Don't think there's been any point in the pandemic at which there's been such a confusing mix of good *and* bad COVID news. I actually think the good > bad, but there's plenty of both, and it's worth thinking about how people react to the uncertainty and confusion that creates.
I guess what I'm getting at is that uncertainty demands nuance, but ironically, people aren't looking for nuance at times of greater uncertainty! They're tired of the uncertainty and want simplicity and even dogmatism.
One obvious example is you've seen an uptick in people scolding images depicting both behaviors that are quite dangerous (that supermarket in Florida where no one's wearing a mask!😬) and others showing e.g. relatively safe outdoor activities. We've lost some of the nuance there.
"How should people change their behaviors once they've been vaccinated but there's still a lot of community spread?" is another question that requires nuance but often isn't getting it.

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More from @NateSilver538

5 Jan
There's this lazy critique that "the mainstream media isn't taking the 'coup' seriously". Really? Have you actually read the articles that e.g. the NYT and WaPo are writing? This is the first article I found on NYT.com today. nytimes.com/2021/01/04/us/…
I criticized the NYT and other outlets a ton from mid-2015 through mid-2017 for how they covered Trump, including this sort of "news analysis" piece that was prone toward tired tropes and false equivalencies. These stories have changed a LOT since then, in my view for the better.
And, yes, sometimes you have to analyze the political incentives of the relevant actors, which may seem banal. And sometimes you have to assess the likelihood of success (exceedingly low). That is part of the story, and moreover, part of taking the story *seriously*.
Read 4 tweets
1 Jan
While this might sound like posturing by de Blasio, New York indeed has a fairly complicated set of categories and subcategories for who is in which tier and it's plausible to think that's slowing things down.

nytimes.com/2021/01/01/nyr…

And you know what'll probably be worse? Having hard-to-define categories like essential workers or pre-existing conditions. There are lots of borderline cases (and some people who will try to cheat the system). Who's going to verify who qualifies? Big administrative burden.
Rigorous verification will take time and slow things down. Lax verification will make things a free-for-all. I don't think these plans are well thought out. If you're going to have subcategories, make them narrow, specific and easy to verify (e.g. "public school teachers").
Read 4 tweets
18 Dec 20
At least with essential workers—if you can define the scope narrowly—there's an argument to be had about reaching herd immunity sooner. What will literally kill people is treating a broad set of preexisting conditions as being as important as age.
If you look at the research, virtually all of people at highest risk of dying from COVID are aged 70+. There are almost no preexisting conditions that matter remotely as much as age.

nature.com/articles/s4159…
But ACIP defines *more than 100m people* as having "high-risk medical conditions" that put them in the same priority tier as people age 65+ for vaccination. This is NOT following the science. States that want to save lives must give age higher priority. cdc.gov/vaccines/acip/…
Read 4 tweets
17 Dec 20
As @Neil_Paine talks about here, we also made a couple of important changes to our NBA projections this year. I'm going to simplify this a bit, but basically it comes down to PLAYER-BASED projections vs. TEAM-BASED projections.

LONG DORKY THREAD 1/

fivethirtyeight.com/features/what-…
In the past, we've really used two ways to project NBA games. One is TEAM-BASED solely using team performance, e.g. via Elo ratings.

Advantage: captures team "intangibles" (coaching, cohesion, recent form)

Big disadvantage: doesn't account for player personnel changes. 2/
Alternatively, PLAYER-BASED projections work by summing up the projections for individual players, e.g. by using our RAPTOR ratings. That is, team performance is assumed to be ~equal to the sum of the parts. This has the opposite set of strengths and weaknesses. 3/
Read 17 tweets
12 Dec 20
The chances that the election would be close enough that late-arriving ballots in PA would be enough to change the overall outcome was maybe something like 0.5% or 1%. (There were not many of these ballots.) So if that's what people were concerned about, it was overblown.
When I pointed out though that yes the Supreme Court could determine the result in a *very* close election but it would likely have to be very close indeed under certain specific scenarios, I got yelled at on this platform for not taking the chances seriously enough.
If you want to take the position that the election aftermath went even worse than expected in some ways, but better than expected in other ways—including the Supreme Court—that seems reasonable and prudent! It's imperative to take stock of what happened.
Read 4 tweets
10 Dec 20
This seems like a fairly realistic set of assumptions for what to expect on the COVID-19 front next year as vaccinations begin to roll out. Things start to get notably better by ~April but it takes until mid-to-late summer before we approach herd immunity.
covid19-projections.com/path-to-herd-i…
The one major downside scenario is if vaccines prevent disease but don't do much to curb *infection*. Otherwise though these seem like pretty middle-of-the-road assumptions. I think he may underplay the role of seasonality a bit, which could help in the summer.
The other thing that seems likely is there will be some pretty fierce debates in that interim period from April-July or so about faster or slower paths to reopening.
Read 5 tweets

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