Optimizing COVID-19 control measures using multi-objective deep reinforcement learning

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dc.contributor.advisorShock, Jonathan
dc.contributor.authorFolarin, Arinze Lawrence
dc.date.accessioned2024-04-30T13:07:07Z
dc.date.available2024-04-30T13:07:07Z
dc.date.issued2023
dc.date.updated2024-04-19T12:57:04Z
dc.description.abstractA crucial area of global research is the hunt for efficient non-pharmaceutical methods to stop the spread of diseases. Recent research has shown that reinforcement learning can be a helpful tool in the medical industry to ad- dress challenging and delicate issues. The goal of this study is to improve COVID-19 control measures through the use of multi-objective deep re- inforcement learning techniques. The results of two case studies, one using a Pareto conditioned network on COVID-19 data from Belgium and the other using a Deep Q-Network, Goal-DQN, and Non-dominated Sorting Genetic Algorithm (NSGA-II) on COVID-19 data from France, are evaluated using both binomial (Stochastic) and Ordinary Differen- tial Equation mathematical models. The study highlights the potential of multi-objective deep reinforcement learning as a method of optimizing public health interventions by shedding light on the optimum COVID-19 control methods for various scenarios and models. Findings show that the suggested strategies are efficient in figuring out the best preventive actions by striking a balance between two crucial choice difficulties encountered when trying to stop the spread of Covid-19 in particular areas. This study makes a substantial contribution to the ongoing fight against pandemics like the Covid-19 event.
dc.identifier.apacitationFolarin, A. L. (2023). <i>Optimizing COVID-19 control measures using multi-objective deep reinforcement learning</i>. (). ,Faculty of Science ,Department of Mathematics and Applied Mathematics. Retrieved from http://hdl.handle.net/11427/39538en_ZA
dc.identifier.chicagocitationFolarin, Arinze Lawrence. <i>"Optimizing COVID-19 control measures using multi-objective deep reinforcement learning."</i> ., ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 2023. http://hdl.handle.net/11427/39538en_ZA
dc.identifier.citationFolarin, A.L. 2023. Optimizing COVID-19 control measures using multi-objective deep reinforcement learning. . ,Faculty of Science ,Department of Mathematics and Applied Mathematics. http://hdl.handle.net/11427/39538en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Folarin, Arinze Lawrence AB - A crucial area of global research is the hunt for efficient non-pharmaceutical methods to stop the spread of diseases. Recent research has shown that reinforcement learning can be a helpful tool in the medical industry to ad- dress challenging and delicate issues. The goal of this study is to improve COVID-19 control measures through the use of multi-objective deep re- inforcement learning techniques. The results of two case studies, one using a Pareto conditioned network on COVID-19 data from Belgium and the other using a Deep Q-Network, Goal-DQN, and Non-dominated Sorting Genetic Algorithm (NSGA-II) on COVID-19 data from France, are evaluated using both binomial (Stochastic) and Ordinary Differen- tial Equation mathematical models. The study highlights the potential of multi-objective deep reinforcement learning as a method of optimizing public health interventions by shedding light on the optimum COVID-19 control methods for various scenarios and models. Findings show that the suggested strategies are efficient in figuring out the best preventive actions by striking a balance between two crucial choice difficulties encountered when trying to stop the spread of Covid-19 in particular areas. This study makes a substantial contribution to the ongoing fight against pandemics like the Covid-19 event. DA - 2023 DB - OpenUCT DP - University of Cape Town KW - Mathematics and Applied Mathematics LK - https://open.uct.ac.za PY - 2023 T1 - Optimizing COVID-19 control measures using multi-objective deep reinforcement learning TI - Optimizing COVID-19 control measures using multi-objective deep reinforcement learning UR - http://hdl.handle.net/11427/39538 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/39538
dc.identifier.vancouvercitationFolarin AL. Optimizing COVID-19 control measures using multi-objective deep reinforcement learning. []. ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 2023 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/39538en_ZA
dc.language.rfc3066Eng
dc.publisher.departmentDepartment of Mathematics and Applied Mathematics
dc.publisher.facultyFaculty of Science
dc.subjectMathematics and Applied Mathematics
dc.titleOptimizing COVID-19 control measures using multi-objective deep reinforcement learning
dc.typeThesis / Dissertation
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
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