Hi @stevencarlislewalker - I made a start on adding in BA.2 (variant.Rmd). I just used the initial model estimates and made a function that calculates the % BA.2 at any time, and then graphed the share of cases that are BA.2. Essentially no new fitting, just a partitioning of the initial model fit to cases based on assumption that I made about % BA.2.
What I might do next is to understand beta as a weighted average of beta for BA.1 and beta for BA.2 and then forward project the weighted average of beta into the future based on the % BA.2 formula.
Hi @stevencarlislewalker - I made a start on adding in BA.2 (variant.Rmd). I just used the initial model estimates and made a function that calculates the % BA.2 at any time, and then graphed the share of cases that are BA.2. Essentially no new fitting, just a partitioning of the initial model fit to cases based on assumption that I made about % BA.2.
What I might do next is to understand beta as a weighted average of beta for BA.1 and beta for BA.2 and then forward project the weighted average of beta into the future based on the % BA.2 formula.