Post #900: Peak of the third wave: Is “dynamic herd immunity” capping the rate of spread of COVID-19?

Posted on November 25, 2020

Source:  Data from NY Times Github data repository.  Data reported through 11/23/2020.

An odd thing has been happening, even as the news is dominated by the worsening of the third wave of the pandemic nationally.  That third wave of US COVID-19 appears to be cresting in the states that led it.  And the strange part is that simultaneous crest across several states has nothing to do with any actions recently taken (or not taken) by state governments to contain the virus. 

The mere fact that some hard-hit states appear to have peaked, in terms of new COVID-19 cases per day, is not the odd part of this.  Here’s what’s odd.

First, note that several states peaked at just about the same time.  Within, say, a week of one another.  Two states peaking in the same week could be a coincidence.  But six or seven states?  Spanning more than 1000 miles?  All of them with extremely high rates of new COVID-19 cases per day?  It’s hard not to think that there’s something that ties that together.

Second, note that this peak occurred despite some states taking action and others not. Famously, for example, the governor of South Dakota refuses to institute a mask mandate or take other protective measures.

Third, note that these peaks occurred well before we could plausibly expect to see any results of any state actions, in any case.  For example, ND and IA mask mandates were passed 11/14/2020 (Post #890) and 11/17/2020 (Post #893), respectively.  Any reduction in infections that result from those changes could not possibly appear in the data prior to the end of November.

That’s due the “pipeline” of cases that are already infected, at any point in time, but haven’t yet appeared in the numbers.  It takes, on average, in most areas, about 12 days for any change in infection rates to appear in the data.  (That’s about five days from infection to onset of symptoms, and then another 7 on average for seeking medical attention, getting tested, and having the test results appear in the data).

Meanwhile, three other states in that vicinity have high case rates and continue with a relatively steep upward trajectory.  But all are well below the peak demonstrated by ND.

Finally, I need to supplement the above with one chart of states that got covered up in the tangle of lines above, and then the remainder of that block of states.

Note that, in particular, MN appears to have peaked in the last week.

And when I put that all on one map, it looks like this.  The block of green-ish states are those that a) had high rates of new cases and b) all appear to have peaked in the past, oh, week or so.  The red-ish states are those, in the same area, with high rates of new COVID-19 cases, but where trends continue upward, with no evident peak or leveling-off of new cases per day.

All pandemics are local.  And by that I mean that you’re going to be reading news articles about individual cities, within those states, that are running out of hospital beds.  And will continue to do so for some weeks.  But in terms of total cases within the states, for some reason, almost all of the states with extreme new-case loads decided to do a 180 in the past couple of weeks.  All in the same geographic area.  All at the same time.

Speculation on what might cause this.

First, let me be clear, I have no firm idea on what is causing this.  I just noticed the oddity, that’s all.

1:  Maybe it’s the weather, and so this break is temporary.  I note that this area had a heat wave just about weeks before these states started peaking.  That should have temporarily raised indoor relatively humidity, and if humidity is key to transmission (Post #894), should have slowed transmission.  And so, maybe this isn’t a peak, but it’s just a temporary break in the trend, due to that past weather event.

The problem there is that the heat wave affected all of those states.  Here’s Bismark, ND and Cheyenne, WY.  Pretty much the same weather pattern across both areas.  If it were solely an artifact of weather, we’d see a break in the trend for Wyoming.  Which we do not.

2: Maybe people wise up before their state government does.  The high incidence of COVID-19 in these states was public knowledge.  Maybe there’s some common threshold of hard-headedness that people can get past, with enough news coverage of how dire the situation is becoming.  And so, these peaks are an artifact of enough people waking up to the situation and changing their behavior, in each state, to break the back of the upward trend.

But the sharp reversals of trend, and the tight synchronization, don’t really seem to fit with that.  At least, not to my eye.  This has the look of something far more mechanical or automatic, and less the look of (e.g.) the will of the people shifting in favor of mask use.

3:  Maybe this is how herd immunity works in this situation:  It generates a natural cap on the rate of spread, for a given set of underlying conditions.  We keep hearing that we need 70% of the population to be immune before we achieve “herd immunity”.  That’s the point where the pandemic dies out for lack of enough “infectable” people to maintain the chains of disease transmission.  But maybe herd-immunity-type effects also limit continued rapid spread.

Herd immunity is not going to be a smooth process.  It’s not as if you’re going to run right up to the herd immunity level, and then have the pandemic stop all at once.  Instead, as a smaller and smaller portion of the population is at risk of infection, presumably the rate of transmission would slow.

At this point, best guess, somewhere around a quarter to a third of the entire population of North Dakota has already been infected with COVID-19.  (See Post #889 for details.)  That’s the roughly 8 percent that had been formally diagnosed, as of mid-November.  Times some unknown multiplier to account for cases that were never diagnosed (asymptomatic individuals and individuals with symptoms mild enough that they did not seek treatment or diagnosis.)

I’m not familiar enough with the techniques used to model epidemics to say for sure, but I’d bet that having a third of your population immune to the disease is enough to put a crimp in the rates of spread.  It might not stop it, but it might plausibly prevent the highest possible rates of spread from occurring.  Except for the fact that COVID-19 spreads largely via clusters, you’d be tempted to say, well, at this point, a third of the chains of infection that used to continue are now being truncated by running into an immune individual.

