edit: Not implying SAR COV-2 is "a cold". I'm implying it's a coronavirus also like the common cold, hence an antibody study would have to be careful to select for the right one.
In this case, 77% of participants were in the medical profession and were evaluated between May and June 2020. I suspect they came in contact with SAR COV-2 and didn't even realize it (body fought it off).
What this study is really suggesting is that your body either (a) has an immune response from previous contact (likely due to similarity with regular corona viruses) or (b) lots of people show no symptoms (we know this) and that they obtain an immune response from it.
In either case, the SAR COV-2 is likely far-less dangerous in the sense that more people have it and there are less deaths per infection OR many of us have a natural immune response (more than previously suspected).
It is unclear whether this antibody reactivity may confer clinical benefits, for instance, modulating the severity of a SARS-CoV-2 infection.
Not really. Mortality rate is number of deaths / number of infections.
We roughly know the total number of deaths. We have no idea what the total number of infections has been.
We should continue to make decisions based on the pretend mortality rate and ignore the true one?
Of course we "want to know it."
However the “pretend mortality rate” you refer to is not a thing. It is 1-3% in people who test positive, which correlates pretty strongly with people who have the disease. Think about it this way: the yearly flu in 2018-2019 had a mortality rate of 1.3-48.7% according to the CDC. The denominator in that was 61,000 or so cases. If you found out that 100,000 additional people were exposed to the flu but had no symptoms and by any definition had no flu illness would that change your outlook on whether the flu vaccine or hand washing is worth it?
Or another example: there is surgery which has a mortality rate of 15% due to post surgery infection. Not everybody needs the surgery, only 100 people per year do. Does that mean the rate of mortality here is fake because the denominator should be 7.5 billion? Or should we only count the people who get the disease that necessitates the surgery?
Yes, of course my outlook would change. Not on the binary scale--i wouldn't suddenly think it's not worth it to wash hands-- but I would want to know when deciding on measures.
The mortality rate is a specific figure, it's not whatever figure makes your reaction most appropriate.
If some people get the virus and have no symptoms, that's an incredibly important piece of the puzzle when deciding how to proceed.
Using your exaggerated examples, what if 99% of the population didn't suffer any adverse effects? Wouldn't you reevaluate the current response in light of that?
This study does not tell you that these people had COVID. That's the point: we don't know what this means. All we know, is that we need to study this further.
> The mortality rate is a specific figure, it's not whatever figure makes your reaction most appropriate.
From Wikipedia: "Mortality rate, or death rate, is a measure of the number of deaths (in general, or due to a specific cause) in a particular population, scaled to the size of that population, per unit of time." What is the "particular population" here is in question. I argue that "people who had the SARS-CoV-2 disease" are the relevant population. That is, people who had symptoms or tested positive after an exposure. This study does not change this because it shows no source of the antibodies: if I inject my COVID-19 antibodies into your arm, it does not mean that you had the disease and therefore should not be counted in the mortality rate, does it? How those antibodies got there is a question yet to be answered but the possible answers range from "those people got a mild case and didn't notice" to "those people developed low level antibodies due to incidental contact with COVID-19" without getting the the illness. Their immune system is not trained well enough to fight off a higher viral load."
> Using your exaggerated examples, what if 99% of the population didn't suffer any adverse effects? Wouldn't you reevaluate the current response in light of that?
Yes and no. First, this study does not tell me anything regarding suffering adverse effects. But if a future study did, I think the 3 million people that have died so far would have wanted us to take more precautions, not less, just because we, the living, won the antibody lottery. At the end of the day this is a highly infectious + somewhat deadly disease that adds up to one deadly pandemic. If your point is that only another few million will die if we let it run rampant vs all of humanity, I don't disagree with you but urge you to examine what it means to let millions of people die in this case.
well it possibly changes the calculus of how many people could potentially be a carrier of the virus. if that many people had the disease enough to have some antibodies even with all the extreme measures put into place it is possible this virus is even more contagious than we thought it could be. i don't know and i feel like we won't know because the study of transmission is woefully hard to do. going into this we didn't even know specifically how the flu was transmitted other than hand waving ways(maybe on the surface, maybe aerosols, maybe particles). our research has improved but it's still not great.
So there's the mortality rate for the overall population (what GP is referencing) vs mortality rate among people who've had COVID (what I believe you're referencing). Both are valid and useful in different circumstances: "How likely am I to die if I get COVID" changes as we discover a glut of asymptomatic cases, but "What % of the population will die of this disease?" stays the same
Ref: https://www.merriam-webster.com/dictionary/mortality%20rate
https://www.merriam-webster.com/dictionary/case%20fatality%2...
That doesn’t sound right — you can’t know the population death rate definitely without knowing the infection rate;
If no one gets the disease in your sample population of 100, your death rate says 0. If 1 person is infected and 1 dies, your population death rate says 1%. If 80 are infected, and 10 dies, your population death says 10%.
The “correct” number should be stable, but the population death rate should be changing as the disease spreads, approaching the correct number (but this is also true of liklihood of dying to COVID)
Your population death rate would only be static if your sample population is 100% infected, which is the same as “How likely am I to die if I get COVID”
As another example: lots of people have the tuberculosis infection which lies dormant in their lungs. TB infection is distinct from TB disease which is where you get symptoms. The former is only dangerous in that it can develop into the latter. The latter can kill you. How should you look at these numbers? Is it worthwhile to drop the number of people with TB disease and just divide deaths by TB infections or is it more informative to derive mortality as deaths / TB diseases while also using TB infections and the conversion rate from those to TB disease to calculate risk and transmissibility? I argue the latter is much more relevant to decision making.
