We do not know if we saved any lives. Did we cure the disease with the lockdown? Did we delay the disease with the lockdown? Are we going to stay locked down until a vaccine is cured? Did we kill a lot more people than we saved with the lockdown?
So we don't know those things until we measure them, correct? Are you saying we can't reach that conclusion until we test people for antibodies? At what point are you willing to accept that what we did saved lives?
Because if you cannot accept that assumption, you cannot possibly accept the data extrapolation you suggest without testing the entire population.
I think it's a safe assumption we've saved lives based on the premise you propose (which as you point out are CDC
estimates) because for the disease to not be so deadly that means it was even more infectious. Ergo, shutting down huge public gatherings being productive SHOULD be a safe assumption.
Most reproduction trends(actual data not projections) show that the virus had already started declining before lockdown measures were put in place. The health minister of Norway believes that they likely could have achived the same success without a lockdown:
https://www.thelocal.no/20200522/no...olled-infection-without-lockdown-health-chief
Sure. And Sweden's herd immunity experiment isn't working as planned either, given they've only achieved 7% of such and have suffered far greater deaths than their lockdown scandanavian neighbors.
Stockholm Won't Reach Herd Immunity In May, Sweden's Chief Epidemiologist Says
I guess it's maybe just coincidence, but I'm a big believer in Occam's Razor.
This is not projections though. This is modeling from data we have. Not from data we predict. I don't know why you are so hung up on this aspect. The Imperial model code has been ripped to shreds in the community, to the point where he refuses to release the full code to the public. He also used data that was withdrawn from the medical community prior to his use(which is where the extremely high IFR data he used came from). Even if these were projection models, it does not mean that we have to accept all models. If someone puts together a model that is shit, then it can be questioned as many people did regarding the Imperial model.
If you get a chance, you should read the article I linked above by Dr. Ioaniddis, where he discusses projections.
You're right--
I'm extremely hung up on this aspect because it's selective absorption. The Imperial model is a red herring at this point. We're talking about models based on data available up-to-the-day and I'm grumpy as f*** that people are accepting as gospel, for example, CDC PROJECTIONS (whatever euphemism you want to use for them, I frankly don't care), when not even a month ago the discourse here was that 'you can't trust models.'
Again, malleability is important. I'm not faulting people for changing their tunes per se because as I said before we're all on a bad data ride together and just trying to make sense of what we get. But I have to call out confirmation bias. It drives me up the wall.
What flip flopping? Are you discussing me changing opinions? I have changed my opinion on the whole subject, I initially understood the concept behind "bending the curve". That makes sense to me, we do not want to overwhelm our healthcare system because that could lead to additional deaths. However, I did not agree with the lockdown until the disease is eradicated concept, especially when all of the numbers are painting this disease as significantly less dangerous than we initially thought.
Also, I voted for Bernie Sanders in the primary and am registered blue. As far as I know most democrats are big supporters of the lockdown, so I don't think I am being partisan with my opinions.
Not you in particular. I just notice a very large anti-projection crowd suddenly being pro-data-modeling when it's fitting their desired outcomes, even despite the existence of contrary data-based narratives (i.e. Sweden example above). People are picking and choosing study outcomes like they're buffet items with no skepticism at all and disregarding the rest with cynicism.
The reality is we're getting conflicting data and narratives from a variety of places with partisan tinges depending on state and we're STILL not testing enough to have such certainty in our assumptions. I think most people are on board with 'proceed cautiously,' but it's like there's a camp trying to prove caution is stupid (see: security guard getting killed for asking someone to wear a mask).