospital PPE in the event of a shortage. Rules that (I believe) were never invoked prior to the COVID-19 pandemic.

When Alaska and Idaho made formal declarations of crisis standards of care, that made the news.  It was judged to be a fairly significant event, that a U.S. state had reached the point of letting people die for want of hospital beds.

Only in Alaska was the effect of the hospital bed shortage obvious enough to be clearly visible on a graph (Post #1269).  You can see that in the two-month-old graph below.  Cases spiking (red), admissions declining (yellow).  In Idaho, by contrast, they largely managed to slough the problem off onto hospitals in eastern Washington.

Source:  CDC COVID data tracker, accessed 9/19/2021.

But at least that made the headlines.

Now it’s reaching the point that when another Western state runs out of beds, people hardly notice.  And the case in point today is Colorado.  They declared something like crisis standards of care last week.  And if my wife hadn’t picked up on it, based on a single New York Times article, I surely wouldn’t have noticed.  Near as I can tell, that’s the sole reference in mainstream media.

Running out of hospital beds has become the new normal.

There’s an oddity, in that the wording of the Colorado executive order is different from others.  (I’d better provide a link to the actual executive order, because in the nut-o-verse this has been characterized as requiring hospitals to refuse admission to unvaccinated individuals.  Whereas the actual executive order says nothing of the sort.)

It says ” … Order authorizing the Colorado Department of Public Health and Environment (CDPHE) to order hospitals and freestanding emergency departments to transfer or cease the admission of patients to respond to the current disaster emergency due to coronavirus disease 2019 (COVID-19) in Colorado.”

And, to be clear, it not only empowers the Colorado DPHE to stop admissions at some hospitals, it requires other hospitals to take those admissions if DPHE so directs it.  In effect, it gives the Colorado DPHE the right limit admissions and to re-allocate hospital admissions across the entire Colorado hospital system.  Not just to deny admission to certain hospitals, but to require other hospitals to accept those admissions.

If you read further, you’ll see that this particular language is taken directly from Colorado state statute.  And, as an extra for experts, this is all to get around the requirements of EMTALA, the Federal law that prevents hospitals from “dumping” undesirable (that is, uninsured) patients.  Hence the “transfer or admission” phrasing of the executive order.  The oddity of phrasing doesn’t necessarily reflect a different view of how best to triage patients.  It’s an artifact of how Colorado state statutes were written, and in turn, how the Federal EMTALA law was written.

Just to be completely clear, the declaration doesn’t even mention COVID-19 or vaccination status.  (So the claims in the nut-o-verse that this requires hospitals to deny treatment to the unvaccinated are completely fictional).

In fact, the bulk of the document pertains to insurance issues.  (Only in America, right?)  It notes that hospitals cannot take a patient’s insurance status into account, and that patients will still be insured even if sent out-of-network on an emergency basis under the provisions of this law.

But at this point, I need to cease ragging on Republican governors.  Because, despite the surge in cases, and the fact that Colorado hospitals are full, the Democratic governor of Colorado won’t do anything more than issue this order allowing the state health department to transfer cases across hospitals.  Mask mandates appear to be local-option only.  Near as I have been able to tell, bars and indoor restaurant seating remain open with no restrictions.

With that, about half the people in Colorado report wearing masks now.  That’s unchanged since the first of September.  So a little thing like running out of hospital beds doesn’t seem to be enough to affect behavior there.

Source:  Carnegie-Mellon University COVIDcast.

In any case, unlike Alaska at its peak, there’s no indication of any outright denial of hospital care yet, in Colorado.

Source:  CDC COVID data tracker accessed 11/11/2021

 

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Coda:  An un-funny anecdote about Medicare Durable Medical Equipment.

Or:  Why COVID-19 in Colorado is distinctly different from COVID-19 in Louisiana.

I used to be a self-employed consultant in the area of health economics.  One day I was tossed the following problem:

In the Medicare program, at that time, there was a more than five-fold difference across the states in spending for home oxygen.  Worse, there was no indication of any difference in need for home oxygen.  There was almost no difference in prevalence of the main disease for which these rentals would be authorized (Chronic Obstructive Pulmonary Disease (COPD), which used to be called emphysema.)

That sort of thing is a big red flag for Medicare.  When they see massive differences in service use or spending, and no differences in the underlying health of the population, they immediately investigate for waste, fraud, and abuse.  In this case, my client — a manufacturer of oxygen concentrators — was rightly worried that this was going to affect his business.

