title: 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 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.
authors.affiliation: Centre for Health Economics Research & Modeling of Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium; Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium; Interuniversitary Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium.
authors.forename: Lander
authors.initials: L
authors.lastname: Willem
authors.affiliation: Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium; Department of Information Technology, Gent University-iMinds, Gent, Belgium.
authors.forename: Sean
authors.initials: S
authors.lastname: Stijven
authors.affiliation: Evolved Analytics Europe BVBA, Beerse, Belgium.
authors.forename: Ekaterina
authors.initials: E
authors.lastname: Vladislavleva
authors.affiliation: Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium.
authors.forename: Jan
authors.initials: J
authors.lastname: Broeckhove
authors.affiliation: Centre for Health Economics Research & Modeling of Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium; School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia.
authors.forename: Philippe
authors.initials: P
authors.lastname: Beutels
authors.affiliation: Centre for Health Economics Research & Modeling of Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium; Interuniversitary Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium.
authors.forename: Niel
authors.initials: N
authors.lastname: Hens
mesh_terms: D002644:Chickenpox / Q000453:epidemiology / Q000517:prevention & control; D019433:Chickenpox Vaccine / Q000008:administration & dosage; D003141:Communicable Diseases; D006801:Humans; D007252:Influenza Vaccines / Q000008:administration & dosage; D007251:Influenza, Human / Q000453:epidemiology / Q000517:prevention & control; D007858:Learning; D008962:Models, Theoretical; D011050:Policy Making; D013269:Stochastic Processes
chemical_list: D019433:Chickenpox Vaccine; D007252:Influenza Vaccines
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