In such instances, the model is called by some authors a substitution model [ 10, 11].
The p-values for the joint hypothesis tests provide support for the model in this instance as the model is not significantly, or substantively, different from the experimental data at conventional significance levels.
Granted the necessary species-specific biological information and translocation protocols to determine the model parameters in any instance in which the model is applied, the analysis indicates that a practical algorithm can be constructed to generate predictions of the optimal number, sex ratio, and division of resources for translocation with the goal of genetic rescue.
So for instance, if (alpha_{3}=0), then the model is said to be of the SEIR type, but for (alpha_{3}neq0), the model is referred to as SEIRS.
For instance, an important application of the model is related to the determination of the most effective preparedness information, for example provided by the surveillance network or other information networks, with the aim to decrease the systemic risk of outbreaks in populations.
For each instance of a sampled parameter vector, the model is run and likelihoods are calculated for the age-dependent prevalence curves generated.
Then, the labeled dataset is augmented with the most confidently predicted instances from the unlabeled pool, and the model is rebuilt.
Moreover, while some uncertainty remains for variables and time points far away from measurement instances, the flexibility of the model was significantly reduced by the data, which led us conclude that the model is not too complex and the data contain considerable information about model parameters.
Where BioModels identifiers are not available, for instance if the parent model has not yet been added to the database, the identifier for the publication (PubMed identifier or digital object identifier) in which the model is described is used.