At first, the Government’s case looked pretty good.  The scatterplot of oxygen use against COPD prevalence showed only a weak association.  There really was a lot of spending variation that appeared unrelated to prevalence of the relevant illness.  In particular, prevalence of illness did nothing to explain the high spending outliers.  (That is, the dots net the top of the graph below.)

Next, that spending variation was large.  The high-spend states really did out-spend the low-spend states by a factor of five or more.

But somewhere along in this process, I managed to recall that the Denver Broncos used to play in Mile High Stadium.  And then it all fell into place.  Those states at the bottom of the list all have one thing in common:  They are Mountain region states.  There isn’t much oxygen there.

When I arranged the same set of states by mean elevation above sea level, I got a much more orderly plot.  Suddenly, those big outliers made sense.

Medicare was supplying a lot of oxygen in those states because Mother Nature wasn’t.  Here’s a quantitative estimate of the impact of elevation on the amount of oxygen available (the partial pressure of oxygen).

Source:  Highpeak.com

If you live in Aspen, CO, you’re missing about one-quarter of your oxygen, compared to life at sea level.  If your lungs are healthy, you’ll soon get used to it.  But if you’ve got COVID, and your blood oxygen saturation starts to fall, you’re going to be a lot worse off in Aspen than you would be at sea level.

And so, my guess is that for a given population of individuals severely ill with COVID-19, a higher fraction of them are going to require an inpatient level of care in Colorado, compared to (say) the U.S. Gulf Coast states.  That’s because a critical deciding factor is their 02 blood saturation levels.

The upshot is that COVID is different in Colorado.  Not because the disease is different, or the people are less healthy.  There’s just less oxygen there.

There is an entire line of disinformation about global warming that works like this:

1. Find any indicator of climate change that isn’t hitting a new high this year.  E.g., global average temperature.
2. Point to that decline and and contrast it to the increase in atmospheric C02 (which reliably hits a new high every year).
3. When that indicator begins hitting new highs again, find something else.
4. Repeat.

For a while, it was all about “the hiatus” in global warming (Forbes, NOAA), until that turned out to be literally imaginary.  The news outlets that touted “the hiatus” simply don’t talk about global temperature now.  A common alternative target for this style of propaganda is summer arctic sea ice extent, where you will reliably see Fox News coverage in any year in which the sea ice extent increases.  (Despite a clear long-term downward trend, consistent with (duh) a much warmer Arctic).  Weirdly, for a while, when the Arctic summer ice was clearly falling, denialists focused on winter antarctic ocean ice cover (which, unlike the arctic summer ice cover, has no strong implications for the future of warming and is more-or-less unrelated to summer arctic ocean ice cover).  Focused on that, except in years when the winter antarctic ice cover is below the peak.  At which time, then turn to some other target of opportunity.

When done properly, everything said in such propaganda pieces is true.  Some years, global temperatures fall, compared to the prior record.  Every year, atmospheric C02 goes up. True facts.

But these pieces are, nevertheless, propaganda.  The are disinformation designed to persuade readers to believe something that isn’t true.  The disinformation works due to the careful cherry-picking of the data point shown and the use of the semi-attached figure of one year of C02 increase.  It’s left up to the reader to make the incorrect inference (that the theory behind global warming requires temperatures to increase in yearly lockstep with annual increases in C02, so this contrary fact disproves global warming).  Many willingly do so.

The details don’t matter here, it’s the basic technique that I’m trying to emphasize.  Any time series of data that shows temporary ups-and-downs (or seasonal changes, or cross-sectional differences) is a potential propaganda tool.  Cherry-pick the right data points, tack on the semi-attached figures, and the result is a steady stream of propaganda pieces that give the impression of something (e.g., that data are always contradicting the scientific consensus on global warming) without actually telling actionable lies about it.

And I guess it goes without saying that Fox News is the master of this tactic.  Near as I can tell, the only information about global warming that makes it onto Fox News is propaganda of that sort.  They’re silent on the vast majority of studies and information that reinforce the scientific consensus (the planet is warming rapidly, we’re causing it), and highlight any piece of information that can be made to seem as if it contradicts that consensus.

And, once you’ve seen it enough times in one context (global warming), it jumps off the page when you see it in other contexts. 

Again, to review the method:

1. Find any relevant data series that goes up and down.
2. Cherry-pick a time where the data are trending the way you like.
3. Tack on the semi-attached figure.
4. Leave it to the reader to make the incorrect causal link between the two.

