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Why It’s So Freaking Hard To Make A Good COVID-19 Model

Investing related I think because if they're having trouble modeling it how do money managers and investors?


  • Because of faulty data.
  • I think it is more a case of confounding factors for which we cannot control. Data is data; it really can't be 'good' or 'bad'. The collection design can be poor; or we may not be able to isolate a single variable, but that's about it as far as the data involvement goes. Where we tend to screw up is in the INTERPRETATION of the data; especially data which contains confounding factors. People then tend to see what they want to see or, indeed, see things which are entirely illusory. I think the article did a good job of showing the difficulties involved in collecting data for which evaluation would be straightforward.
  • edited January 9
    Well, to quibble about "good" or "bad" data, what about the 737Max? Data from a faulty sensor was input to a computer which essentially caused two aircraft to crash. (This of course is a very simplified, but still factual description of the complex circumstances of the crashes.)

    Was that data good, or bad?
  • Old_Joe said:

    Was that data good, or bad?

    I could nitpick and say it's imaginary 'data', or not really data at all, but that's just splitting hairs. In truth, I wasn't thinking about completely bogus 'data' as being a possibility; I more or less presume that when I'm measuring something, it is really there! Good catch and spot on, Old Joe.
  • @racqueteer- Thanks. I'm perhaps a little more prone to that perspective than other people because I spent years working with data systems that controlled, measured, and reported on San Francisco's public safety communications systems. For example, the status of an emergency generator at a remote radio site, or for that matter, on the activity of the radios themselves.

    That sort of experience teaches one to trust nothing absolutely. ;)
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