More Data-mining
This article is an update to
That article delivered implausibly large extrapolated values. For forecasting a couple of weeks, it might be fine. But for more distant forecasts, it is clearly flawed. So I played with a more standard, but still ad-hoc, S-shaped curve:
This has the virtue of simplicity. For Italy, the US, CA and NY, the values come out to be
The large outlier here is the US. Italy, NY and CA look quite similar in their coefficients. Note that the coefficient A gives the long run proportion of the population that will die; the US is an order of magnitude worse than Italy or California or New York. Indeed, over half the predicted deaths in California have already happened, while less than 5% of the eventual US deaths, which exceed a million.
As with the previous model, the fit appears fairly good.
As before, I've posted the spreadsheet where I did the calculations at
https://mcafee.cc/tmp/DeathsProjection2.xlsx
The sheets ending in 2 (e.g. Italy2) are the new ones; the earlier sheets refer to the previous article's calculations.
Is it possible that the US does not work as well as CA and NY because it is a mixture of those curves from different areas that are at different points on the curve? That is, even if every county or state is on one of these curves with parameters similar to the ones you found for CA and NY, when you try to estimate the curve for the aggregate, it does not work?
Very interesting! Are you going to update the spreadsheet with new data? Also, have you tried to apply that methodology to other countries (like Japan to South Korea?).