Strikes me as similar to those who predict x% returns on their s&p index funds based on past stock market performance.
The future is always unknown.
We know deaths / symptomatic infections really well due to the large numbers involved.
The antibody tests are designed with high specificity for particular antibodies, this study went out of their way to look for any reactivity.
The emergency use authorizations allowed anyone who plausibly had a test to market it, even if it had not been independently validated in any way by anyone. Many folks just bought what they could find, even if it didn’t have an emergency use authorization.
Combined with porous borders, e-commerce and the internet, lack of consequences for someone in China to ship junk here? We got buried in (at best) untested junk, and often outright scams.
This has resulted in a rather predictable disaster when it comes to data and awareness - very few of the tests are being cross referenced to other data sources or tested against an independently verified data source, and even fewer (if any) are being evaluated by a independent party. The vast majority of tests in April have since been removed from authorization because they don’t work, don’t work well enough, etc. You can read a very nice paper from the FDA here summarizing it. [https://www.nejm.org/doi/full/10.1056/NEJMp2033687]
As far as I am aware, there is still no clear picture on which tests have actually been validated, by whom, and to what extent against what data set. Will they pick up variants, and if so which ones? With what accuracy? What are common field errors (collection problems, etc) that can lead to problems? What about manufacturing variance and supply chain issues?
You've raised the bar a long ways from except in a very small number of cases not actually validated in the field to perform that way to a comprehensive analysis of all tests that have even maybe been available.
Of course, the 'very small number of cases' makes it impossible to argue without doing a comprehensive study, but it's clear enough that the widely used commercial tests were developed with the idea that they should minimize false positives and tested against pre-Covid serum.
Do you have any data on which tests are widely used? (As in by number of tests done?)
I’m hoping it’s shifted the way you’re saying, but as of November there were more BS ones than legit ones, and it was hard to find a real validated test. None of the folks I knew getting antibody tests were able to find any of the validated ones.
That validated tests are good is great if they are being used, or used widely. Because in November the ones being used widely were still junk, which was my point.
If you have, per your study, 3 tests that are validated - are they used in the field for these studies? If so, objection withdrawn for current studies using them.
If they are studies based on the older, unvalidated, and often junk tests - then that point still stands.
it's not the virus but rather our bodies that are so mysterious. the virus is relatively simple in comparison (which is different from saying we know everything about it, since we obviously don't).
To get somewhat useful data, you’d probably need to do that for at least a couple thousand folks (10k each arm would be my guess), so you’re looking at what, 5 million total in the study, 2.5 million exposed? Based on the best data we had at the time (assumed 3.5% fatality rate I believe?), that would have been 87k fatalities out of that group. [https://www.who.int/bulletin/volumes/99/1/20-265892/en/ ], Currently seems like estimates are around 1-2% which would drop it to only 25-50k [https://www.nature.com/articles/s41392-021-00527-1].
For many very, very good reasons that isn’t how we roll right now though, and we do have good technology for solving this either. Star Trek style ‘simulate’ mode is a looong way away in this space.
Looking forward to it hopefully getting better soon though - a lot of money going into getting this figured out.
in general, we have trouble modulating our attention and resource allocation around unique events like this, and it behooves us to zoom out and put it in the proper context.
From https://www.reuters.com/article/us-health-coronavirus-scienc...:
Along with inducing antibodies for immediate defense, mRNA vaccines against COVID-19 also stimulate the lymph nodes to generate immune cells that provide protection over the long term, a new study confirms. The early wave of antibodies are generated by B cells called plasmablasts. In healthy volunteers, blood tests showed that two doses of the Pfizer/BioNTech vaccine induced "a strong plasmablast response," said coauthor Ali Ellebedy of Washington University School of Medicine in St. Louis. The immune cells that will produce antibodies upon exposure to the virus in years to come - called memory B cells - are generated by germinal center B cells found only in lymph nodes near vaccine injection sites, his team explained in a paper currently undergoing peer review for possible publication in a Nature journal. In repeated biopsies of volunteers' lymph nodes, "we saw a robust germinal center response," Ellebedy said. The responses lasted at least seven weeks, "with no sign of cooling down anytime soon," he added. "While we do not have long-term samples yet, it is safe to assume given the magnitude and persistence of the germinal center reaction that those individuals will develop a durable immune response" to mRNA vaccines. Moderna Inc's vaccine also uses mRNA technology.
this list is not comprehensive.
From what I can find online, there's a correlation, but it's pretty light on details about how strong it is.
> Sense of "indisposition involving catarrhal inflammation of the mucous membranes of the nose or throat" is from 1530s, so called because the symptoms resemble those of exposure to cold; compare cold (n.) in earlier senses "indisposition or disease caused by excessive exposure to cold" (early 14c.), "chills of intermittent fever" (late 14c.).
Put less technical: shivering, runny nose, etc, are symptoms of both the common cold and being cold.
It seems it's best to think of "cold" and "flu" as totally informal terms with a wide range of possible meanings. I've had Actual Real Influenza twice and both times it knocked me on my ass for a week, lost weight, etc. yet I hear people saying "I have the flu" when they have mild symptoms and only miss a single day of work. Hard to take such a wide range medically seriously.
They could have had influezna with mild symptoms. Two different people can have the same influenza virus enter their body and experience different effects. Anywhere from no symptoms at all to death.