These findings are consistent with the predictions of our model and difficult to reconcile with alternative models that impose symmetry or homogeneity of either inputs or outputs.
The interdependence and feedback among interacting variables, processes, actors and institutions are hard to model and difficult to forecast.
The attack rate of the influenza virus is crucial for these models and a difficult parameter to obtain.
Whereas, the atlas-based models assume a prior model of organization, which is (i) nonexistent for the growth of 3D cell culture models, and (ii) difficult to generate as a result of cellular and organizational heterogeneity.
Scale differences and temporal sampling make evaluation of coarse to moderate spatial resolution remote sensing models and DVMs difficult using in situ measurements of NPP, as they represent distinctly smaller areas.
Even though this may ease the implementation of practices, this makes the model complex and difficult to understand.
However, both methods are model-dependent and difficult to apply in a noninvasive setting, while their potential under non-physiological conditions has been scarcely explored.
Experimental studies in appropriate animal models are costly and difficult, and computational models are ideal for generating and testing hypotheses.
Simulation models are complex and difficult to build and run.
For example, murine tumor models are expensive and difficult to adopt for routine drug screening.