With that as introduction, look at the Fox headline above and tell me what you’re supposed to believe, based on that. 

Did you come up with “well, that proves that masks don’t work, doesn’t it”?  From which you have to move on to “mask mandates don’t work”.  And so, obviously, based on that, we don’t need mask mandates.  Why would any idiot think that mask mandates had any purpose?

Let me now illustrate what you should have gotten out of that headline:

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 11/10/2021, 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.

As an Extras for Experts, note the critical timing here.  As little as two weeks ago, they couldn’t have written that headline.  Four weeks ago, they’d have to have written it the other way around.  Give it another four weeks, and I’m betting they wouldn’t be able to write that.  But for this brief time window, they can write that headline, and it’s a fact.  Four weeks from now, this will have been forgotten.  And they’ll have found another target of opportunity for their next piece of propaganda.

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But why?

A lot of rational people, particularly scientists, have a hard time understanding why anyone would bother to produce (or consume) this continual stream of propaganda.

To me, having seen this time and again, and having run up against it with my ultra-conservative older brother, the answer is simple.  It’s all about faith and belief, and has nothing to do with science.

In a nutshell, for most people, when facts conflict with faith, you must deny the facts.  It really doesn’t go any deeper than that.  It’s not intrinsically different from the Catholic Church’s persecution of Galileo for the heresy of claiming that the earth moved around the sun.

In my brother’s case, anything that would require deviating from orthodox political conservatism must be dismissed.  It’s not that he is anti-science, per se.  It’s that he works backwards from the policy implications.  If the science implies policies that conflict with his political faith, then the science must be denied. 

It really is that simple.

There are two types of people.

For some, facts and reason determine what their course of action should be.  And if rationality conflicts with faith, then faith has to change in light of the facts.  For others, faith determines their course of action.  And if facts and reason get in the way of that, those facts and that reasoning must be denied, so that faith remains unchallenged.

And so, broadly speaking, that “faith based community” creates a tremendous demand for hearing only what they want to hear.  Free markets will supply anything that is demanded.  The result is entire industries devoted to satisfying that demand for “facts” that match faith.

Back in the objective world, the fact is, the earth is warming.  That’s primarily due to a buildup of an incredibly stable gas (C02) in the atmosphere, faster than the ecosphere can absorb it.  The C02 comes from burning fossil fuels at a rapid rate.  The overwhelming consensus of informed opinion is that this process isn’t going to end well for civilization.

Fact is, masks work to reduce spread of COVID-19.  (And other airborne illnesses, for that matter.)  A high-quality mask (e.g., N95 respirator) works better than a low-quality mask.  But, all other things being equal, the higher the fraction of the population wearing masks in situations with non-negligible risk of disease spread (indoor public spaces), the lower the spread of COVID-19 will be.

If your faith bars you from considering anything but individual action, motivated by individual incentives, then these facts are inconvenient.  That’s because the most effective ways to stop C02 emissions, and to stop COVID-19 spread, require coordinated action that cannot be achieved by person-level free-market incentives. (Or, at least, none that have ever been proposed as feasible.)

As with the Catholic church and a heliocentric solar system, a lot of people maintain some sort of faith that requires that they must declare such actions to be heresy.   Those people need someone to feed them the disinformation that will allow their faith to remain unchallenged.  And one way or the other, the dollars behind that demand support the sort of mainstream disinformation highlighted at the top of this post.

It’s not that people are too dumb to know the difference.  It’s that they don’t care, and they actively crave the disinformation. 

And as a result, we have a fully-developed denial industry.  Literally the same entities that helped deny that smoking causes cancer were hired to help deny that combustion of fossil fuel causes global warming.

Given that, it’s really no surprise that the same techniques keep showing up.  In this case, the technique is to take any time-varying data, and pop up with a bit of disinformation during any brief period when the numbers are running your way.  Remain silent when the numbers aren’t in your favor.  And keep changing targets, because nobody in your target audience will even hold you to task for anything you’ve ever said.

I’d like to say that people will eventually figure that out.  But they won’t.  To the contrary, it’s not that the propaganda causes their non-factual beliefs.  It’s that they actively seek the disinformation that agrees with their faith.  They take comfort in it.  And the rest of us just have to live with the results, as best we can.

