Dear Billy,
If libbi gets stuck, it is almost certainly because the truncated Gaussian (rejection) sampler has trouble finding proposals that respect the limits. This is fixed in the latest version of libbi (1.4.0) which uses a more sophisticated truncated Gaussian sampler - can you try that and see if it improves things?
If you pass 'verbose=TRUE' to the call of adapt_proposal you can see if the sampler gets stuck (i.e., if output stops).
As an aside, having had a quick look at your model file, it appears slightly odd that the proposal distributions are restricted to be narrower than the prior. For example, if your prior on mu_v is uniform between 0 and 20, it seems strange to limit proposals to be greater than 3 - and similarly for d_infection (which could have a truncated_gaussian prior). If you use adapt_proposal to use the empirical covariance as proposal distribution, it'll respect any limits given in the priors when constructing the proposals.
Seb.