It is Tyler Cowen week on the blog.
But I ask you, where are the numerous cases of leading epidemiologists screaming bloody murder to the press, or on their blogs, or in any other manner, that the most commonly used model for this all-important policy analysis is deeply wrong and in some regards close to a fraud?So we did have blog material on this, here, here, and here. But it is a fair critique that we should have been more aggressive in blogging about this issue. Andrew Gelman was ahead of the curve here, way back on March 29th:
I have a few thoughts on this model. First, yeah, it’s curve-fitting, no more and no less. Second, if they’re gonna fit a model like this, I’d recommend they just fit it in Stan: the methodological appendix has all sorts of fragile nonlinear-least-squares stuff that we don’t really need any more. Third, I guess there’s nothing wrong with doing this sort of analysis, as long as it’s clear what the assumptions are. What the method is really doing is using the second derivative of the time trend on the log scale to estimate where we are on the curve. Once that second derivative goes negative, so the exponential growth is slowing, the model takes this as evidence that the rate of growth on the log scale will rapidly continue to go toward zero and then go negative. Fourth, yeah, what Dorman says: you can’t take the model for the asymptotic limit seriously. For example, in that methodological appendix, they say that they use the probit (“ERF”) rather than the logit curve because the probit fits the data better. That’s fine, but there’s no reason to think that the functional form at the beginning of the spread of a disease will match the functional form (or, for that matter, the parameters of the curve) at later stages. It really is the tail wagging the dog.