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dc.rights.licenseopenen_US
dc.contributor.authorCOLAS, C.
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHEJBLUM, Boris
ORCID: 0000-0003-0646-452X
IDREF: 189970316
hal.structure.identifierGroupe de Recherche en Economie Théorique et Appliquée [GREThA]
dc.contributor.authorROUILLON, Sebastien
IDREF: 149491913
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorTHIEBAUT, Rodolphe
dc.contributor.authorOUDEYER, P.-Y.
dc.contributor.authorMOULIN-FRIER, C.
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPRAGUE, Melanie
dc.date.accessioned2021-11-18T16:30:43Z
dc.date.available2021-11-18T16:30:43Z
dc.date.issued2021-07
dc.identifier.issn1076-9757en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/123869
dc.description.abstractEnModeling the dynamics of epidemics helps to propose control strategies based on pharmaceutical and non-pharmaceutical interventions (contact limitation, lockdown, vaccination, etc). Hand-designing such strategies is not trivial because of the number of possible interventions and the difficulty to predict long-term effects. This task can be cast as an optimization problem where state-of-the-art machine learning methods such as deep reinforcement learning might bring significant value. However, the specificity of each domain—epidemic modeling or solving optimization problems—requires strong collaborations between researchers from different fields of expertise. This is why we introduce EpidemiOptim, a Python toolbox that facilitates collaborations between researchers in epidemiology and optimization. EpidemiOptim turns epidemiological models and cost functions into optimization problems via a standard interface commonly used by optimization practitioners (OpenAI Gym). Reinforcement learning algorithms based on Q-Learning with deep neural networks (dqn) and evolutionary algorithms (nsga-ii) are already implemented. We illustrate the use of EpidemiOptim to find optimal policies for dynamical on-off lockdown control under the optimization of the death toll and economic recess using a Susceptible-Exposed-Infectious-Removed (seir) model for COVID-19. Using EpidemiOptim and its interactive visualization platform in Jupyter notebooks, epidemiologists, optimization practitioners and others (e.g. economists) can easily compare epidemiological models, costs functions and optimization algorithms to address important choices to be made by health decision-makers. Trained models can be explored by experts and non-experts via a web interface. ©2021 AI Access Foundation. All rights reserved.
dc.language.isoENen_US
dc.subject.enControl Strategies
dc.subject.enCost Functions
dc.subject.enDecision Making
dc.subject.enDeep Learning
dc.subject.enDeep Neural Networks
dc.subject.enEpidemiological Models
dc.subject.enEpidemiology
dc.subject.enEvolutionary Algorithms
dc.subject.enInteractive Visualizations
dc.subject.enLearning Algorithms
dc.subject.enLearning Systems
dc.subject.enNon-Pharmaceutical Interventions
dc.subject.enOptimization
dc.subject.enOptimization Algorithms
dc.subject.enOptimization Problems
dc.subject.enReinforcement Learning
dc.subject.enStandard Interface
dc.subject.enState-Of-The-Art Machine Learning Methods
dc.subject.enVisualization
dc.title.enEpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models
dc.typeArticle de revueen_US
dc.identifier.doi10.1613/JAIR.1.12588en_US
dc.subject.halÉconomie et finance quantitative [q-fin]en_US
bordeaux.journalJournal of Artificial Intelligence Researchen_US
bordeaux.page479-515en_US
bordeaux.volume71en_US
bordeaux.hal.laboratoriesGroupe de Recherche en Economie Théorique et Appliquée (GREThA) - UMR 5113en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionCNRSen_US
bordeaux.institutionINSERM
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03435494
hal.version1
hal.date.transferred2021-11-18T16:30:47Z
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Artificial%20Intelligence%20Research&rft.date=2021-07&rft.volume=71&rft.spage=479-515&rft.epage=479-515&rft.eissn=1076-9757&rft.issn=1076-9757&rft.au=COLAS,%20C.&HEJBLUM,%20Boris&ROUILLON,%20Sebastien&THIEBAUT,%20Rodolphe&OUDEYER,%20P.-Y.&rft.genre=article


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