Official ONS data and article
News, analysis and comment from the Financial Times, the worldʼs leading global business publication
www.ft.com
View attachment 8249
*facepalm*
So you're talking about that bit that Agema already explained to you was a misrepresentative soundbite that you are incorrectly taking to imply a generalized point by exaggerating the scope of a specific data point and focusing exclusively on raw lethality without consideration for contemporary circumstance or other factors.
Analogy: Have you ever seen a creationist try to argue against evolutionary theory by pretending that either mutation or natural selection was not a component of the process? Similar idea here. What makes a virus dangerous is a synthesis of a variety of traits. You can't simply laser focus on one aspect to the exclusion of all others, but that is exactly what you are insisting on doing.
In this case, the claim you're making is rooted in a limited-scope statement that
the Omicron variant has, at this moment, statistically lower lethality on an
individual level. However, the claim that the Flu is more dangerous than Covid [in England] is a declaration that makes a false implication by going well beyond that scope. That's a scope applied to a
population, not an
individual, meaning that we also must consider additional factors beyond simple lethality for a given case, such as infectiousness. And Covid is significantly more infectious.
By the article's own account, despite the fact that people are still being cautious about spreading respiratory infections, Omicron still elevated the death toll by 50% over the typical flu season, and 30% higher than historically bad years. And don't get me wrong, that's much better than where we were when the Alpha strain first hit, but to take from that the conclusion that Covid has now been reduced to something - all else being equal - less dangerous than the flu [in England] is amateur folly, which the writers obviously understand as they're quick to hedge their claims to something more reasonable within the article itself.
Here: quick thought exercise. Which is more dangerous for a population? A virus that can infect 2000 people in a week but will only kill 2% of them, or a virus that can only infect 100 people in a week, but will kill 10% of them? If we judge by individual lethality, then we must conclude that it is the latter virus because the former kills 2 out of 100, whereas the latter kills 10 out of 100. But that's a snapshot of an abstraction that ignores infectiousness in favor of assuming that they both infect an equal number of people, which you cannot do when making a generalized statement about the danger of a virus to the population. Let's run the numbers real quick to see how this snowballs. For simplicity, we'll assume the spread exhibits linear growth.
Week 1: Virus A infects 2000 and kills 40. Virus B infects 100 and kills 10.
Week 2: Virus A's death toll is up to 80. Virus B's toll is 20.
Week 3: Virus A is up to 120. Virus B is up to 30.
Week 4: Virus A is up to 160. Virus B is up to 40.
Now that we've seen that, I ask again: Which virus is more dangerous
to the population? The numbers have not changed. Virus A still only kills 2% of the time, while Virus B kills 10% of the time. We just stopped employing tunnel vision for the on-paper mortality rate of the virus and started looking at what that means in practice considering how the virus spreads. Virus B is more dangerous to the individual, but its slower spread means that it's significantly less dangerous to the population than Virus A is. Virus A may kill more per infection, but Virus B kills more overall over a given time period and is much more difficult to contain because of how fast it spreads.
So yeah, about what I was expecting. You're not deferring to ONS data, you're skim-reading a Financial Times editorial's own quick-and-dirty calculations (which were partially derived from manipulating ONS data), and you aren't even representing that accurately because you are more interested in proving a point than understanding the subject. For goodness sake, the damn article notes that the experts are cautioning that a recent uptick in hospitalizations is a reason to be wary of exactly what you're pushing, implying that the cause is likely due to people being less cautious and their resistance waning.
You are trying to imply a ceteris paribus conclusion when ceteris paribus assumptions do not apply. The article understood the distinction. You did not.
IgG antibodies last too long to be any definitive answer to whether someone has long-term symptoms triggered by covid.
And yet again you're trying to argue as if I'm proposing some comprehensive hypothetical method that you can argue against rather than supplying you a cliff notes summation
of what doctors do and the methods your own sources employed. That you - a layman with little-to-no understanding of the topic - don't understand how they do it is immaterial to the fact that they do it.
Patient reported symptoms are not some horribly inaccurate way to do a study. The studies claiming long covid is something happening at some "high" rate use even worse methods. I've asked this many times from people here, show me legit data to proves long covid occurs more often than long flu, long RSV, long rhinovirus, long etc.
"A study"? No. I believe I've been saying from the get-go that this stuff has its place. The conclusion that
you're projecting onto the paper? It's absolutely terrible for that. Because again: Vague subjective measurements do not prove specific objective conclusions. This is not a negotiable point, Phoenix.
You're wrong, and you are misrepresenting the paper. End of story.
And really? We're back to the "prove it happens more often" bullshit? I believe I already got into that:
it's further worth noting that even if you had been right about comparative incidence rate, it would still be - once again - irrelevant, because concerns about Long Covid are not predicated on comparative incidence rate as contrasted with the long term aftereffects for other diseases. This isn't a footrace wherein all the prize money goes to the first person past the post.
Generously, that'd be what we'd call the Fallacy of Relative Privation (aka the "Appeal to Worse Problems" Fallacy), a positively puerile fallacy that insists that if something isn't the foremost example of its kind, it's not worth concerning yourself with. Eg, "Breast Cancer isn't as bad as Brain Cancer, so why do we make a big deal about being vigilant about it?" When you understand why trying to object to Breast Cancer screenings by comparing Breast Cancer's fatality rate to that of Brain Cancer is idiotic, you will understand why the argument you just attempted by misrepresenting that study is equally stupid.
