Reinforcement learning-based decision support system for COVID-19. (July 2021)

• Record Type: Journal Article
• Authors: Padmanabhan, Regina; Meskin, Nader; Khattab, Tamer; Shraim, Mujahed; Al-Hitmi, Mohammed
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: COVID-19 -- Reinforcement learning -- Optimal control -- Active intervention -- Differential disease severity; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102676
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Novel disease spread model that accounts for the influence of NPIs on the overall disease. transmission rate and specific infection rates during the asymptomatic and symptomatic periods. RL-based closed-loop controller for mitigating COVID-19. Design of reward function to account for cost and hospital saturation constraints. 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.