It’s about time to revisit herd immunity. So far, all I’ve been able to say, from the data, is that we’re not there yet. (Duh.)
I’m going to start off this next set of posts by explaining why the current vaccinations don’t matter (much, yet) in terms of getting individual states or the U.S. as a whole to the herd immunity level. Continue reading Post #1035: Herd immunity, part 1: Vaccines don’t matter, much, yet
Source: All graphs are calculated from NY Times Github COVID data repository, data reported through 2/16/2021.
We’re now down 68% from the peak. Just 32% left to go.
Source: Calculated from NY Times Github COVID data repository, data through 2/9/2021.
Just a quick recap of what I’m waiting to see in the data, and why. And why the recent sharp decline in new case growth in North Dakota might or might not be it. Continue reading Post #1002: North Dakota new COVID cases decline rapidly.
Today’s numbers. Looks pretty good.
But not good enough. They aren’t good enough, as they currently exist, to prevent a fourth wave of COVID, from the U.K. variant.
But don’t freak out just yet. FWIW, my calculation is that if we merely stay the course — keep vaccinating at the current rate, maintain existing COVID hygiene — the inevitable spread of the U.K. variant will merely cause a slowdown in our recovery, and need not cause a fourth U.S. COVID wave.
Details follow.
Continue reading Post #1000: The simple arithmetic of the U.K. coronavirus variant
And nobody notices!
The US is now seeing less than half the number of new COVID cases per day that it saw at the peak! Ditto! Continue reading Post #998: North Dakota hits 10 cases/ 100,000/ day!
Source: Calculated from NY Times Github COVID data repository, data reported through 2/4/2021.
New COVID-19 cases per 100/000 / day are just shy of half the level they were at the 1/8/2021 peak. Continue reading Post #994: Now 48% below peak.
This article on herd immunity in India was published in today’s Washington Post. I thought I’d bring it to your attention. Not just for the content regarding herd immunity as the likely explanation of the current state of COVID in India. But also for an official estimate of total U.S. infections that seems just slightly lower than the one I have been using. Continue reading Post #993: A couple of bits of confirming evidence on herd immunity
Der Schrei der Natur (The Scream of Nature), by Edvard Munch. Source: Wikipedia.
This is not a cheap attention-getting trick. I’m just trying to get with the program.
Time for a little math. We’re current having about 3000 deaths/day from COVID. We’re vaccinating maybe 1.5M per day. Maybe 8% of the population is partially or fully vaccinated. But it takes six weeks from the first shot to full immunity. And the CDC tells us that we need to have 70% vaccinated (maybe more!) to bring the pandemic to an end.
Let’s do the math. What does our official party line imply, in terms of total additional COVID deaths before the pandemic is controlled. Extrapolating at the current rate. I get:
(((62% * 330M persons)/1.5M persons/day) + (6*7 more days for full immunity)) * 3000 deaths/day = 535,200
Hence the title of this section. Just another half-million to go. Unless a more infectious strain of COVID takes over. Or enough people refuse to be vaccinated.
First, don’t blame me. I’m not the one setting the official message. I’m just taking what the CDC is telling us and doing the math to work out the logical implications. I’m just trying to get with the program.
Charitably, the CDC’s official position reflects a policy of keeping as many people as anxious as possible, to maximize total vaccinations. Part of their job is to herd the population in the right direction. And so, no doubt, to them, the end justifies the means. It’s for the greater good.
That makes current CDC messaging on vaccines of-a-piece with “you don’t need masks” followed by “wear a cloth mask”. Neither of which was for your good. If they wanted to give individuals advice for greatest personal safety, they’d have been saying “wear an N95” from the get-go. Instead, that CDC advice was for the greater good. For the good of the herd.
Personally, I’m plenty anxious enough about COVID without the extra help from CDC. But I understand that a lot of people aren’t. So I understand that the CDC is going to keep beating this drum, and putting out the most pessimistic picture possible, and frankly ignoring any good news, from now until the end of the pandemic.
So I try not to take it seriously. They’re just doing their job, as they see it. Given what they have to work with.
And the shepherds gonna shep shep shep shep shep.
Just move along.
I keep forgetting this and making stupid mistakes as a consequence. So this item is here for my benefit.
