Most people don’t get that “Harvard” isn’t one thing, it’s three: a fictional college, a shorthand for elitism, and a real school with smart, dedicated people

The administration thinks they are attacking the first, justifying it with the second, and it’s the third who get hurt When you see a movie or a tv show, far more often than proportionally, one of the characters went to “Harvard”. Stories are set there. It’s a shortcut, a signifier to avoid having to write more backstory.

That place doesn’t exist

Yesterday, the NIH R35 “Outstanding Investigator” grant to fund scientists in my lab studying antibiotic resistance was terminated for reasons not related to the content of the science, or any actions taken by me or members of my lab
This is a project and an area I've been working in for years. We've published numerous papers on the topic, and recently went through a competitive renewal in which the science of the grant was rigorously evaluated and supported.

And it was cut for cheap political points

I know a few science writers follow me, and today I'm asking you to please, please learn about Poisson Noise (or Standard Error). It's an estimate that basically means if you see N events, you really can only trust that to within +- sqrt(N) Otherwise you might make an embarrassing mistake like thinking these two numbers mean different things, and aren't just sampling noise:

https://twitter.com/apoorva_nyc/status/1486465856938909698

There’s no doubt N95/KN95/KF94/FFP2 is better than surgical is better than cloth but the numbers in this graphic are classic data bullshit

https://twitter.com/seungminkim/status/1478867026542219264

When you see things like this, ask yourself:

Do we know what an infectious dose is? (not really)

Does it make sense the numbers are symmetric? (No)

Does 15 minutes for unmasked/unmasked make any sense? (Definitely not)

Is there a dependence on distance? Ventilation? Why not?

This quote, from a hopeful article on omicron, is a profoundly misleading understanding of evolution.

Viruses do not "want" anything, they have no idea what they're doing. They just mutate and what happens to be a more effective strategy survives. The idea that a virus expectedly mutates in a way that's to its advantage is getting causality backwards. It mutates randomly, and the advantageous one wins.

But mortality doesn't actually need to be under selection, particularly when it tends to occur weeks after transmission

Since it's World Antibiotic Awareness Week, let's revisit the recent article in which Llewelyn et al. convincingly argue that it's time to drop the message that not finishing a course of antibiotics contributes to resistance. Because it's incorrect

#WAAW
bmj.com/content/358/bm… I once tried to track down the origins of this idea. I found dozens of papers claiming it, citing others, which cited others, and every chain went back to one source: Fleming's conjecture at his Nobel speech. Neither theory nor evidence support it.

Very excited to share our latest preprint on how to estimate SARS-CoV-2 variant abundances in wastewater. A large joint effort with @jasmijnbaaijens, Alessandro Zulli, @IsabelOtt, @cduvallet, @BillHanage, @NathanGrubaugh, @jordan_peccia, and more 1/

medrxiv.org/content/10.110… Others have worked on this problem, but there's a fundamental issue with trying to quantify variants: the mutations that define variants are so far apart that they never appear on the same read, and often not even on the same molecule(!) in heavily degraded wastewater RNA 2/

I don't think it's widely appreciated how incredible an achievement this is. Biotechnology has advanced unbelievably in the last fifteen years, but even still, going from new virus to completed phase 3 clinical trials in eleven months is like... I can't come up a good metaphor

https://twitter.com/eliowa/status/1329614738247069697

Maybe announcing a Mars program and landing a crew twelve months later? It's certainly on par with the Manhattan project.

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/

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.

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

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.

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/

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/