It's certainly true that forcasting using exponential fits is bound to have huge error bars, but it also seems foolish to "show" that it can't be accurately done early on a curve without noticing that it is fairly easy to fit (and relatively stable) once it becomes non-exponential (e.g. once rate of change is constant).
Of course in many cases there are external bounds that are similarly useful, for example when you know the maxima (e.g. full population size). Then the question to the early data is simply whether a fit to an exponential is "better" than successively higher order polynomials.
Currently, this forcasting information seems not particularly helpful at all for fitting epidemiological data for Covid19 since nowhere do we have data for a case where strict distancing hasn't been an effect of high death and hospitalization rates well before 50% of the population would provide any effective immunity.
Of course in many cases there are external bounds that are similarly useful, for example when you know the maxima (e.g. full population size). Then the question to the early data is simply whether a fit to an exponential is "better" than successively higher order polynomials.
Currently, this forcasting information seems not particularly helpful at all for fitting epidemiological data for Covid19 since nowhere do we have data for a case where strict distancing hasn't been an effect of high death and hospitalization rates well before 50% of the population would provide any effective immunity.