1. Reduced modelling and optimal control of epidemiological individual-based models with contact heterogeneity
- Author
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C. Courtès, E. Franck, K. Lutz, L. Navoret, Y. Privat, Institut de Recherche Mathématique Avancée (IRMA), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), TOkamaks and NUmerical Simulations (TONUS), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), École Centrale de Lyon (ECL), Université de Lyon, Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), and ANR-20-CE40-0009,TRECOS,Nouvelles directions en contrôle et stabilisation: Contraintes et termes non-locaux(2020)
- Subjects
Control and Optimization ,Applied Mathematics ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,Neural network ,Optimal control ,Individual-based models ,Reduced models ,49M99 93B45 (Primary) 93-10, 92D30 (Secondary) ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,Control and Systems Engineering ,Optimization and Control (math.OC) ,FOS: Mathematics ,Super-spreaders ,[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] ,Mathematics - Optimization and Control ,Software - Abstract
Modelling epidemics via classical population-based models suffers from shortcomings that so-called individual-based models are able to overcome, as they are able to take heterogeneity features into account, such as super-spreaders, and describe the dynamics involved in small clusters. In return, such models often involve large graphs which are expensive to simulate and difficult to optimize, both in theory and in practice. By combining the reinforcement learning philosophy with reduced models, we propose a numerical approach to determine optimal health policies for a stochastic epidemiological graph-model taking into account super-spreaders. More precisely, we introduce a deterministic reduced population-based model involving a neural network, and use it to derive optimal health policies through an optimal control approach. It is meant to faithfully mimic the local dynamics of the original, more complex, graph-model. Roughly speaking, this is achieved by sequentially training the network until an optimal control strategy for the corresponding reduced model manages to equally well contain the epidemic when simulated on the graph-model. After describing the practical implementation of this approach, we will discuss the range of applicability of the reduced model and to what extent the estimated control strategies could provide useful qualitative information to health authorities., Comment: 37 pages, 21 figures, to be published in the journal "Optimal Control Applications and Methods" (Special Issue: Optimal control in therapeutics and epidemiology)
- Published
- 2022
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