Understanding Epidemiology Models

Re:Understanding Epidemiology Models by mistrial9 on Thursday June 04, 2020 @12:24PM (#23418611)

I pulled an epidemiology textbook from the 1970s, from a reputable author.. the math is very dense, but basically an extension of calculus-type curves from the 1950s or so.. The overall effect seems overbearing and self-referential (to my untrained eye). The serious gravity of the subject, with so many lives affected in an event, leads to a combination of erudition and also solemn, unapproachable pronouncements, it seems. Maybe there are useful aspects to this curve approach, in hindsight.. but, as mentioned in other posts here, data-driven feedback is now a thing, where it just was not a thing then..

lessons learned -- Question Authority!? use good tools .. look for data and use it well.. ask the right questions..

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Re:Understanding Epidemiology Models by avs733 on Thursday June 04, 2020 @12:40PM (#23418807)

you picked up a source from 50 years ago in a field you (seemingly) know little about and your takeaway was 'question authority'?

Some reflection on your own epistemic commitments and beliefs may be in order...

Honestly the ongoing discussion of COVID treatment and modeling in HN scares the pants off me given how uninformed but self-assured it is.

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Re:Understanding Epidemiology Models by mistrial9 on Thursday June 04, 2020 @01:27PM (#23419331)

I took some time to review something that was "authoritative" from 50 years ago, and yes, the carry-over to the modern times is not great. Does it make me weak to question it ?

I am purposefully not "self-assured" but rather, looking into the topic. As a learner, why would I not question the contents I found?

The textbook leaves no room for alternative -- in fact, the tone and presentation of the textbook is that the topic is settled, and the math is "best".

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Re:Understanding Epidemiology Models by mattkrause on Thursday June 04, 2020 @03:42PM (#23420876)

That's the nature of a textbook though, not the field.

Most textbooks present a pretty definitive take on a topic, if only for pedagogical reasons. You might get a few well-defined controversies, like various theories of matter in chemistry, or frequentist vs. Bayesians in a stats book. Even there though, that debate is often resolved, one way or the other, by the end of the first chapter.

The actual back-and-forth in most fields happens in journal articles and conference presentations instead.

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Re:Understanding Epidemiology Models by twic on Thursday June 04, 2020 @02:15PM (#23419890)

Having formerly been a medical scientist, i don't entirely trust medical scientists to get this stuff right. But currently being a computer programmer, i really, really don't trust computer programmers to get this stuff right.

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