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We discuss implications for the choice among model types, focusing on policy design. In some conditions, however, these differences in means are small compared to variability caused by stochastic events, parameter uncertainty, and model boundary. The response of the models to policies can also differ even when their base case behavior is similar.
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More interestingly, the DE and mean AB dynamics differ for several metrics relevant to public health, including diffusion speed, peak load on health services infrastructure, and total disease burden. Obviously, deterministic models yield a single trajectory for each parameter set, while stochastic models yield a distribution of outcomes.
ANYLOGIC SENSITIVITY ANALYSIS SOFTWARE
Sk圜iv Structural 3D is a cloud-based Structural Analysis software that allows users to model, analyze and design complex 3D structures. Compare price, features, and reviews of the software side-by-side to make the best choice for your business. We examine the impact of individual heterogeneity and different network topologies, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Simulation Modeling using this comparison chart. Using contagious disease as an example, we contrast the dynamics of a stochastic AB model with those of the analogous deterministic compartment DE model. Because resources are limited, the costs and benefits of such disaggregation should guide the choice of models for policy analysis. AB models relax aggregation assumptions, but entail computational and cognitive costs that may limit sensitivity analysis and model scope. When is it better to use agent-based (AB) models, and when should differential equation (DE) models be used? Whereas DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity across individuals and in the network of interactions among them.