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Michael Baym

@baym

30 Jul

@LeeKShaffer

Excited to share my latest preprint with @LeeKShaffer and @BillHanage, “Perfect as the Enemy of the Good: Using Low-Sensitivity Tests to Mitigate SARS-CoV-2 Outbreaks” in which we show how the math of superspreading events can improve contact tracing 1/
dash.harvard.edu/handle/1/37363…

The key idea is: if A is sick and has contacted B, B is probably still fine, but if you also know that A has infected C then there's a much better chance that B has been infected. Superspreading (or overdispersion) means that infection _events_ are correlated 2/

Here's where testing comes in: because B and C being infected is correlated, you don't need a test that gets both of them right all the time. Either one testing positive gives you information. So a low sensitivity test on all of A's contacts is almost as good as a perfect one. 3/

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Michael Baym

@baym

30 Mar

I see a lot of motivated reasoning as to why this can't be as bad as serious models predict be without massive societal action. And I know these are desperate attempts to reason why the world must be similar to past experience, but it's hard to be sympathetic.

But all those thinkpieces two or three weeks ago, what did they accomplish? They sowed just enough doubt to slow action (and apparently some Medium posts got the ear of the White House). And now we are seeing the tragic consequences of insisting the world must be as you hope.

The curve is bending, our fates are not fully sealed. Hold the line. Keep distancing, be safe to reduce the background rate of hospitalization. Listen to epidemiologists with the experience to understand the complications and nuance. This will be a long fight.

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Michael Baym

@baym

15 Mar

I just did an updated calculation of what happens to America if we do nothing. And it is nothing short of terrifying.

The current rate of spread is a near-perfect exponential. If we do not change our behavior dramatically and fast, here is what the math says: 1/n

The last eleven days give a remarkably good fit for linear regression on the log cases (R^2=0.9981), that's good enough to project the exponential. Here's what happens:
~March 18th the US passes 10k cases
~March 26th we pass 100k cases
~April 4th the US passes 1 million cases

If 10% require hospitalization:
~April 5th the US passes 1.6M cases, or 10x the number of ventilators
~April 11th we 9.24M cases, or 10x the total hospital beds

If we do not change our behavior, by early April the entire US medical system will be treating critical Covid-19 cases

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Michael Baym

@baym

7 Mar

The reason to cancel meetings and seminar visits is the same reason we have them in the first place: by establishing long-distance connections and high-connectivity nodes, we help ideas spread much faster through our social networks. It's the same for a virus.

More math: Locally, early in an outbreak, the expected impact of a social event scales as the number of people times the number each interacts with. Roughly the attendance squared.

Therefore cancelling a 50 person event is over a 1000 times as important as cancelling a 1-1.

Of course this eventually becomes linear, at a 10,000 person event you can't possible interact with everyone. But the point remains that from an outbreak-spreading perspective large events are disproportionately more important than small ones.

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Michael Baym

@baym

5 Jan

It’s the season for grad school interviews. I’ve been doing these a couple years now (for a few different programs), and in the interest of dismantling the hidden curriculum, here’s how I’d interview you and what I’d look for: 1/

(Before I go on I want to emphasize that this is just how one person at one school does interviews. It is not universal, and you should take this as a data point and nothing more.) 2/

Most importantly, I want to see that you are prepared for the rigors of grad school This means that you have the academic training (and if you made it to the interview you almost certainly already do) to not be overwhelmed and the motivation to push through the difficulties. 3/

Read 16 tweets

Michael Baym

@baym

6 Apr 19

@nytimes

While I'm happy today's @nytimes front page is bringing attention to antibiotic resistance, I'm not sure war is a great analogy. In war there are two (or more) parties who know they're fighting and are trying to defeat one another. That's not really what's going on here. 1/

Sure, we are trying to kill bacteria before they can kill us, but they aren't doing that at all. They're just surviving in whatever environment they happen to be in, and adapting to whatever evolutionary pressures happen to be on them. It's not adversarial. 2/

This means that we can potentially predict what will evolve as a result of specific actions, and plan them accordingly. We aren't outsmarting an opponent, we're learning the rules of a system that's more complex than we're used to, and that difference matters. 3/

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