That lag time between the actual infection event, and the reporting of that event, is an estimate. And as far as I know, there’s no official estimate out there. My best guess was an average of 12 days from infection to reporting, based largely on the experience in Wuhan. That’s a median of five days from infection to symptom onset, and a further seven days for finally going to the doctor, getting tested, and having that reported. But I have also seen serious estimates of “two to three weeks”. On top of which we have to factor in the seven-day moving average, so that in addition, we’re looking at reported data that’s an average of another four days old (or so). Hence the range given in the title.
Now stop for a minute and let’s work through what that implies for The Great Post-Holiday Surge of ’20. Infection events actually peaked well before Christmas, and were falling rapidly throughout that entire holiday period.
The literal tippy-top of the peak of (the seven-day moving average of) reported cases was January 8, 2021. Working back from that, using the shortest plausible lag (16 days), infection events peaked on 12/23/2020. And if the longer lags are true, infection events may have peaked as early as 12/14/2020.
Take a second to appreciate the irony. The entire time that our public health officials were warning about a dangerous surge of holiday-related infections — based on the (non-existent) post-Candian-Thanksgiving surge (Post #916), and the (equally non-existent) post-U.S.-Thanksgiving surge (Post #922) — new infections were actually going down the entire time.
More precisely, pretty much the exact moment when our public health leadership was calling for a great surge in cases to occur, we were actually seeing what appears to be the start of the end of the U.S. COVID third wave. That was the when infection events began the weeks-long nation-wide decline that we have witnessed so far.
The lesson is that you were going to be given a warning about The Great Post-Holiday Surge of ’20, no matter what. Clear evidence to the contrary, for prior holidays, be damned. Or, at least, completely ignored. And nobody is ever going to be held to account for the fact that the prediction wasn’t just wrong, it was about as wrong as it could possibly be. That’s fair game, as these things go.
Now, I am not going to bring up “the boy who cried wolf” here. Despite being a natural with the whole shepherd thing. Because, you may recall, there actually was a wolf. Nor will I say that a stopped clock is right twice a day, because CDC is oh-for-two on post-holiday surges. Every dog has its day? Even a blind squirrel finds a nut once in a while? If you had an infinite number of monkeys typing randomly on an infinite number of typewriters … None of the above. I’m going with “jaundiced eye”. Sufficiently hackneyed and yet on-point.
I now tend to look at the official line out of CDC with a jaundiced eye. Because it isn’t their job to tell the truth. It’s their job to do what it takes to protect the public health. For the greater good.
Shepherds gonna shep shep shep shep shep. Doesn’t go any deeper than that.
Here’s the updated table on the fraction of the U.S. population that is probably already immune to COVID, using data reported as of 2/1/2021.
At this point, I think you realize you’ll never see anything like this from official government sources. Not because it’s wrong. Not because these are grossly incorrect estimates. But because the takeaway from this is way too upbeat.
And to be crystal clear, the reason you’ll never see this is NOT because you can’t count on an extremely high likelihood of a lengthy period of immunity after being infected. You’ll see estimates all over the map, but I put the median of what I’ve seen as “at least six months, likely longer”. (As opposed to the duration of immunity following vaccination, which nobody knows yet, but the smart money is on having to get re-vaccinated every year if you want to remain immune. And so, to a close approximation, about the same as with post-infection immunity.) Putative cases of re-infection are so rare that they make the news, and many of those cases are arguably due to underestimating the false-negative rate of COVID PCR tests. (That is, individuals who appeared to be over COVID based on two consecutive negative PCR tests, but may have simply gotten two false negatives in a row.)
Instead, as explained above, the CDC insists on presenting just about as bleak a picture as possible. If our public health officials talk about herd immunity, at all, they only count the people who have been vaccinated. And they insist that 70% (or more) must be vaccinated before we reach herd immunity.
They focus solely in vaccination. They ignore the vastly larger population of individuals who have recovered from infection. And they ignore the fact that the 70% number is for immunity used in isolation. Whereas, in fact, we are using both immunity and COVID hygiene (masking/distancing/limiting contacts) to break up what would otherwise be infection events.
And I just have to keep telling myself, it’s not because they’re stupid. Or willful. It’s just Shepherds gonna shep shep shep shep shep.
For the U.S. as a whole, one can make (and I have made) the argument that we’ve already reached a version of herd immunity via the mass of individuals who have already been infected (or vaccinated), combined with our ongoing COVID hygiene practices (masking/distancing/limiting contact). That was the gist of a set of posts last week, what I call “remission” of the pandemic.
