{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/39538"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/39538","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Optimizing COVID-19 control measures using multi-objective deep reinforcement learning","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Folarin, Arinze Lawrence"],"institution":"Department of Mathematics and Applied Mathematics","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Shock, Jonathan"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-22T22:23:13Z","subjects":["Mathematics and Applied Mathematics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/39538","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Shock, Jonathan"]},{"key":"dc:creator","label":"Author","values":["Folarin, Arinze Lawrence"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-04-30T13:07:07Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-04-30T13:07:07Z"]},{"key":"dc:date.issued","label":"Date","values":["2023"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Mathematics and Applied Mathematics"]},{"key":"dc:type","label":"Dc Type","values":["Thesis / Dissertation"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters","MSc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Mathematics and Applied Mathematics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/39538"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Optimizing COVID-19 control measures using multi-objective deep reinforcement learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Shock, Jonathan"],"dc:creator":["Folarin, Arinze Lawrence"],"dc:date.accessioned":["2024-04-30T13:07:07Z"],"dc:date.available":["2024-04-30T13:07:07Z"],"dc:date.issued":["2023"],"dc:description.abstract":["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."],"dc:identifier.uri":["http://hdl.handle.net/11427/39538"],"dc:publisher.department":["Department of Mathematics and Applied Mathematics"],"dc:subject":["Mathematics and Applied Mathematics"],"dc:title":["Optimizing COVID-19 control measures using multi-objective deep reinforcement learning"],"dc:type":["Thesis / Dissertation"],"dc:type.qualificationlevel":["Masters","MSc"]},"updated_at":"2026-07-22T22:23:13Z"}