Geeky note: LOESS-type smoothers are often overused and sometimes even abused. But they're a good use for COVID-19 data where there are fairly consistent trends on a ~weekly basis but also erratic reporting from day to day.
LOESS methods are often misused because they do not handle *true* discontinuities in the data very well, i.e. some huge event happens and there's a huge, essentially instantaneous shift in the polls. You *don't* necessarily want to smooth that over.
But if you have some prior that the underlying data should be reasonably smooth and continuous, and the main challenge is that the estimates you get of the underlying function are noisy/stochastic, they can be useful.