I still have to wonder what the party line will be if (when) the U.S. pandemic ends well before 70% have been vaccinated. Putting aside the U.K. and other more-infectious variants, my best guess (from the table above, and current rates of infection and vaccination) is that we’ll hit an actual 70% with immunity when we get something less than 35% of the population vaccinated. That, plus the effects of COVID hygiene, essentially guarantees that the viral replication factor will fall below 1.0 and the pandemic will slowly shrink away.
But we already know the answer, based on the Christmas surge that actually turned out to be (what looks like) the start of the end of the U.S. third wave. There will be no after-the-fact reckoning.
But before I get too optimistic, I have to keep reminding myself that the story I’m telling works well for the U.S. as a whole. But it does more-or-less nothing to explain the awesomely uniform declines across the states. Like so, in logs, so that constant growth is a straight line.
So when you take a hard look at which I’m saying, I really don’t have solid explanation at all. I think there’s something there, but not enough to explain the fact that every state in the U.S. is showing declining cases. (Let alone the eerie coincidence of the peaks in many countries). The uniformity more strongly suggests that COVID is seasonal, and now it’s the season for COVID to go away.
In particular, I am not yet seeing what I hope to see, which is an ever-increasing rate of decline in North Dakota. If 70% is the herd immunity level, then the volume of infections there, plus nearly 10% immunized (with overlap of the infected), ought to be pushing them well past that limit. And the vaccinations would drive the rate down faster than what you would see from simple, infection-acquired herd immunity.
On the other hand, see Item 3. It may have already happened, but just hasn’t shown up in the numbers yet. Meanwhile, all we can do is wait and see.
Judging from the feedback I’ve gotten, most people can’t make head or tail out of my last three posts on herd immunity. (Post #978, concept; Post #979, empirical estimate; Post #981, U.K. variant estimate).
At my wife’s suggestion, let me just tell it as a story, and see if that’s clearer.
You’ll hear that 70% of the population must be immune to COVID-19 before we can end the pandemic in the U.S.
That’s wrong. Or, at the very least, that depends on what you mean by “end”.
By “wrong”, I don’t mean that there’s some uncertainty around that number. There is, to be sure. But by “wrong”, I mean the 70% figure is conceptually wrong.
If we’re talking about the problem we are facing right now — ending this current wave of the pandemic — then that’s the wrong number to look at. It’s far too high. And that’s because most people who use that 70% figure don’t have their thinking straight about what, exactly, that 70% number represents.
And, ironically enough, clarifying that last point was what those confusing posts were about. So let me try to fix that with this post. Continue reading Post #982: Herd Immunity IV: The simplified version.
Edit: Read Post #982 first.
This post presents a calculation to match the herd immunity discussion of the just-prior post. Read Post #978 first, then this one.
Here, I back-solve for the level of immunity in the population that should bring the effective COVID-19 viral reproduction factor below 1.0 (i.e., end the third wave of the pandemic), as long as we maintain masking, distancing, and other behaviors limiting viral spread.
This is a simple calculation, based on one point in the progress of the pandemic in North Dakota. That point being the two weeks when North Dakota saw its sharpest increase in cases.
So there’s not a lot of accuracy here. And it’s not an estimate, in the sense of being a statistic calculated from pooling a lot of data. It’s really just a round-numbers (but data-based) illustration. It shows that the two different herd immunity concepts defined in the prior post lead to two very different levels of required population immunity. And that we may already be hitting the lower level in some states.
Bottom line: 40%. Once something like 40% of the population has been infected, that ought to be enough to set the third wave of COVID on a downward trajectory. As long as we maintain masking, distancing, limits on social gatherings, and other such controls. But we’d still need the classic “70% herd immunity” to return to normalcy, meaning, life without those controls.
The upshot is that the uniformly downward trajectory seen in the U.S. Midwest probably isn’t a fluke, or luck. It’s probably just a matter of arithmetic.
The clear policy implication is that there is a more efficient way to use the COVID vaccines, if the goal is to bring the U.S. third wave of COVID to a close. You should concentrate vaccination in those states that have had the fewest infections so far. You shouldn’t aim for an equal share of the population vaccinated in each state, as we are now. You should aim for an equal share immune in each state, either via vaccination or via prior infection. That means shifting vaccine from states that have already had widespread COVID infection, to states where a higher fraction of the population still lacks immunity to the virus.
Continue reading Post #979: The two distinct levels of herd immunity, Part II
