2020, Personal, Society

A Journal of the Pandemic Year: Day 37

Continued from Day 36

I think one reason I don’t have much to say about our pandemic is because I have been putting my faith in the experts, to a much greater degree than I normally do.

From the beginning, my girlfriend was paying far more attention than I was and was more informed. And when I finally got off my ass to start getting informed, I found that I knew nothing about epidemiology. (Because of course I don’t.)

And given that I already work from home, it was very easy for me, in my privileged position, to accept the recommendations to not go near people.

But I must say, I haven’t been doing this entirely blindly.

For as long as I can remember I have been skeptical of the masses. If something was popular, I usually didn’t like it before I even understood it.

Once I was able to express this (semi) intelligently, I decided that widespread acceptance of something (especially something artistic) was usually a sign that the thing in question was bad.

I find this deep-seeded distrust of the masses to have attempted to infect my thinking about what is going on, and on more than one occasion.

I have found myself speaking to a few of those “Cure is worse than the disease” folks, and listening to them, if never actually agreeing with them. (I should point out that I’ve listened to them only for a time, eventually I’ve given up.)

I’ve also found my way into reading some fairly esoteric, but possibly quite important, debates with the statistical community. I’ll summarize them crudely as follows:

  • The pure math folks and the applied theory folks think epidemiologists are not good statisticians, that the field attracts low calibre people.
  • The epidemiologists think the pure math and theory folks don’t understand biology or virology.

Both sides think the others’ models wrong, math folks because epidemiologists are supposedly bad at probability and statistics, and epidemiologists because they believe that the theorists don’t understand how viruses (and people) actually behave in the real world. It’s very possible that both of these criticisms are right to some degree or other.

I can tell you, though, that, having met more than a couple pure math folks, they think everyone who goes into applied math is dumb, not just epidemiologists. It’s possible that this is just a bias of pure theory people. (It’s true in disciplines other than mathematics, for sure.)

Moreover, if the epidemiological models have indeed erred and exaggerated the danger, this exaggeration caused leaders and people to take notice in a way in which more accurate models may not have succeeded. I am not a fan of “noble lies” – see, for instance, the CDC’s re: masks – but if this was an noble accident that saved lives, whether or not the statistical models could have been better is just a question of historical interest.

When dealing with uncertainty – especially a type of uncertainty that the vast majority of us have never had to face in our lives – it’s good to be too cautious.

Day 42

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