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A model is identifiable if the value of each model parameter can be uniquely determined under ideal conditions.

In this setting, the quasispecies model is identifiable.

As there are more data points (93) than parameters (27) parameters, the mechanistic model is identifiable in theory.

This appendix shows that the full model is not statistically identifiable, whereas the reduced model is identifiable.

Despite our reservations, and the criticisms in literature of attempting this, the full MNAR selection model is identifiable and resulted in the following parameter estimates.

P roposition 1 If for each edge in a PSG at least one of the following conditions is true, then the model is identifiable.

Thus, 9 parameters out of the total 11 parameters in the extracted model were identifiable.

Mean FSDs of the estimates were all <50%, consistent with the model being identifiable from the data.

Furthermore, if the model in Eq. (21) is fully observed, e.g., y1 =  x1,  y2 =  x2, then all parameters in the model are identifiable.

However, this approach may open discussions on the possibility of an overparameterization of models and the identifiability between the parameters of cured incidence and uncured survivals [ 10], although the parameters of the standard cure model are identifiable in some sense [ 19].

An aim of identifiability analysis is to determine if the parameters of a model are identifiable or not, i.e. whether its parameters can be uniquely estimated.