This is also why I'm not the biggest fan of arguing to care about Long COVID *because* of its economic effects. Because if we do, then does the other side darkly have "a point" when it comes to things like this? COVID is a humanitarian issue - life has no price.

Even if you choose to dismiss the long-term effects of COVID, claiming the pandemic was "overblown" from the start is simply incorrect - despite what some newly appointed figures in the United States now suggest.

It reminds me of when I used to work in EMS. People would call 911 and then tell us to leave when we arrived on scene because seeing EMTs made things feel more "real" and that if we went away, things may go back to normal. Makes me think that's part of why people hate seeing masks.

WHO classifies a rare disease as something affecting less than 65 per 100,000 people, 0.065%. Low estimates of symptomatic LC are often folds above that. To accept a new, repeating, not-rare condition is some sort of combination of psychological forces like denial and general economic forces.

We have another article that covers that here: www.peertopublic.com/posts/unders...

Ha, TTRPG DMs, the true stat masters. But totally correct. I remember being worried about 0.1% at first. To see that we are taking integer level risks... repeatedly. If you take a 1% fatal risk every day, you won't make it through the year. It's 95% chance you won't make it past 300 days.

In addition, I believe the internet is way too saturated these days so I try to lower my quantity footprint and increase my quality footprint by making less-frequent content that adapts to our ever changing world, and updating those instead of posting new things if need be.

The point is: taking precautions, like masking, based on what’s reasonably possible is simply being cautious and responsible.

The phrase is also used to create distance from responsibility. If an engineer makes a plane that instantly crashes or a baseball catcher misses a ball, claiming that the science of Newtonian physics changed doesn't cut it.

The masks drop because turbulence shifts the range of possible outcomes to include worse scenarios. They stay down until the meaningful set of outcomes no longer includes disastrous ones—either through landing or prolonged stability.

If a plane hits turbulence, O2 masks drop as a precaution. The captain doesn’t yell, “We’re doomed!” then say, “Oh wait, the science changed. Taking the masks back” after 20 seconds of calm—only to drop them again and repeat the chaotic cycle with every bump.

The phrase, "the science changed" has been used a lot during this pandemic as a polarized, flip-floppy alternative for consistent precautions. This approach reflects more on the competing motives within institutions than genuine scientific shifts, as it rarely applies elsewhere.

Given the evidence for COVID's long term harms (panaccindex.info/p/what-covid...), we have to at least include some "bad" outcomes in our bounded predictions. This is where we get to how to act under uncertainty. When bad outcomes are meaningfully possible, you act cautiously.

I don't know exactly how things will play out, so I make my charts flexibly to account for as many outcomes as possible. But because we need to act somehow and the set of all possibilities isn't helpful, we need to bound our predictions within reasonable ranges.

There have been and will be many predictions about this pandemic - both correct and incorrect. As Yogi Berra said, it's tough to make predictions, especially about the future. How then are we supposed to navigate predicting and acting under uncertainty? Flexibly and cautiously.

Thanks for the shoutout! The wait shouldn't be too much longer...

Ha, in that case I'd add "doesn't think about what it says until it's already said it."

And speaking from a #ComplexSystems perspective, one of the biggest hurdles to its widespread, public adoption is a form of underfitting - the centralized and deterministic mindset.

I argue that we often underfit the "data" in the world around us due to limited processing capacity, relying heavily on habits, cognitive biases, and heuristics. Confirmation bias, for example, favors info that confirms pre-existing beliefs, ignoring more complex patterns in new data.