4 ms·

This was a big issue in Covid case forecasting: changing the model polynomial/formula and then fitting it to recent case count data points, gives different fore

• ARandomerDude 4y ago
  [flagged]
  • dang 4y ago
    Please don't cross into unsubstantive comments or flamebait. "Comments should get more thoughtful and substantive, not less, as a topic gets more divisive." https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
• gus_massa 4y ago
  For a short time prediction a quadratic/cubic is usually not that bad. I guess for covid-19 cases a week is fine. More than a week is probably too much, because the peaks last like a month from beginning to end, so it's too weird for a simple model. I guess you need a SIR model for that https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology https://en.wikipedia.org/wiki/Compartmental_models_in_epidem... Also, the grow phase is quite exponential, so even for a polynomial fit I'd prefer to use it after changing the vertical axis. Otherwise the polynomial must fit too many almost zeros and then jump, and you will get weird and negative results. There was also a strong weekly variation, cases on weekends were written down on Monday (or Tuesday), so I think it's better to look at the 7-day running average. The problem is that in this paper they are extending the "forecast" too much. It's somewhat the equivalent of 6 months. I thin no serious source tried to make very long term forecasts. (Anyway, some trends and general forecasts are possible, like with the flu.)