The point here is that that maybe the basic arithmetic of this pandemic makes the rate of spread somewhat self-limiting. Once it reaches some high rate of new cases per day, for long enough, the rate has to go down due to the rapid build-up of surviving immune individuals.

Notably, the case mortality rate for COVID-19 is now quite low (e.g., about 1% in the Commonwealth of Virginia).  That makes the situation for COVID-19 materially different from that of the 1918 Spanish Flu. With this low mortality, if  COVID-19 tears through a population rapidly, then it rapidly builds up a large population of immune individuals. And that large population, while not enough to stop the pandemic from continuing to spread, may be enough to cap the rate of spread.  The very fastest rates are no longer feasible, because enough chains of transmission are being truncated.

If so, that’s very good news for my “reefer test” ( Post #888).  That means that the rates won’t continue to climb until you finally run out of potential victims.  Instead, for a given set of circumstances, you’ll see the rates all peak around the same point.  And the commonality of that peak occurs because you’ve built up enough survivors to “clog up the works” just enough to cap the rate of spread.

As a footnote, I’ll bring back an earlier version of the diagram above.  Oddly, note that the two peak summertime states both peaked at just about the same daily rate of new cases.  Despite being in completely different climates and locations.  That was for the air-conditioning-led summer outbreak.  And now, with what I’d call the heating-led winter outbreak, we’re kind of seeing the same thing.  Just at a different level of new cases per day.

(But that may just be reading too much into the data.  There was a spectrum of peaks in the summer outbreak.  Obviously, the ones at the top are all going to be near the top.)

4:  Does “herd immunity” really require 70% of the entire population to be immune.  Maybe you run out of risk-takers well before 70% are infected.

How about people like me, who are basically minimizing exposure already, scrupulous about mask use, and wearing an aerosol-filtering fitted mask when shopping (Post #780, Post #807).  Does herd immunity require 70% of people in my situation to be infected before the pandemic stops on its own?  Or, by dint of isolating myself, am I more-or-less irrelevant to the herd immunity calculation?

Let me put it this way:  A vaccine provides (we hope) 90% protection against being infected.  We count (90% of) the vaccinated population as part of that 70% in the herd immunity calculation.

But suppose that a good mask and careful behavior results in 80% protection against being infected.  What’s the difference, exactly, between that, and being vaccinated?  (In terms of the herd immunity concept.)  The vaccinated individual is assumed to be (more-or-less) permanently removed from the pool of persons who can be infected.  The mask-and-behavior person remains at risk of infection, regardless.  So there’s clearly a long-run difference — the virus can slowly “pick off” persons from the mask-and-behavior pool, but not from the vaccine pool.  But in terms of breaking the chains of transmission, in the short run, I’d say that those two routes to stopping spread of the virus are roughly equivalent.  One terminates 90% of the chains, the other terminates 80% of the chains.  So to speak.

And so maybe, at some point, the population of risk takers that is responsible for high rates of spread gets thinned down somewhat.  Not by mortality, but by becoming infected and so becoming immune.  And so, even if just a third of ND residents are immune, maybe that’s a lot closer to 70% of the risk takers.

If so, the rapid spread attributable to failure to take precautions might be self-limiting well before 70% of the entire population is immune.  Maybe, to prevent the most rapid spread, all you need is 70% of the risk-taking population.  And that might be a much smaller fraction than 70% of the entire population.

Best guess:  “dynamic herd immunity”.

These synchronized and rapid reversals of the upward state trends, for the states with high growth rates, suggest a mechanistic explanation, rather than a behavioral one.

For sure, it’s not a result of government action.  That’s been piecemeal, and in key states (ND, IA) occurred far too recently to account for the turnaround.

The weather is something that would affect a broad area.  But the same heat wave that plausibly might have resulted in the peak in (say) SD also affected states where a peak has not yet occurred, such as WY.

Having the populations of these states all “wise up” at the same time seems improbable, given how close the timing is.

My bet is that the rapid growth is self-limiting.  The virus leaves so many immune survivors behind, in such as short period of time, that it chokes off that very rapid growth.  So it’s not herd immunity (that disappearance of the virus) but instead a natural limit on the rapid spread.  Rapid spread can only go on for just so long before it (in effect) chokes on its own impact on the population.

Let me call this “dynamic herd immunity”.  That’s the idea that a high rate of spread can go on for just so long before it has to slow down.  And that it will slow down well before 70% of the population has been infected.

How long that high growth may continue, and how rapid that growth can be, will of course depend on underlying conditions.  In these mask-averse states with dry and cold winters, that can proceed much faster than it might in states with good mask use and milder climate.

Seeing all these states, all peaking around the same time, around the same growth rate, suggests that there’s something about the mechanics of epidemics at work here.  My best guess is “dynamic herd immunity”.  A high rate of new case growth chokes itself off, at some point.  The virus will still be spreading, but at a slower rate.  And if so, that’s very good news from the standpoint of running out of hospital beds.  Maybe the lower apparent severity of the average case (Post #897), and “dynamic herd immunity”, mean that we won’t have to fail the reefer test after all.  We’ll manage to get through this, despite ourselves.