I think you're overthinking things. The OP makes the basic point that epidemic logistic/S-curves like Covid look like exponential growth for a good portion of

I think you're overthinking things. The OP makes the basic point that epidemic logistic/S-curves like Covid look like exponential growth for a good portion of their trajectory and so will behave like exponential for a reasonable period in the future. And the data is inherently noisy. Trying to get more than that is counter-productive. Basically, the bulk of the data is pointing to massive death later, the factor stopping that is factors external to the data we have now. Basically, we know the epidemic is ramping now, we know there's some limit to how far it can go but we don't really know that limit 'till we're close. And trying to get more clever than this results in really bad claims (like guessing we're peaking and abandoning caution). I'd also add testing results have been unreliable based on real world limits to testing (limits to number of tests, general screw-ups). Death data is more reliable than testing data but delayed by infections taking time to kill people. The "quick and dirty" modeling of Tomas Pueyo has been more successful than clever actions. See: https://medium.com/@tomaspueyo/coronavirus-act-today-or-people-will-die-f4d3d9cd99ca https://medium.com/@tomaspueyo/coronavirus-act-today-or-peop... In contrast, https://covid-19.direct https://covid-19.direct has good data but too clever algorithm for spotting peaks. If you browse their data, you get used to garbage "forecasts" of where the peak is. https://medium.com/@tomaspueyo/coronavirus-act-today-or-people-will-die-f4d3d9cd99ca https://medium.com/@tomaspueyo/coronavirus-act-today-or-peop...