[Goose]

If smoothing over that noise by taking cumulative sums improves precision, it's a misleading result.  I just did a quick simulation using linear regression on noisy data, and the cumulative sums produce more precise slope estimates.  But it's an invalid result because the residuals of the model are highly autocorrelated.

Exactly. And this auto-correlation allows one to predict future data from past data. ARMA predictors are exactly that. If you look at the formulas for a linear regression, it involves taking the mean of the data and the standard deviation of the data. Obviously, this mean is an average, and an average of averages is the average of the original sequence, except at the edges. You are correct that predicting the value of the sum of variables is easier than predicting the individual values, especially if there is random noise in the data. Another way to say this is that the standard deviation of a measurement is proportional to 1/sqrt(number of samples).   The problem isn't with averages, it is with the overall applicability of the model. The model in the UW paper is predicting cumulative deaths per unit time. Differentiating this curve produces deaths per unit time. The derivative is always noisier than the sum. Another way to look at it is that 100 tosses of a fair coin doesn't exactly predict the 101st toss, but is a pretty good predictor of 200 tosses.

Anyway, none of that matters because the covid-19 models here have zero predictive value even if the model calculations were all done correctly.

And you know that how? I agree that this particular model has issues, and I have stated what I think they are. It isn't that the calculations were done incorrectly. it is rather 1) given the model proposed, how well does it actually match the total deaths in the Wuhan outbreak  on a day to day basis (which is what it is supposed to do)? and 2) Why should we expect outbreaks elsewhere to be accurately predicted with the same function(with different parameters)? That latter condition is I think a tougher nut to crack. They sort of assume it is true. If they presented lots of data showing that their model did in fact "predict the past", i.e. it fits the last N days well, maybe that would be an argument it can predict the future. That data is not provided, as far as I can see, and I don't know if they have it. If that data is presented, and looks good, there is reason to believe this model may have predictive value, subject to any previously observed "RMS" errors. I think this model deserves a "don't know" rather than "zero value" rating.

[Goose]

If smoothing over that noise by taking cumulative sums improves precision, it's a misleading result.  I just did a quick simulation using linear regression on noisy data, and the cumulative sums produce more precise slope estimates.  But it's an invalid result because the residuals of the model are highly autocorrelated.

Exactly. And this auto-correlation allows one to predict future data from past data. ARMA predictors are exactly that. If you look at the formulas for a linear regression, it involves taking the mean of the data and the standard deviation of the data. Obviously, this mean is an average, and an average of averages is the average of the original sequence, except at the edges. You are correct that predicting the value of the sum of variables is easier than predicting the individual values, especially if there is random noise in the data. Another way to say this is that the standard deviation of a measurement is proportional to 1/sqrt(number of samples).   The problem isn't with averages, it is with the overall applicability of the model. The model in the UW paper is predicting cumulative deaths per unit time. Differentiating this curve produces deaths per unit time. The derivative is always noisier than the sum. Another way to look at it is that 100 tosses of a fair coin doesn't exactly predict the 101st toss, but is a pretty good predictor of 200 tosses.

Anyway, none of that matters because the covid-19 models here have zero predictive value even if the model calculations were all done correctly.

And you know that how? I agree that this particular model has issues, and I have stated what I think they are. It isn't that the calculations were done incorrectly. it is rather 1) given the model proposed, how well does it actually match the total deaths in the Wuhan outbreak  on a day to day basis (which is what it is supposed to do)? and 2) Why should we expect outbreaks elsewhere to be accurately predicted with the same function(with different parameters)? That latter condition is I think a tougher nut to crack. They sort of assume it is true. If they presented lots of data showing that their model did in fact "predict the past", i.e. it fits the last N days well, maybe that would be an argument it can predict the future. That data is not provided, as far as I can see, and I don't know if they have it. If that data is presented, and looks good, there is reason to believe this model may have predictive value, subject to any previously observed "RMS" errors. I think this model deserves a "don't know" rather than "zero value" rating.