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 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.

Grantor

Department of Mathematics and Applied Mathematics

Year dc:date.issued

2023

Author dc:creator

• Folarin, Arinze Lawrence

Advisor dc:contributor.advisor

• Shock, Jonathan

• Mathematics and Applied Mathematics

Handle dc:identifier.uri

http://hdl.handle.net/11427/39538

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/39538