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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16883

Title: Active learning to understand infectious disease models and improve policy making
Authors: Willem, Lander
Stijven, Sean
Vladislavleva, Ekaterina
Broeckhove, Jan
Beutels, Philippe
HENS, Niel
Issue Date: 2014
Citation: PLoS computational biology, 10 (4), (ART N° e1003563)
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 by herd immunity. Second, we demonstrate the use of surrogate modeling techniques on input-response data from a deterministic dynamic model, which was designed to explore the cost-effectiveness of varicella-zoster virus vaccination. We use symbolic regression to handle high dimensionality and correlated inputs and to identify the most influential variables. Provided insight is used to focus research, reduce dimensionality and decrease decision uncertainty. We conclude that active learning is needed to fully understand complex systems behavior. Surrogate models can be readily explored at no computational expense, and can also be used as emulator to improve rapid policy making in various settings.
Notes: Willem, L (reprint author), Univ Antwerp, Vaccine & Infect Dis Inst, Ctr Hlth Econ Res & Modeling Infect Dis, B-2020 Antwerp, Belgium, lander.willem@uantwerpen.be
URI: http://hdl.handle.net/1942/16883
ISI #: 000336507500029
ISSN: 1553-734X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2015
Appears in Collections: Research publications

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