Just learned from a good news article in @nature nature.com/articles/d4158… about this site metrics.covid19-analysis.org. To be crystal clear it has nothing to do with @CCDD_HSPH. It is not reasonable in my opinion to mske such estimates with any confidence for large parts of the world
The uncertainty stated on this site is purely statistical uncertainty assuming data and model are accurate. This _vastly_ understates uncertainty. In many places, case confirmation is delayed dramatically (weeks) & variably, but this assumes 5 days from infection to confirm'n.
Changing testing practices mean changing proportions of cases ascertained and thus changing estimates of cases and R separate for reality. No correction or acknowledgment of uncertainty.
While it is valuable to try to squeeze information from data, the level of confidence displayed on this site vastly overstates the level of confidence we should have in R(t) estimates even in places that do very good and consistent testing.
See this preprint for an effort to compile all the challenges and some possible solutions. Every issue raised here contributes uncertainty to every R(t) estimate. Taking a global data source and cranking the software on it is not a way to get at truth. medrxiv.org/content/10.110…
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