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Pains me to say it, but @ProfMattFox made a good point yesterday about the difference between ID methods and non-ID methods. I was thinking about that on my dog walk this morning.
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The distinction seems to be 2-fold. First, a lot of non-ID epidemiology is theory-light. I don& #39;t mean that as a criticism. Epidemiologists study the real world and we recognize that it& #39;s unfathomably complicated. We won& #39;t have an epi version of general relativity.
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In ID, there& #39;s a bit more fundamental (germ) theory. For example, for @ProfMattFox to get gonorrhea, he has to ... you get the point. There& #39;s a strong (albeit cringy, in this circumstance) theory that allows you to build a model (SIR models and their ilk).
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But there& #39;s another distinction between ID and non-ID models that i find interesting. Non-ID models are overwhelmingly statistical models: you collect data, then you fit a model to estimate parameters (simply speaking).
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There& #39;s a strand of ID epi that does that too, but the ID models that are getting press right now aren& #39;t (generally) statistical. They& #39;re based on probability rather than statistics. The model is built by chaining together a slew of conditional probabilities.
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What& #39;s the probability you become Infectious in the next time unit given that you& #39;re exposed? What& #39;s the probability you die in the next time unit given that you& #39;re infectious? That& #39;s the heart of compartmental models.
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(yes, yes, there are differential equations...but to estimate these you essentially rely on difference equations in one way or another)
total aside/
total aside/
In this way, the difference between ID epi and non-ID epi methods reminds me of when i taught the phd-level 1st semester stats inference class for epi/env health/health policy students.
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The first 1/3 of class was probability: laws, expectations, distributions. Then we needed to turn the corner to talk about statistics. Turning that corner was always HARD. I remember Andrew Gelman writing that once, so I was glad to have smarter company in feeling that way
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That disconnect is the same as the one between ID epi and non-ID epi. Probability vs Statistics, to put it simply.
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In biostats, there& #39;s no fundamental disconnect between probability and statistics. In epi, there is a disconnect between ID and non-ID. I suspect it& #39;s because we teach it that way. Non ID methods teachers sidestep ID methods in most courses.
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oh i& #39;m not totally sure what the appropriate way to add citations is in twitter but my point about non id epi not having strong theories comes, i think, from Sander in this talk: https://www.youtube.com/watch?v=-ZYwaQQpsz0">https://www.youtube.com/watch...
I& #39;m stopping after this tweet to go hang out with my kids, but i show this video every year in my 3rd semester methods class, @Lester_Domes , and they LOVE it. (or they love me not talking...hard to say)
