[BostonCard]

But there is little to no support for the bell-shaped curves they're using.

Again, Gaussian error function ≠ Gaussian function.  It is not a bell-shaped curve; it's a sinusoidal curve.  And the SEIR model would predict that a cumulative count of deaths would be a sinusoidal curve (though not, as far as I can tell, a Gaussian error function).

BC

The model uses the Gaussian error function for the cumulative number of deaths.   The curve for the number of deaths by day is just the derivative of the error function, which is a gaussian curve.  That's what they're showing in the results here http://covid19.healthdata.org/projections

I'm not sure why they're modeling the cumulative deaths rather than the individual counts by day, as that is surely a far easier equation to model with no integrals involved.

The count per day is noisier.  Just look at the series from Italy that started this thread, or the Santa Clara data.

BC

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Apologies if this is a dumb question but I'm going to ask it anyway. I'm not keeping up with the conversation here as I haven't touched any of this since my undergrad days. But when you say curve-fitting do you mean just trying to find some sort of function that can be applied to the data to help explain the growth?

Yes.  The problem is that the data are the deaths so far, which are still growing quickly in most states.  But there is little to no support for the bell-shaped curves they're using.  It's possible that things will reach a peak and then slow down nicely, but there are still a lot of ways this could play out.  Models like this one give false confidence that things are trending in the right direction when, so far at least, there is little to be optimistic about (outside of Washington maybe).  That may change in the next couple of weeks in a lot of states as lockdowns start to reduce the death rates.  But we have to wait and see.

Wouldn't that also be a flawed strategy considering that we have shifting our responses (from it's just like the flu to shelter in place) during this whole thing, so wouldn't different stages have their own unique curves at different points ... and then we are combining a bunch of different places with different responses, and traveling in between. Definitely sounds like a bad way to project into the future.

That's why they use a mixed effects model.  It allows you to have parameters that vary over time and space.

BC

[BostonCard]

But there is little to no support for the bell-shaped curves they're using.

Again, Gaussian error function ≠ Gaussian function.  It is not a bell-shaped curve; it's a sinusoidal curve.  And the SEIR model would predict that a cumulative count of deaths would be a sinusoidal curve (though not, as far as I can tell, a Gaussian error function).

BC

The model uses the Gaussian error function for the cumulative number of deaths.   The curve for the number of deaths by day is just the derivative of the error function, which is a gaussian curve.  That's what they're showing in the results here http://covid19.healthdata.org/projections

I'm not sure why they're modeling the cumulative deaths rather than the individual counts by day, as that is surely a far easier equation to model with no integrals involved.

The count per day is noisier.  Just look at the series from Italy that started this thread, or the Santa Clara data.

BC

---

Apologies if this is a dumb question but I'm going to ask it anyway. I'm not keeping up with the conversation here as I haven't touched any of this since my undergrad days. But when you say curve-fitting do you mean just trying to find some sort of function that can be applied to the data to help explain the growth?

Yes.  The problem is that the data are the deaths so far, which are still growing quickly in most states.  But there is little to no support for the bell-shaped curves they're using.  It's possible that things will reach a peak and then slow down nicely, but there are still a lot of ways this could play out.  Models like this one give false confidence that things are trending in the right direction when, so far at least, there is little to be optimistic about (outside of Washington maybe).  That may change in the next couple of weeks in a lot of states as lockdowns start to reduce the death rates.  But we have to wait and see.

Wouldn't that also be a flawed strategy considering that we have shifting our responses (from it's just like the flu to shelter in place) during this whole thing, so wouldn't different stages have their own unique curves at different points ... and then we are combining a bunch of different places with different responses, and traveling in between. Definitely sounds like a bad way to project into the future.

That's why they use a mixed effects model.  It allows you to have parameters that vary over time and space.

BC