Source: NY Times Github data repository, data reported through 11/27/2020.
As of 12/27/2020, ND had the fourth-lowest rate of new COVID infections in the U.S.
As of 1/14/2021, ND had the second-lowest rate of new COVID infections in the U.S. Only Hawaii has lower rate. See Post #951.
Original post follows:
This is one of those seemingly simple 2+2=4 analyses. In this case, it’s literally 8*10 > 70.
The arithmetic isn’t rocket science. Anybody can do that. My only value-added here has been in keeping an eye on the situation, and realizing why that arithmetic might matter.
Right now, 10% of the population of North Dakota has been formally diagnosed with COVID-19. As of data reported through 11/27/2020, they’ve had 77,242 known cases. That’s out of a population of about 760,000 (per the US Census Bureau). Or (77,242/760,000 = ~) 10%.
A 10/25/2020 publication by CDC staff says that, best estimate, on average, 8 people have had COVID-19 for every one that has been diagnosed.
IF CDC staff are right, and IF that US average applies to the US Midwest, then North Dakota has probably achieved COVID-19 herd immunity. Or is close to it. And much of the US Midwest has or will be following suit in the near future.
Obviously, that’s two big ifs. But anybody can follow the math. That’s 8*10% = 80%, and that’s higher than the 70% conventionally thought to be required to achieve herd immunity to COVID-19.
Oh, and note the peaks on all the curves at the top of the graph above.
Discussion follows. This brings together several points that I’ve brought up over the past two months or so.
I thought I was being sarcastic.
A population achieves “herd immunity” against an infectious disease when a large enough portion of the population is immune to it. If enough people are immune, the ongoing epidemic or pandemic of that disease dies off. There aren’t enough “infectable” people to carry the chains of disease transmission forward. Those chains die out. And so does the pandemic.
Conventionally, scientists have (gu)esstimated that about 70% of the population needs to be immune before we’ll have herd immunity against COVID-19.
If you think about that even a little bit, you’ll realize just how slapdash the 70% figure is. It models humans as if we were no different from a herd of sheep. E.g., there’s no allowance for a significant fraction of the population that interacts rarely or not at all with others. And it doesn’t account for the possibility that some may already be immune to COVID-19 from vaccination for unrelated diseases with surface proteins similar to those on COVID-19 (and so, already having antibodies that will attach to COVID-19). Most importantly, epidemiological and other evidence suggests that MMR vaccination may yield partial immunity to COVID-19. (Reference, Press Release, Earlier Reference, Clinical Trial, Case Report of 36 cases) But I’m just not going there. Just take it as a matter of (scientific) faith that, at this point, about 70% of the human herd needs to survive COVID-19 to achieve herd immunity against this disease.
A couple of weeks ago, I started joking about North Dakota and herd immunity. In Post #889 (11/13/2020), I pointed out that if they merely kept on doing what they were doing — which was, more-or-less, nothing — with their (then-) current rate of new infections, they’d likely achieve COVID-19 herd immunity by New Year’s Day.
And the next day, tongue in cheek, I expressed disappointment that they were spoiling this wonderful natural experiment by (finally) passing a mask mandate and other COVID-19 hygiene measures (Post #890, 11/14/2020).
And then, just a couple of days ago, I wondered if the simultaneous peak in the COVID-19 new case rates in many upper-Midwest states might somehow be the first limiting effect of herd immunity (Post #900, 11/25/2020). Because it sure looked like a whole lot of states among those high-infection-rate states were showing peak infection rates, all at more-or-less the same time.
What I’m trying to get at is that I’ve had my eye on this herd immunity thing for a while. And whatever is causing all those simultaneous peaks, at the top of the graph above, it sure has the look of something intrinsic to the pandemic. It’s starting to look like once you get enough crazily rapid spread of COVID-19, you hit some sort of limiting factor.
Maybe that limiting factor is human intelligence. Maybe the great mass of people finally wises up, once the situation gets bad enough. Despite their leadership. And their evident unwillingness to pay attention to facts. Or trust the advice of public health experts.
And maybe monkeys will fly out of my butt. So far, evidence suggests that “the native intelligence of the U.S. population” is a slender reed on which to hang the end of the third wave of the pandemic. It doesn’t seem to have played much of a role so far, in much of the U.S. So it seems like a long shot to bet that’s the root cause of the peaks seen above.