 

I saw an article yesterday listing out all of the extremely likely side-effects you should expect if you get the COVID-19 vaccine for your 5-to-11-year-old child.  It made it look like a terrible gamble.  If I’d seen that article, and nothing else, I’m not sure I’d have gotten my kid vaccinated.

The only problem is, they showed the numbers for vaccinated children ages 5-11 in complete isolation.  They didn’t show how young children fared, relative to teens.  And they didn’t compare to the placebo group.  Or to typical self-reported side-effect rates for (e.g.) flu vaccine.

If your job had been to scare parents away from vaccinating their kids, you could not have done a better job of it than by cherry-picking that exact bit of information.  And showing zero context.

Just to be clear about how these numbers arise, if somebody had asked me if I’d felt fatigue or muscle pain in the past week, the answer would have been “yes”.  As it would, pretty much any week of the year.  If I’d been part of the study, by responding honestly to that question, I’d have been contributing to the reported potential side-effect rate of the vaccine, as shown in the popular press.

This isn’t to dismiss the side effects.  They sometimes occur.  It’s more that you need some context to make sense of the reported side-effect rates.

I thought I’d get the actual data, and show how the side-effect rates for children look in proper context. 

Here’s the original slide of results, as presented to the U.S. CDC, which I will now simplify to highlight the main findings:

Source:  Presentation to CDC, “BNT162b2 (COVID-19 Vaccine, mRNA) Vaccine in Individuals 5 to <12 Years of Age”, November 2nd 2021, Alejandra Gurtman, MD, Vice President. Vaccine Clinical Research and Development, Pfizer Inc

Point 1: The side effects … were generally milder and less frequent in 5- to 11- year olds than they were in adolescents.  Putting aside that most of the side effects disappear within one to three days, little kids actually had lower rates of side effects than teens and adults.  That’s what these pairs of bars below are showing.  So if you weren’t too worried about vaccinating your  sixteen-year-old, you should be even less worried about vaccinating your six-year-old.

 

Point 2:  Side effect rates were only modestly higher for children actually getting the vaccine, compared to placebo.   These contrasts below are between vaccine and control groups.  If I had to condense it, it would say that compared to children who got the placebo, the vaccinated children were:

• 10% more likely to report fatigue or headache
• 5% more likely to report fever or chills
• 3% more likely to report muscle or joint pain.

Point 3:  And again, to be clear “If they arise, side effects generally are gone within one to three days.”

There’s another potential point of comparison, which is the rate of the same reported side effects for standard flu vaccine.  One might reasonably ask how frequently these same side-effects get reported for flu shots.  That’s a vaccine that most of use get annually without a thought in the world of having a significant adverse reaction.  (N.B., a flu shot is recommended in the U.S. for everyone over the age of six months, so it’s a vaccine that is definitely recommended for this 6-11 age group.)

It’s tough to get any hard numbers on that, probably because the flu vaccine has been considered safe for so long.  Here are a few bits and pieces.

Of the side effects tracked above for COVID-19 vaccine, the one article I found listed these rates of side-effects in adults, for flu vaccine:

• fever (perceived) 15.2%.
• fatigue (17%)
• muscle pain (17.7%)

Here are the side effect rates for Fluzone in adults:

• Muscle pain 18.9%
• Headache 13.1%
• Shivering 4.8%
• Fever 0.9%

I’m not going to beat this point to death, because my point is simple.

Side effects are not some new thing that just happened with the COVID-19 vaccine for young children.  The common side-effects of the COVID-19 vaccine in children are the same as the common side effects, for the same vaccine, in teens and adults.  And the same as the common side effects of the flu vaccine. 

The rate of side-effects from the COVID-19 vaccine, in young children, is less than in teens and young adults.  And, very roughly speaking, the rate of side effects of COVID-19 vaccination in children is not grossly different from the rate of the same reported side effects from flu vaccination in adults.

If you didn’t hesitate to get your teenage kids vaccinated, there’s no new reason to hesitate about younger children.  And if you get the flu vaccine every year for you and yours, you have arguably taken on as much risk of adverse events with that as you will with the COVID-19 vaccine.

It is all-but-impossible to get those conclusions out of standard mainstream news reporting about this.  It’s just too dog-bites-man, I guess.  It doesn’t fit into the modern style of fear-oriented journalism.  Instead, what will catch your eye is some chart showing an apparently high rate of side effects.  With no way for you to know that this is completely normal, no different from the experience of other age groups, and not hugely different from the side-effect rates for flu vaccine.