It can be surprisingly hard to include birth, death, immigration, emigration and aging into a disease model in satisfactory ways. Also, one must make a decision on modeling in continuous versus discrete time. Consider the strengths and weaknesses of the analysis presented, and list changes to the model that might be improvements.

In an imperfect world, it is nice to check the extent to which the conclusions are insensitive to alternative modeling decisions. These are testable hypotheses, which can be addressed within a plug-and-play inference framework. Identify what would have to be done to investigate the changes you have proposed. Optionally, you could have a go at coding something up to see if it makes a difference.


Re-running the code after revising the model would tell us whether a modified model was a substantial improvement, measured by maximized log likelihood. Also, we would find whether the conclusions about local persistence are robust to the change.

This version produced in R 4.3.2 on February 19, 2024.

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