A T Cell Army against SARS-CoV-2(hellovirology.com) |
A T Cell Army against SARS-CoV-2(hellovirology.com) |
If I am understanding it right, it's saying many people already have T-cells from common colds that can to some extent recognise Covid-19, and those that have had Covid, even a mild case, have T-cells that seem to last quite a while.
> There are also preliminary hints that some people might have a degree of preexisting immunity against the new coronavirus. Four independent groups of scientists—based in the U.S., Germany, the Netherlands, and Singapore—have now found that 20 to 50 percent of people who were never exposed to SARS-CoV-2 nonetheless have significant numbers of T-cells that can recognize it. These “cross-reactive” cells likely emerged when their owners were infected by other, related coronaviruses, including the four mild ones that cause a third of common colds, and the many that infect other animals.
> But Farber cautions that having these cross-reactive T-cells “tells you absolutely nothing about protection.” It’s intuitive to think they would be protective, but immunology is where intuition goes to die. The T-cells might do nothing. There’s an outside chance that they could predispose people to more severe disease. We can’t know for sure without recruiting lots of volunteers, checking their T-cell levels, and following them over a long period of time to see who gets infected—and how badly.
> Even if the cross-reactive cells are beneficial, remember that T-cells act by blowing up infected cells. As such, they’re unlikely to stop people from getting infected in the first place, but might reduce the severity of those infections. Could this help to explain why, politics aside, some countries had an easier time with COVID-19 than others? Could it explain why some people incur only mild symptoms? “You can go pretty crazy pretty quickly with the speculations,” says Crotty, who co-led one of the studies that identified these cross-reactive cells. “A lot of people have latched onto this and said it could explain everything. Yes, it could! Or it could explain nothing. It’s a really frustrating situation to be in.”
"Immunology Is Where Intuition Goes to Die"
https://news.ycombinator.com/item?id=24069662
Also, from the about the author page of the submitted piece: "Karmel Allison received her PhD in Bioinformatics from the University of California, San Diego, and has over a dozen years of experience in software and machine learning. None of these things is directly related to virology, you'll note, and thus you should take everything she writes with a grain of salt."
You do get a somewhat lower overall herd immunity threshold, but less than you thought.
It's like you didn't even read the article. At MOST, it means we MIGHT need a lower rate for herd immunity.
No, it actually doesn't, the math functions differently than what is naively believed. See:
https://statmodeling.stat.columbia.edu/2020/08/03/math-error...
The R0 would have to be corrected to the higher value, which would mean that the herd immunity threshold for the susceptible part of population would have to be higher(!)
Also, even among completely asymptomatic a high viral load is surely observed, implying that they aren't "immune" in the sense that they aren't the ones who continue to spread the infection -- but even for that, more research is needed.
Which means, until we know more, that those who are in danger aren't less in danger at all until they get a vaccine. With all the consequences we already know, like in the states which were strongly hit.
> the models of exponential growth was unfounded
I don't believe anybody ever claimed that the exponential growth would be indefinite. However, exponential growth through the susceptible part of population in one important phase of the spread (before any interventions are taken) was indeed a good enough approximation of what was observed.
Edit: re: the comment below: "it still means that herd immunity is easier and faster to achieve" -- no, not necessarily, see what I wrote about the "asymptomatic." For all we know at this moment, they get the virus and they spread it. If they just don't get sick it is then different from them not being involved in the transmission. And if some amount of people are identified who can't get it, the result can still mean "for two more years it has to be like it was up to now."
Edit 2: re the comment below: "That maths you link to literally says that the threshold for herd immunity among the entire population is indeed lowered." No it doesn't, not in the sense "it is better". Try to work from R0 = 2.7 which was observed. Assume 50% can't even spread. The R0 for the remaining 50% is then not 2.7, but has to be scaled up to 5.4(!) Which means, e.g. that the 90% of the 50% have to become immune to reach the herd immunity. And if up to now e.g. 10% of these were infected in past 6 months, that means 6 months * 8 = 4 years more to go. Whereas, when no 50% "immune" population exist, if with given R0 2.7 the threshold is e.g. 60%, and we have 10% infected, we have only 5 times 6 months to go, i.e. 2.5 years(!). You see, it's nothing so obvious like one likes to believe. Like Ed Young said: "Immunology Is Where Intuition Goes to Die." Search for the article (it's not about this calculation, but about how simple mental models don't work with immunology on other levels too).
