@freemo Have you seen the USC antibody study results? Interesting, and somewhat expected https://news.usc.edu/168987/antibody-testing-results-covid-19-infections-los-angeles-county/
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@obi indeed, not surprising
@realcaseyrollins thats what it has always seemed to me, but what do i know @freemo
Nothing about this changes the fact that it was as serious as they said. We always knew, and accounted for, the fact that many more were infected than tested. We also always knew that a large portion were asymptomatic though this doesnt change the fact that a large portion still died, these facts arent at odds either.
@freemo @realcaseyrollins I know its a lot to base off just one study, and to scale it towards the entire population, but if it were to that scale in that range (2.8-5.6%) wouldn't that bring the mortality rate down to somewhere between .02 and .06%? That added with the fact that COVID19 attributed deaths dont require a positive test, I'm just saying its not as dire as the media makes it out to be. Of course a lot of people died. A lot of people are always dying. Never good, but we don't do shit about the rest.
No there are sooooo many things wrong with that assumption.. putting aside anything to do with scaling that figure to the whole population you are forgetting one very fundamental fallacy in your thinking.. its called the False Positive Paradox..
In any disease where the number of people who have the disease is a minority of the population, even if the test for the disease has a very low false-positive rate then when you randomly sample and test the population the **overwhelming** majority of positive results will be false-positives.
this is a more specific form of the Base Rate Fallacy logic: https://en.wikipedia.org/wiki/Base_rate_fallacy
No I never said that. No studies show a high infection rate in terms of percentage of the population infected. New york city, for example, one of the worst hit is around a percentage point, most areas much lower.. it has a high R0 but the total number infected in terms of percentages are relatively low thankfully.
As far as I know that too was the same test as we are discussing here so subject to the same False Positive Paradox... point is we need other types of information, like what I discussed above, to really draw any sort of conclusion either way.
I never said the study is worthless, its very valuable, it just doesnt draw the conclusion you (or the media) seems to think it draws.
Data and studies aside if we just look at the high profile people being infected and dieing or otherwise having severe complications should tell you something.
Actor Nick Cordero just had his leg amputated due to getting COVID-19, when the last time you heard of any celebrity getting a leg amputated due to the flu? Or all the high profile people who have died from COVID-19, again, when was the last time you heard of any famous person even dying of the flu?
Any studies or numbers aside its pretty clear even if this is over or under hyped, its a pretty serious disease.
@freemo Truthfully i don't know of anyone famous who has had it bad. Never heard of this guy you referenced lol @realcaseyrollins
Thankfully they havent been huge actors, but famous enough I suppose:
Well that wasnt really the conclusion in the first place.. thought by whom?
All the experts up until then and still now at the moment would say the same thing.. we dont know the number infected very well.
The link i provided explained why the False Positive Paradox means that we cant draw conclusions from the test results without other types of data needed to calculate the true-positive rate.
We would need to first know the actual incidence of the virus in the population along with the false-positive rate of the test, with those two pieces of data then we can conclude meaningful results from the test.
Basically your working backwards, your trying to use a test to determine incident rate when you need to know the incident rate first in order to interprit the results of the test. which is exactly why we need other types of data which we dont have before this particular data comes useful.
@freemo @obi I thought you have to have had the #Coronavirus before you can get the antibodies. Would that count as an "incident"? Perhaps some of these people are naturally immune and were born with antibodies.
False positives happen for numerous reasons. technical inaccuracies of a test (such as detecting antibodies from another source or similar) can be one, but it can also include things as simple as human error, cross contamination, and countless other reasons.
The reason it is called the false positive paradox is exactly because of the reaction you are exhibiting now.. it is counter intuitive to what your instinct tells you when interpreting such a test.
I usually find when dealing with counter intuitive logic it can be helpful to think about it in the most extream case to help you see why it is the way it is... imagine some imaginary disease that is very rare, no one knows how rare it is though (just as we have no clue with coronavirus how common it really is).. so we develop some test to test for the disease so we can figure out how common it is.
Now lets say, for simplicity, we know due to human error or whatever other error inherent int he testing process that 1% of the time the test will say someone has the disease when they really do not (false positive). Lets also assume that in reality (though unknown to the scientists) only one person in the entire world actually has the disease, or for simplicity sake, no one does.
So armed with our 99% certain test we test the entire population of the world in an effort to determine how wide spread the disease is. What result would we get?
Well according to our 99% reliable rest 78 million people in the world have the disease (we tested 7.8 billion and 1% of them got a false positive).. Since we dont know the actual number of people in the world who had the disease, or even the the false positive rate of the test going in, we will happily conclude the disease has reached 78 million people even though in reality the disease is non-existant.
In other words.. the test is only actually helpful to us if we know the incident rate in the population and the false-positive rate of the test.. without these additional data points the test doesnt tell us anything of value.
Depends.. it has long been the case that laypeople reading studies, or even news media outlets reporting on them, have done more harm than good. Its pretty common for the media and laypeople to draw incorrect conclusions that professionals might know better not to do.
The study **is** valuable, it just doesnt draw any conclusions on its own and will simply be part of a bigger puzzle as time goes on. But the lay person is likely to draw premature conclusions from it anyway.
Among scientists its a huge gripe about how people and the news tend to report on studies. Its almost always wrong.
The study **does** mean what it says, it just doesnt mean what **you** say it means.. the fault isnt in the study or its claims, its in your interpretation of it.
@freemo @obi I mean, if scientists say "more people had #Coronavirus than we thought", I'm inclined to believe them, personally. But again, I respect your opinion.
The study does not say that, only the news which reported on it did.. so no "scientists" are saying that, its not even a scientific statement (how do they measure the percentage people "thought" had it, and are you talking about people who had coronavirus or people who had COVID-19)...
I think what your really trying to say, but lack the scientific verbage to express it is... coronavirus doesnt always manifest as COVID-19, it has a high incidence of asymptomatic carriers... which is entiery true (and what a lot of scientists would agree with including the ones who did that study). but you have to understand in technical language your saying something very different than "COVID-19 isnt very deadly".. both because coronavirus doesnt always cause COVID-19 because it means something slightly different, and also because of reinfections.
Also its a lot easier to know that asymptomatic carriers are high than it is to actually claim 5% of the population had the virus... so your kinda getting yourself stuck in the weeds in multiple ways here.
@freemo @obi "USC and the Los Angeles County Department of Public Health on Monday released preliminary results from a collaborative scientific study that suggests infections from the new coronavirus are far more widespread — and the fatality rate much lower — in L.A. County than previously thought...The antibody test is helpful for identifying past infection, but a PCR test is required to diagnose a current infection."
I see no reason to disagree with this, nor any evidence that this is false.
Those arent words spoken by a scientist.. your welcome to beleive it or not, but do **not** try to claim this is something the study or scientists said. It is a layperson's interpritation of it, and not really a technical statement.
notice the words "preliminary" and "suggests" as well, also notice they dont even say who "previously thought" it.. are they talking about popular opinion? Its not a very technical statement but if they are just saying that a bunch of americans sitting home drinking beer thought the virus wasnt as wide spread as it is and this study is evidence they are wrong.. well sure thats fine.. but its not really a surprise to scientists who havent really made much of an assertion either way how wide spread it is, most admit we dont have the data to say that yet.
@freemo I.e Noam Chomsky, one of the most respected Cognitive scientists and liberals (I've always loved his logic), came out recently endorsing Biden, someone who is obviously in cognitive decline, and said if u don't vote Biden its a vote for Trump....grrrrr
@realcaseyrollins or more simply, just cuz u r a scientist, it doesn't mean what you say is correct
