Post #1326: COVID-19 winter wave gathers speed, and Missouri data on reinfections

Posted on November 19, 2021


Nobody cares about methodology.  That’s why I no longer bother to mention it when states make large one-day corrections to their data.  I undo whatever the state did, then get on with plotting the data as consistently as possible over time.

But today’s data boo-boo is  worth mentioning.  Missouri added about 6500 cases of re-infection to their counts.  There’s a formal definition of re-infection from the CDC, which says that a COVID-19 re-infection occurs when a person tests positive (via PCR or antigen test) at least 90 days after a previous positive test.  Restated, a positive COVID-19 test, more than 90 days after a prior positive test, counts as a re-infection.

This is noteworthy because it’s the first state I’ve seen that tracks the re-infection rate.  (It’s not as if I’ve looked for it.)  Without getting caught up in the exact details, Missouri is currently showing that about 1.5 percent of all COVID-19 infections in that state are re-infections.  (That is, re-infections divided by the sum of confirmed and probable cases = ~0.015.)

Source:  Missouri Department of Health and Senior Services.

There are a few caveats.

First, I wonder about the potential false-positive rate in that re-infection estimate, but I have no hard data on that.  Fragments of COVID-19 viral RNA and surface proteins can linger after a person has recovered and is no longer infectious.  (Standard PCR tests do not distinguish these “dead” viral fragments from virus that is still capable of reproducing.)  That’s why you are specifically directed NOT to get re-tested within 90 days of a confirmed infection, and why all institutions end quarantine based on a specified number of symptom-free days, and not based on a re-test (e.g., purely at random, here are directions from U.KY., Yale).

Second, I’m not sure you can accurately assess your odds of re-infection, based on the data above.  (That is, the odds of getting COVID-19 again, once you’ve already had it).  Among other things, I think you’d need to know what fraction of the entire population has already had one infection.

I bring up that second point because there’s a clear corollary:  Anything you might have read about low re-infection rates, early in the pandemic, is probably now out-of-date.  I mean, if few people had been infected at that point, then pretty much by definition you were going to see very few re-infections.  But now, it’s not so clear.  At any rate, that 1.5% above seems vastly higher than the fractional percents that appeared in the earlier research literature.

Without over-thinking it, I am a little surprised that the re-infection rate is that high.

But that’s only due to my ignorance.  As it turns out, re-infection by respiratory viruses is common.  We know that immunity fades over time.  I’ve certainly had flu more than once in my life.  But there are multiple strains of flu, so I’m not sure I’ve been “re-infected” with the same strain.  By contrast, researchers have looked for same-strain reinfections, and have found that a non-negligible fraction of people  get re-infected with more-or-less the exact same strain of flu on different occasions.  Apparently, it’s not even all that rare.

That said, the CDC continues to classify COVID-19 reinfection as “rare”.  But it’s not clear whether that’s merely because your risk of re-exposure is low (there just aren’t that many cases per 100,000 per day).  Or whether your likelihood of re-infection conditional upon re-exposure, is low (whether you really can’t catch it again, even if re-exposed).

So we can file this one away with all the other aspects of COVID-19 that I somehow thought must have been unique to this disease.  But that were in fact common across many viral respiratory diseases.  Things such as asymptomatic infections, and long-distance aerosol spread of disease, and so on.  And now, as it turns out, for your entire life, you were at risk of getting re-infected with more-or-less the exact same strain of flu virus.  Who knew?

Winter wave picks up speed.

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”  The NY Times U.S. tracking page may be found at

Do I need to belabor this?  The only news is that the rate of increase seems to be accelerating.

The U.S. new case rate is up 29% over the past seven days. 

Three regions (Midwest, Mountain, Northeast) are already well over their peaks from the summer Delta wave.

In the case of the Northeast, there are now three states that seem to be “going vertical” in terms of new case growth.

The only clear ray of light is what I discussed yesterday, the trio of ID, MT, WY in the mountain states.  Historically, they ought to be seeing increasing new case rates.  But instead, they seem to have peaked a few weeks ago, at the end of their summer Delta wave, and have been heading down since.

All of this, a bit over two weeks after the unusual summer warmth faded in October, and cold fall weather settled over much of the northern U.S. as of the first of November.

So, you look at Europe, you look at our mediocre vaccination rate, you look at the change in the weather, and I guess that a winter wave isn’t a surprise.

Problem is, we’re all sitting here, fingers crossed, hoping it won’t be too bad. All the while NOT looking at Japan, China, Korea, where somehow, for the second winter in a row, those high-mask-use countries seem to be sidestepping a significant winter wave.

While new research comes out — in this case, a meta-analysis or review of prior individual studies — concluding that:

Mask-wearing is the single most effective public health measure at tackling Covid, reducing incidence by 53%, the first global study of its kind shows.

And yet America remains dumb as a post.  A bare-faced post.

Worse, thanks to Republican politicization of masks, the facts no longer matter.  It has long passed the point where the actual efficacy of masks or vaccines, or lack thereof, has any relevance.  We’re stuck with a significant fraction of the population that ain’t gonna do neither, and you can’t make them.  So nyah.

I wear a high-filtration (N95, or maybe KF94) mask.  It’s not as if one lacks for good choices (Post #1246).  I’ve gotten my vaccines and booster.  I’m getting ready to fire up my humidifiers (Post #895).   I’m staying out of situations that I perceive as high-risk public indoor spaces, and certainly keeping my distance from the dumbasses who can’t be bothered to mask up in public.

I encourage my friends to do the same.

And I hope that with this winter 2021 wave, we finally have enough of the remaining population get infected so that we achieve something approaching herd immunity.  And COVID-19 can finally fade into the background in the U.S.A.

End of screed.

The crazy thing is, near as I can tell, we should be getting close enough to herd immunity that one more broad wave of infections should push us over the line.  Here’s my estimate of where we stand, now, where the magic herd immunity number is 80%, given how this is calculated.  Best guess, we’re somewhere around 77%.


Even accounting for the numerous uncertainties and assumptions that have to be made to produce this estimate, my point is, it’s not beyond reach.  One more wave, like last year’s winter wave, and we should be there.  Or in the neighborhood of there.

Meanwhile, vaccination (including booster shots) continues apace.  And every day, more people acquire immunity via infection.  We’re slowly reducing the fraction of the population (in red below) with no immunity to COVID-19.  If we can push that segment below 20%, then, in theory, the pandemic will collapse for lack of fresh disease carriers.  And while it will almost certainly not disappear, we should have long stretches of time where the circulating rate is low enough for most of us to ignore.

To me, it just looks like it’s going to take one more winter wave to do that.  And at this point, that winter wave is looking like a near-certainty.