[Gibbon1]

I have another view really. Which is reading a paper analyzing pandemic overshoot. The authors covered a half dozen models. From very simple ones built on a simple differential equations to complex ones that factored in network effects. My take away is actual models[1] in this space tend to be robust.

The Imperial College Model is a complex model designed to answer subtle questions about the spread and containment of an epidemic. But there is nothing subtle about COVID19. The model predicts catastrophe unless you turn all the knobs to contain it to 11 and do it now.

The model is validated by real experience. Italy, New York both blew up. And elsewhere half hearted measures merely slowed the virus down, not stop it.

The true the deniers can't escape is. If what's been thrown at the pandemic is unnecessary. Then if so, why hasn't the pandemic just collapsed?

[1] As opposed to models that fit a prior defined curve to data. Those are shit.

[lbeltrame]

> The model is validated by real experience. Italy, New York both blew up.

Do you know where the first clusters in New York where?

Because in Northern Italy, and especially around Bergamo, hospitals and then nursing care homes turned into infection centers, and with the population there so skewed with the most vulnerable (along with imperfect knowledge on the pathology) it was easy for the virus to kill them.

In fact most of the initial clusters were in hospitals, and negligence turned Alzano Lombardo in a nice spreading place.

Would a model tuned on what we know now, taking into account different infection routes and places, work the same way? I don't know, but it is a question worth asking even if in the end the model proves to be absolutely correct.