I rather prefer the approach here: https://epiforecasts.io/covid/posts/national/united-kingdom/ https://epiforecasts.io/covid/posts/national/united-kingdom/ whi

I rather prefer the approach here: https://epiforecasts.io/covid/posts/national/united-kingdom/ https://epiforecasts.io/covid/posts/national/united-kingdom/ which does not attempt to calculate r for the most recent days and shows days for which uncertainty is great in progressively lighter colours. Part of the excellent work done by the London School of Hygiene and Tropical Medicine's Centre for Mathematical Modeling of Infectious Diseases much of which can be found here: https://cmmid.github.io/topics/covid19/ https://cmmid.github.io/topics/covid19/. That model uses the EpiEstim R package to estimate R based based on the number of case onsets and a serial interval distribution. EpiEstim implements the methods of Cori et al: https://academic.oup.com/aje/article/178/9/1505/89262 https://academic.oup.com/aje/article/178/9/1505/89262 which also uses Bayesian methods. It looks like the linked model is based on Bettencourt & Ribeiro: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0002185 https://journals.plos.org/plosone/article?id=10.1371/journal...