Reinforcement learning-based decision support system for COVID-19
Author | Padmanabhan, Regina |
Author | Meskin, Nader |
Author | Khattab, Tamer |
Author | Shraim, Mujahed |
Author | Al-Hitmi, Mohammed |
Available date | 2021-05-24T09:43:28Z |
Publication Date | 2021-07-01 |
Publication Name | Biomedical Signal Processing and Control |
Identifier | http://dx.doi.org/10.1016/j.bspc.2021.102676 |
Citation | Padmanabhan, Regina, Nader Meskin, Tamer Khattab, Mujahed Shraim, and Mohammed Al-Hitmi. "Reinforcement Learning-based Decision Support System for COVID-19." Biomedical Signal Processing and Control (2021): 102676. |
ISSN | 17468094 |
Abstract | 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. |
Language | en |
Publisher | Elsevier |
Subject | Active intervention COVID-19 Differential disease severity Optimal control Reinforcement learning |
Type | Article |
Volume Number | 68 |
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COVID-19 Research [811 items ]
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Public Health [411 items ]