[burger]

Sorry, BC and Goose, but you are not going to convince me that these models are useful without data showing that they accurately predict the peak (in both timing and intensity), and we are way too early in this process for that.  The Gaussian curves they are using imply a specific relationship between the early "exponential" stages of the outbreak and the later leveling off and decrease.  There is no reason to expect that the Gaussian fits better than any of a number of other potential curves.

Imagine that someone knows nothing about gravity and wants to predict the arc of a projectile.  If they only have data from the first few seconds of the trajectory, when the motion is nearly linear, you could fit a tremendous variety of different curves to that data.  Even if you knew that the projectile will eventually return to earth, most of those curves would be totally wrong.  But you wouldn't be able to evaluate the curves until the projectile reaches its apex or nearly so.

That's where we are with covid-19.  It will eventually reach a peak and then decline, but the specific timing and height of the peak are unknown.  Fitting a curve to the first n data points does not necessarily tell you anything about the peak.  I am not a fan of those Imperial College models, but at least they are trying to model the mechanisms that generate the data, and that gives them a basis for projecting forward.  Fitting the curve that fits the first two weeks of data best does not tell you what is coming in the future.*

*I could change my mind with a convincing demonstration that arbitrary curve fitting for the first 2 weeks of past outbreaks was sufficient to model the course of an epidemic.  I doubt this has been done though.

[burger]

Sorry, BC and Goose, but you are not going to convince me that these models are useful without data showing that they accurately predict the peak (in both timing and intensity), and we are way too early in this process for that.  The Gaussian curves they are using imply a specific relationship between the early "exponential" stages of the outbreak and the later leveling off and decrease.  There is no reason to expect that the Gaussian fits better than any of a number of other potential curves.

Imagine that someone knows nothing about gravity and wants to predict the arc of a projectile.  If they only have data from the first few seconds of the trajectory, when the motion is nearly linear, you could fit a tremendous variety of different curves to that data.  Even if you knew that the projectile will eventually return to earth, most of those curves would be totally wrong.  But you wouldn't be able to evaluate the curves until the projectile reaches its apex or nearly so.

That's where we are with covid-19.  It will eventually reach a peak and then decline, but the specific timing and height of the peak are unknown.  Fitting a curve to the first n data points does not necessarily tell you anything about the peak.  I am not a fan of those Imperial College models, but at least they are trying to model the mechanisms that generate the data, and that gives them a basis for projecting forward.  Fitting the curve that fits the first two weeks of data best does not tell you what is coming in the future.*

*I could change my mind with a convincing demonstration that arbitrary curve fitting for the first 2 weeks of past outbreaks was sufficient to model the course of an epidemic.  I doubt this has been done though.