Any serious attempt at modelling this over python would use the pydata stack (numpy, pandas, etc), which run on top of C++ anyways.
The best solution here would probably be to package up the core routines into a library and use this from either R or Python.
A lot of the criticism I saw was because the core routines did not need to be packaged up. There were a lot of common data structures reimplemented, etc.
I don't think the model had many novel routines. It could be built just using industry standard and tested tools in python, R, Julia (if you really want speed) etc. But it reinvented the whole ecosystem in one big ball of C.
tbh, this should have been built on STAN or similar. There's so many variables and assumptions that the output is completely dominated by the parameters chosen. Seeing the distribution of outcomes instead of a point estimate would be actually useful.
I'd love if this was written in R, Python or Stan so I could contribute, but that's probably not the researchers focus ;)
While Stan is amazing, I shudder to think as to how long this model would have taken to run using MCMC (1 week plus maybe?).