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

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 learni...

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Autores principales:	Lander Willem, Sean Stijven, Ekaterina Vladislavleva, Jan Broeckhove, Philippe Beutels, Niel Hens
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/73f74cd4c41e4bc397be35bb27c6756d
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spelling	oai:doaj.org-article:73f74cd4c41e4bc397be35bb27c6756d2021-11-18T05:52:58ZActive learning to understand infectious disease models and improve policy making.1553-734X1553-735810.1371/journal.pcbi.1003563https://doaj.org/article/73f74cd4c41e4bc397be35bb27c6756d2014-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24743387/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Modeling 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.Lander WillemSean StijvenEkaterina VladislavlevaJan BroeckhovePhilippe BeutelsNiel HensPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 4, p e1003563 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Lander Willem Sean Stijven Ekaterina Vladislavleva Jan Broeckhove Philippe Beutels Niel Hens Active learning to understand infectious disease models and improve policy making.
description	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.
format	article
author	Lander Willem Sean Stijven Ekaterina Vladislavleva Jan Broeckhove Philippe Beutels Niel Hens
author_facet	Lander Willem Sean Stijven Ekaterina Vladislavleva Jan Broeckhove Philippe Beutels Niel Hens
author_sort	Lander Willem
title	Active learning to understand infectious disease models and improve policy making.
title_short	Active learning to understand infectious disease models and improve policy making.
title_full	Active learning to understand infectious disease models and improve policy making.
title_fullStr	Active learning to understand infectious disease models and improve policy making.
title_full_unstemmed	Active learning to understand infectious disease models and improve policy making.
title_sort	active learning to understand infectious disease models and improve policy making.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/73f74cd4c41e4bc397be35bb27c6756d
work_keys_str_mv	AT landerwillem activelearningtounderstandinfectiousdiseasemodelsandimprovepolicymaking AT seanstijven activelearningtounderstandinfectiousdiseasemodelsandimprovepolicymaking AT ekaterinavladislavleva activelearningtounderstandinfectiousdiseasemodelsandimprovepolicymaking AT janbroeckhove activelearningtounderstandinfectiousdiseasemodelsandimprovepolicymaking AT philippebeutels activelearningtounderstandinfectiousdiseasemodelsandimprovepolicymaking AT nielhens activelearningtounderstandinfectiousdiseasemodelsandimprovepolicymaking
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Active learning to understand infectious disease models and improve policy making.

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