Active learning to understand infectious disease models and improve policy making.

ABSTRACT: Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive. A more systematic exploration is needed to gain a thorough systems understanding. We present an active learning approach based on machine learning techniques as iterative surrogate modeling and model-guided experimentation to systematically analyze both common and edge manifestations of complex model runs. Symbolic regression is used for nonlinear response surface modeling with automatic feature selection. First, we illustrate our approach using an individual-based model for influenza vaccination. After optimizing the parameter space, we observe an inverse relationship between vaccination coverage and cumulative attack rate reinforced b

SUBMITTER: Willem L 

PROVIDER: S-EPMC3990517 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature