https://socictopen.socict.org/files/original/2d22e433d3829a3c185b49601e7b0a22.pdf 3e2db354825eb6b3d931c32b2f2b088d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Reinforcement learning-based decision support system for COVID-19. Creator An entity primarily responsible for making the resource Regina Padmanabhan, Nader Meskin, Tamer Khattab, Mujahed Shraim, Mohammed Al-Hitmi Description An account of the resource Globally, informed decision on the most effective set of restrictions for the containment of COVID-19 has been the subject of intense debates. There is a significant need for a structured dynamic framework to model and evaluate different intervention scenarios and how they perform under different national characteristics and constraints. This work proposes a novel optimal decision support framework capable of incorporating different interventions to minimize the impact of widely spread respiratory infectious pandemics, including the recent COVID-19, by taking into account the pandemic's characteristics, the healthcare system parameters, and the socio-economic aspects of the community. The theoretical framework underpinning this work involves the use of a reinforcement learning-based agent to derive constrained optimal policies for tuning a closed-loop control model of the disease transmission dynamics. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource covid-19, Optimal control, reinforcement learning, Active intervention, Differential disease severity Identifier An unambiguous reference to the resource within a given context 10.1016/j.bspc.2021.102676 Source A related resource from which the described resource is derived Biomedical signal processing and control