What are the most pressing scientific issues and debates around the mathematical modeling of #Coronavirus/#COVID19, and are we actually solving them? (thread) 0/n
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(for those who prefer blog post format instead, a slightly edited version of this thread can be found here: caesoma.github.io/posts/2020-06-… and medium.com/@caesoma/are-w…)
I am strongly in favor of distancing measures and -- in the absence of absolutely flawless test/tracing -- lockdowns, It's the only real tool available. I also strongly oppose *herd immunity* as a "strategy", and think we are likely not close nor should count on it. (thread) 1/n
That's a strictly epidemiological opinion. I have my own personal opinions about the impact on the economy and society, the protests, and other non-public health aspects of the pandemic, but that's beyond my expertise and won't get into that. 2/n
That said, there's very little debate of actual scientific questions around the pandemic, including herd immunity. For the most part there's the stuff we already knew, there's straightforward application of fundamental principles, and then there's the crazies. 3/n
The crazies are people that for whatever reason, usually political, will seek out information that confirms what they wish to be true, and don't care about (and are often not qualified for) a rational discussion of the body of knowledge available 4/n
Now because of the crazies, many prestigious scientists/instant subcelebrities are doing basic shit so we can justify stuff we already know: avoid contact with infected people, wear masks just in case, keep track of transmission routes, incidence rates. That's not their work 5/n
That should probably be done by the WHO, CDC, etc (or commissioned by them), if they had dedicated funding. Maybe the UK is who has something closest to this (SAGE). 6/n
The British reports in late March were basic modeling, reused some code (imperial.ac.uk/mrc-global-inf…) and then provided some initial estimates of intervention impact (imperial.ac.uk/mrc-global-inf…). It's not brilliant work, but it's what was needed at the time. 7/n
I argued against unfair criticism of this work and the crazies. Also, at this point other herd immunity studies were taken out of context, while some apparently respectable researchers decided really try to get data showing it was close, but that's not novel science either 8/n
But it's been nearly 3 months, and we need better assessments of the epidemic, so novel means whatever helps us "solve" this as fast as possible. The flip side is that there's also the need not to feed the crazies and cause worst case scenarios (see Brazil) 9/n
That has led to things like this here, and the @TheLancet herd immunity assessment in the beginning. These are simplistic, and useless at best, and very much the kind of thing that could've been reasonable in the beginning of the pandemic 10/n
We published work showing a potentially more optimistic scenario (how optimistic depends on the quantification of parameters that are still mostly unknown), and I knew it would probably be used by the crazies, so I tried to be as nuanced as possible 11/n
HOWEVER, it's been REALLY hard to get past the standard narrative/don't feed the crazies polarization so somethings just don't go into the discussion. Happens all the time in science, just now it's more deadly. 12/n
The bigger problem is (a fraction of) people are not stupid, enven if they don't have scientific training they can see that some work gets more attention, and it's usually what fits the preferred narrative. That undermines the credibility of science and scientists as a whole. 13/
This has also been great for some that are padding their resumes with basic work that can weasel itself into Science, Nature, Lancet, NEJM, PNAS... as long as you are recognizable enough and fit the preferred narrative. That also always happened, just now it's more fucked up 14/n
So What are the most pressing scientific issues and debates around the mathematical modeling of #Coronavirus/#COVID19?
The same as before the pandemic, if it's also useful now. But we are not debating those, we're just doing the work we already did, building our resumes, and letting the power structures keep science boring and dominated by the same people.
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