[Goose]

I read through the methods, and this isn't even a model. It's a curve fitting exercise*.  They are simply fitting a normal (bell-shaped) curve to the death counts.  There are past examples (like HIV) where fitting this kind of curve gave wildly misleading results.  I would put zero stock in these predictions.



*curve fitting is fine for describing the distribution of past events, but it's useless for prediction.

Could you please post the link you used to get that methods section?

http://www.healthdata.org/sites/default/...2020_4.pdf

Thank you sir! After looking at what they are doing, I believe it isn't quite as bad as you say, although at the end of the day I totally agree with your conclusion. The fact they are using a Gaussian function to fit the curve has nothing to do with the "normal" distribution. It is just that this particular sigmodial function fit the Wuhan data better than any other function they tried. They did not quantify how good a fit it was, as far as I can tell. This approach makes some sense if one believes that the projected death rates predict hospitalization rates and case load, and that that curve has the same basic shape everywhere, just with different parameters. IOW, everyone is like Wuhan except with a timescale that could be stretched/shrunk or shifted ahead/retarded back. There are so many other assumptions about how these parameters are inter-related that there is almost no way their model could be correct. They have to "fit" curves to the data in Santa Clara for example only knowing the initial values. Hard to do even if the distribution will end the end look like Wuhan, and it very well may not for many reasons they aren't even aware of. There are so many differences that the idea this will be the case is hard to believe.

One thing they also did not present is a discussion of how well their methodology would have predicted results in Italy, Spain, France etc. had it been applied early on. I suspect it would not have fared well.

Curve fitting can indeed be a good method of predicting the future if you have good reason to believe that the present event will behave basically like the past event subject to factors that can be parameterized by the curve. Sometimes, there are good reasons to believe that it will, especially if there have been many "similar" events that did in fact behave that way. However, if it is an event that you have never seen before, it is a big assumption that the next such event will behave similarly.

[Goose]

I read through the methods, and this isn't even a model. It's a curve fitting exercise*.  They are simply fitting a normal (bell-shaped) curve to the death counts.  There are past examples (like HIV) where fitting this kind of curve gave wildly misleading results.  I would put zero stock in these predictions.



*curve fitting is fine for describing the distribution of past events, but it's useless for prediction.

Could you please post the link you used to get that methods section?

http://www.healthdata.org/sites/default/...2020_4.pdf

Thank you sir! After looking at what they are doing, I believe it isn't quite as bad as you say, although at the end of the day I totally agree with your conclusion. The fact they are using a Gaussian function to fit the curve has nothing to do with the "normal" distribution. It is just that this particular sigmodial function fit the Wuhan data better than any other function they tried. They did not quantify how good a fit it was, as far as I can tell. This approach makes some sense if one believes that the projected death rates predict hospitalization rates and case load, and that that curve has the same basic shape everywhere, just with different parameters. IOW, everyone is like Wuhan except with a timescale that could be stretched/shrunk or shifted ahead/retarded back. There are so many other assumptions about how these parameters are inter-related that there is almost no way their model could be correct. They have to "fit" curves to the data in Santa Clara for example only knowing the initial values. Hard to do even if the distribution will end the end look like Wuhan, and it very well may not for many reasons they aren't even aware of. There are so many differences that the idea this will be the case is hard to believe.

One thing they also did not present is a discussion of how well their methodology would have predicted results in Italy, Spain, France etc. had it been applied early on. I suspect it would not have fared well.

Curve fitting can indeed be a good method of predicting the future if you have good reason to believe that the present event will behave basically like the past event subject to factors that can be parameterized by the curve. Sometimes, there are good reasons to believe that it will, especially if there have been many "similar" events that did in fact behave that way. However, if it is an event that you have never seen before, it is a big assumption that the next such event will behave similarly.