The herd immunity threshold is dependent of R0, and that is exactly what they try to explain in the post. But like I've said, we don't even know how many of "asymptomatic" are part of all equations. And which equations are the right ones. The "right" models could be even more complex than what I've used here.
Edit 3: re: Citing Judith Curry. Judith Curry, the only qualification having from being one of the most favorite "experts" to be cited by climate deniers. But what she claims is still wrong: https://www.skepticalscience.com/skeptic_Judith_Curry.htm
The models of exponential growth were always unfounded. Exponential growth would have you believe that in a few short months, a sum larger than the entirety of the human population would get the virus. We knew then that it was ridiculous. The question wasn't if it was exponential or gompertz, but what the coefficients were.
Those who parroted the exponential curve were just trying to use scare tactics to drive clicks to their websites.
This hypothesis is entirely untested as far as I know.
https://clinicaltrials.gov/ct2/show/NCT04327206
https://clinicaltrials.gov/ct2/show/NCT04328441
It seems there are no current clinical trials for OPV, just that author arguing that such trial could be started (first time in April).
BTW, looking at how polio manifested, it really is a good example about what viruses are able to do, and why only counting "dead" (as in CFR or whatever) misses the point:
https://www.cbc.ca/news/canada/manitoba/covid-19-is-threaten...
"Polio often destroyed nerve cells that controlled muscles, leaving some with paralyzed limbs or lungs."
Sometimes these are chunks of toxins that your body recognizes a toxic, but without the actual part of the toxin that causes the toxicity. A kind of flag but without the army behind it.
"But ex post, holding the epidemic trajectory constant, it means that the disease is more transmissible per contact event than you expected."
...but that's wrong. The epidemic trajectory isn't constant (R0 is not an innate property of a virus; it changes with context), and it's pretty likely that we've completely mis-measured it by sampling from the wrong population. Our estimates for R0 are mostly model extrapolations from PCR testing data, which are not uniformly sampled from the population. If anything, in fact, they're heavily biased toward the susceptible population.
In other words, because we're sampling disproportionately from the most susceptible population, our estimates of R0 already seem high. If you sampled randomly from the population of less-susceptible people, you would see that the R0 is much lower than you originally predicted.
On top of that, there's absolutely no reason to believe that cellular immunity would make it seem like R0 is lower in any particular population: T-cell-mediated immunity is a late response. It can reduce the severity of an infection, but it's still highly likely you'd show up as a positive PCR test, and count toward an R0 estimate.
So you have a measurement based on a test that isn't strongly affected by cellular immunity, derived from a population biased toward the most susceptible individuals. If you have to doubt something here, you doubt the estimate of R0 (i.e. the model), not the observations of reality.
https://unherd.com/thepost/nobel-prize-winning-scientist-the...
But there are iirc ex surgeon-general equivalents in at least Germany and Israel and independents like Karl Deninger who had been saying essentially the same, with data to back themselves up, since March.
I think you're getting too caught up in the details. Its still good news, and it still means that herd immunity is easier and faster to achieve
It is not even clear it's "good news", it doesn't "still mean" at all, read carefully under "Edit 2" of my parent post. It can also mean that it will take longer. R0 must be reevaluated, and threshold is f(R0). We don't even know the "correct" function for the threshold, even less which people contribute in which way. We don't know how much were infected up to now, the high numbers weren't random samples, prevalence in global population is much lower than in a few hot spots where a lot of deaths happened. "Math is hard." Immunology is complex.
I'm at the point of concluding: "All R0-based models are useless for a disease like this." The true dynamics are far too complex, and this assumption that we can conclude anything by plugging linearly-varying (or constant!) parameters into a trivially non-linear model is absurd.
The maths you refer to do not make that judgment at all. They indicate that, assuming all the assumptions that go into those models are true (they aren't), herd immunity may be lowered, but not by much.
Try this maths https://judithcurry.com/2020/05/10/why-herd-immunity-to-covi...