Explanation of methods

To aid with interpretation of package outputs, we summarize the currently used inputs, data, methods and assumptions in \(R(t)\) estimation across the following categories:

 ⎘: How the relationship between \(R(t)\) and infections is defined
 ⎘: How \(R(t)\) is constrained using distributions for key variables
 ⎘: How \(R(t)\) is constrained over time
 ⎘: Additional data and distributions that are used to constrain \(R(t)\)
 ⎘: Inference frameworks that are used to estimate \(R(t)\)

Open research questions

Several open research questions remain, and are not discussed here further. These include:

• the ability to deal with very low case counts in sub-regions of geographic areas of interest

• how to deal with data drop-out