Instead, I wonder if you just run out of bodies. That is, among the people out-and-about, circulating and spreading disease, I wonder if the limiting factor is, in fact, herd immunity. Or something close to it. Such a large fraction of the people actively spreading disease have already caught it that the chains of disease transmission begin to die out. And the new-infection rate peaks.
That’s the idea I was getting at with the phrase “dynamic herd immunity” in Post #900. Maybe a conservative estimate didn’t put North Dakota quite at the herd immunity level yet. So the pandemic wouldn’t outright stop. But maybe with a sufficiently large fraction of the population, maybe at least it couldn’t continue to spread at that rapid clip. Enough chains of infection were being stopped by (now-) immune individuals that the rate of infection had to slow.
But now that the US CDC staff have doubled-down on the idea that the known COVID-infected population is just a tiny fraction of the total, you can just appeal to straight-up herd immunity. If the CDC staff estimate is correct, and if it applies to the states in the upper Midwest (two big ifs), then North Dakota has plausibly hit the herd immunity level. And many of the nearby states (with nearly as high a rate of daily new infections, for nearly as long) will at or near that level as well.
Good enough for now.
And I think I’ll just end it right there. I was going to belabor this with a detailed analysis of the CDC staff estimate, and why I think that likely overstates the actual pool of infected people. And so on.
But I don’t think anybody cares, and it’s almost rounding error within the bigger picture anyway. The only hard number in the whole analysis is the number of persons known to have been diagnosed with COVID-19. Everything else — the multiplier for the undiagnosed, the fraction of the population needed to achieve herd immunity, and so on — those are all, to some extent guesses (or, to be politer, estimates).
The bigger picture here is two-fold.
One is the reefer test (Post #888). One part of the bigger picture is the possibility for overwhelming the capacity of the hospital and mortuary systems with COVID-19 patients, to the point where you have to store the bodies in refrigerated trailers (reefers). That’s the danger of the very high rates of spread seen recently in the Midwest, and that’s the danger that can largely be sidestepped if those high rates of daily new infections are, in fact, self-limiting now due to herd immunity.
The other is the apparent reduction in the average severity of new cases. This is well beyond what can be explained either by shift in age distribution, or additional testing. (At least, in Virginia — see earlier posts on that.) The falling case-rate mortality numbers have been noted nation-wide. And so, for whatever reason — better medical practice, more widespread use of masks — who knows — the case rates for hospitalization and death are substantially lower now than at the start of the pandemic.
Those two interact. The lower average severity means that states can afford to see a much more rapid spread of disease before they hit the reefer limit. They can afford to see many multiples of the rate that sent New York so close to that limit. Because a much smaller fraction of those new cases ends up in the hospital and ends up dying.
(As an aside, a vast decline in the mortality rate over time was first observed in the analysis of the Chinese experience with COVID-19, per the World Health Organization analysis of that experience, as I reported back in Post #551. Although, in hindsight, I would rewrite that post.)
I guess I need to end this by saying that if this pans out — if those two big ifs are true — then this is very positive news for the course of the pandemic in the U.S. If these high rates of new daily infections are self-limiting, then we have a much greater chance of keeping below the capacity of the hospital and mortuary systems, as we wait for mass vaccination against COVID-19 to commence.
Thanksgiving data addendum.
Thanksgiving is going to put a few artifacts into the data graphed above. But I don’t think it changes the story at the moment.
Reporting artifact. First, many state health departments would have been short-staffed on Thanksgiving day itself. That would lead to a backlog in reporting on or about the 26th itself, which is evident by the √-mark looking ends of many of the lines above. That will have been cleared out of the data no later than next Tuesday at the latest.
Testing artifact. Second, many people may have been reluctant to seek testing (or unable to obtain testing) on Thanksgiving day itself. Given that it takes about three days for such test results to filter into the data, we should expect to see an even larger set of the √-marks starting tomorrow (Sunday) or so. It will take a few days after that for that to have worked its way out of the data. That effect should have filtered through the data by mid-next-week at the latest.
Increased infection artifact. Finally, there’s the actual impact on infection rates, from the traveling and socializing due to Thanksgiving. That should start showing up around 12 days after-the-fact. (That’s an average of five days between infection and symptom onset, and then seven-ish days for seeking medical attention, getting tested, and having the test results show up in the data.) That should show up as a broad upward hump in the numbers, across all states, starting on or around December 7, 2020.