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Optimizing COVID-19 control measures using multi-objective deep reinforcement learning
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 improv...
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| Format: | Thesis |
| Language: | Eng |
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Department of Mathematics and Applied Mathematics
2024
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| _version_ | 1867613273101172736 |
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| access_status_str | Open Access |
| author | Folarin, Arinze Lawrence |
| author2 | Shock, Jonathan |
| author_browse | Folarin, Arinze Lawrence Shock, Jonathan |
| author_facet | Shock, Jonathan Folarin, Arinze Lawrence |
| author_sort | Folarin, Arinze Lawrence |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/39538 |
| institution | University of Cape Town (South Africa) |
| language | Eng |
| last_indexed | 2026-06-10T12:33:31.121Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Mathematics and Applied Mathematics |
| publisherStr | Department of Mathematics and Applied Mathematics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/39538 Optimizing COVID-19 control measures using multi-objective deep reinforcement learning Folarin, Arinze Lawrence Shock, Jonathan Mathematics and Applied Mathematics 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. 2024-04-30T13:07:07Z 2024-04-30T13:07:07Z 2023 2024-04-19T12:57:04Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/39538 Eng application/pdf Department of Mathematics and Applied Mathematics Faculty of Science |
| spellingShingle | Mathematics and Applied Mathematics Folarin, Arinze Lawrence Optimizing COVID-19 control measures using multi-objective deep reinforcement learning |
| thesis_degree_str | Master's |
| title | Optimizing COVID-19 control measures using multi-objective deep reinforcement learning |
| title_full | Optimizing COVID-19 control measures using multi-objective deep reinforcement learning |
| title_fullStr | Optimizing COVID-19 control measures using multi-objective deep reinforcement learning |
| title_full_unstemmed | Optimizing COVID-19 control measures using multi-objective deep reinforcement learning |
| title_short | Optimizing COVID-19 control measures using multi-objective deep reinforcement learning |
| title_sort | optimizing covid 19 control measures using multi objective deep reinforcement learning |
| topic | Mathematics and Applied Mathematics |
| url | http://hdl.handle.net/11427/39538 |
| work_keys_str_mv | AT folarinarinzelawrence optimizingcovid19controlmeasuresusingmultiobjectivedeepreinforcementlearning |